"We Own Our IP" Is Not Enough. The AI-Assisted Chain of Title Every Founder Has to Build Now.

The Supreme Court ended the debate on March 2, 2026.

When it declined to hear Thaler v. Perlmutter, the Court left the D.C. Circuit's ruling intact and settled the central question that had hung over AI-assisted creative work for three years: AI cannot be an author. AI cannot be an inventor. The ownership of anything a company created with AI assistance depends entirely on what the humans on the team did, documented, and contractually secured before investors ever asked for the IP schedule.

That last part is the one that catches founders off guard.

The IP chain-of-title problem with AI-assisted work is not a single document gap. It runs across four separate legal frameworks, each with its own proof requirements, each with its own failure point. A company can get three of them right and still face a diligence problem because the fourth is missing. The frameworks are copyright, patent, work-made-for-hire and assignment, and trade secret. The failure points in each are mechanical, not vague. And the proof each requires is specific enough that "we believe we own it" is never the answer that closes a financing round.

This is the long-form reference behind Issue 03 of The Thursday Redline, my LinkedIn newsletter. The Redline was written for founders who need the founder-direct version: what breaks, what to build, and what the board needs to see. This article goes deeper on the statute and case mechanics, the practical application of the November 2025 USPTO guidance, the Copyright Office's selection-arrangement-modification test, how courts are now reading the trade secret "reasonable measures" standard in the context of AI vendors, and what investor diligence actually looks like in a 2026 term sheet for an AI-using startup.

The Brief

What settled: SCOTUS denied cert in Thaler v. Perlmutter on March 2, 2026, leaving the D.C. Circuit's ruling as binding: AI systems cannot hold copyright. Human authorship is required. The rule is not changing.
What updated: USPTO's November 2025 guidance confirmed the AI-as-tool standard for patent inventorship. No Pannu factors required for single-human-plus-AI scenarios; traditional conception standard applies.
What cases said in 2026: Trinidad v. OpenAI (January 2026) held that feeding confidential data into consumer-tier AI without contractual restrictions strips trade secret protection. United States v. Heppner (February 2026) held the same logic applies to attorney-client privilege.
What vendor terms now mean: GitHub Copilot moved to usage-based billing June 1, 2026, with three different data-retention regimes depending on which underlying model the team uses. The vendor environment is not uniform.
What this affects: Patent filings, copyright registration, investor IP reps, contractor agreement audits, and board-level AI-tool risk reporting.

What Changed, and When

The copyright side: March 2, 2026 finalizes a three-year build

The human-authorship requirement under 17 U.S.C. § 101 (the Copyright Act's definition of a copyrightable "work of authorship," which has required human creation since at least 1973) has always existed. What changed between 2023 and 2026 is the litigation record that now sits underneath it.

Generative AI made the question operational: what happens when a human prompts a machine and the machine generates the output? Who authored the output?

The Copyright Office answered that in two stages. In March 2023, it issued registration guidance that required disclosure of AI-generated content in new applications and stated that purely AI-generated content would not receive registration. In January 2025, it published its Part 2 Copyrightability Report, which established the analytical framework courts and examiners are now using: a work containing AI-generated material is copyrightable only to the extent the human author contributed original expression through selection, arrangement, coordination, editing, modification, or integration of that material.

Thaler v. Perlmutter was the D.C. Circuit case that tested the outer boundary. Stephen Thaler attempted to register a work he claimed was created autonomously by an AI system with no human authorship whatsoever. The district court denied registration; the D.C. Circuit affirmed in March 2025, holding that human authorship is required under 17 U.S.C. § 101 and that this is not a question the court could resolve by reading the statute more expansively. SCOTUS denied certiorari on March 2, 2026. The rule is now settled.

What that means practically: the ruling does not say AI-assisted works are unprotectable. It says works with no human authorship are unprotectable. The distinction matters enormously. A founder whose engineer uses GitHub Copilot to draft a module and then reviews, edits, restructures, and integrates that code into the product has a copyrightable work. The protectable elements are the portions where human judgment created original expression. The AI-generated portions the engineer accepted without modification are a different question.

The Copyright Office's selection-arrangement-modification test gives the analytical structure. The Office has applied it in registration decisions since January 2025 in a consistent pattern. If an engineer used AI to generate a first draft of code and then rewrote 60 percent of it in the company's architecture style, the rewritten portions carry human authorship and are protectable. If a designer used AI to generate ten logo concepts and then chose one with no modification, the choice itself may not satisfy the originality threshold: selection of an existing work without original creative contribution has historically not supported copyright protection. If a product team used AI to generate marketing copy and then revised the tone, restructured the argument, and added brand-specific phrasing throughout, the revisions are the protectable layer.

The core rule an AI-using company must internalize: companies cannot treat AI-assisted output as company-owned content by default. The ownership depends on a record of what the human team contributed. That record does not have to be elaborate, but it has to exist before diligence starts.

The patent side: November 2025 guidance and the AI-as-tool standard

Patent inventorship under 35 U.S.C. § 100(f) requires a human inventor who "conceived" the claimed invention. In patent law, "conception" is a term of art: it means the formation, in the mind of the inventor, of a definite and permanent idea of the complete and operative invention as it is hereafter to be applied in practice. Conception is a mental act current doctrine requires a human to perform.

The question for AI-using startups was whether the Pannu factors, the three-part test developed in Pannu v. Iolab Corp. (Federal Circuit 1998) to evaluate joint inventorship among multiple human contributors, applied when one of the contributors was an AI system. The three Pannu factors are: (1) contribution to the conception of the claimed invention; (2) collaboration with other inventors; and (3) contribution to the joint conception of the whole invention rather than merely an obvious element. If an AI system had to satisfy those factors, and courts held it could not because it was not a legal person, the implication for patent validity was unclear and potentially destabilizing.

The USPTO's November 2025 guidance resolved this. The guidance, published in the Federal Register on November 28, 2025, treats AI as a tool analogous to laboratory equipment, a computer, or a database. Just as inventing with the help of a computer does not require the computer to be named as a joint inventor, inventing with the help of an AI system does not require the AI to be named. The Pannu factors are not applied to single-human-plus-AI scenarios. The conception test applies to the human inventor, and to no one else.

Under the November 2025 USPTO guidance, the examiner's analysis in a single-human-plus-AI case does not ask whether the AI contributed significantly to the claimed invention under the Pannu factors. The examiner asks whether the named human inventor can demonstrate conception of the claimed invention. When a patent application discloses that AI tools were used in the inventive process, the analysis is the same as it would be for a human who invented with the help of sophisticated software: does the record support the conclusion that a human being formed the required mental picture of the complete and operative invention?

What that means for documentation is specific. If a founder or lead engineer used an AI tool to generate design options, test hypotheses, or accelerate prototyping, the patent application and the inventor's declaration must be supported by contemporaneous records showing the human inventive contribution. The record should capture: the date of conception; the named inventor or inventors and their roles; the problem being solved; the specific idea the human formed about how to solve it; which AI tool was used and in what capacity; what the AI generated; how the human evaluated, selected, modified, or rejected the AI output; and how the human's refinement led to the claimed invention.

This is not a new documentation burden in kind. Patent counsel has always required inventors to maintain lab notebooks and invention disclosure records. What is new is that the AI-tool-use component of that record is now expected. An application that discloses AI assistance without a supporting record of human conception is more likely to face an examiner inquiry, and a patent that issues without that record is more vulnerable to post-grant challenge on inventorship grounds.

The timing issue is the one that matters most in a startup context. Inventor declarations are executed at or near the time of filing. The contemporaneous record has to exist before the filing, not after. A company that waits until it is in Series B diligence to reconstruct who conceived what will find the record thin and the post-grant risk hard to quantify.

The assignment gap: why AI does not fix a weak contract

The work-made-for-hire doctrine under 17 U.S.C. §§ 101 and 201, which provides that works created by employees within the scope of their employment belong to the employer, is straightforward for full-time employees working within their normal job duties. It becomes more complicated when contractors, agencies, consultants, or tool vendors are involved.

Independent contractor work does not qualify as work made for hire unless the work falls into one of nine statutory categories listed in 17 U.S.C. § 101 (which include contributions to collective works, parts of motion pictures, translations, and a few others) and there is a written agreement between the parties expressly designating the work as made for hire. Code, graphic design, and marketing copy produced by a contractor do not fall into those categories in most cases. That means the rights to a contractor's work belong to the contractor unless there is a written agreement transferring them.

AI-generated work does not change this dynamic. It makes the gap more likely to surface because the contractor's contribution is often less obvious. If an agency produced a brand identity using AI image generators and a human designer made selection and arrangement choices, the diligence question is whether the assignment covers the AI-assisted work, who made the protectable human contribution, and whether the contractor had the rights to assign in the first place (which requires reviewing whether the contractor's own AI vendor agreement allows commercial exploitation of outputs).

The standard IP assignment clause in a contractor or consulting agreement should address: (1) the full scope of work created during the engagement, including AI-assisted output; (2) the contractor's representation that they have the right to assign (including a representation that their AI vendor terms permit assignment of outputs); (3) a specification of which elements of the AI-assisted output include human authorship the contractor is transferring; and (4) cooperation obligations covering patent filings and inventor declarations.

The agency-of-record relationship, common in marketing, adds another layer. If a startup used an outside creative agency that created brand assets, copy, and digital content over multiple years under an older engagement letter predating the generative AI era, that letter almost certainly does not address AI-assisted work and may not contain a broad enough assignment to cover it. The company may believe it owns its brand assets when the controlling contract does not actually say so.

The trade secret side: what the 2026 cases established

Trade secret protection under the Defend Trade Secrets Act (18 U.S.C. § 1836, the federal civil cause of action for trade secret misappropriation) requires, among other things, that the information at issue derives independent economic value from not being generally known or readily ascertainable, and that the owner takes "reasonable measures" to maintain its secrecy (defined at 18 U.S.C. § 1839(3)(A)). State trade secret law under the Uniform Trade Secrets Act (a model law adopted in 48 states and the District of Columbia) follows substantially the same standard.

The "reasonable measures" element is the one AI vendor behavior directly touches.

Trinidad v. OpenAI (decided January 2026) is the clearest statement of the principle. The plaintiff had fed proprietary business frameworks, process documentation, and competitive intelligence into ChatGPT using a consumer-tier account. When a dispute arose, the plaintiff attempted to assert trade secret claims over those materials. The court dismissed those claims. The court's reasoning: by feeding confidential materials into a platform where the vendor's terms of service permitted use of input data for model training and improvement, the plaintiff voluntarily disclosed those materials to a third party without contractual restrictions governing confidentiality. That voluntary disclosure, even if the plaintiff did not intend to "publish" the information, was sufficient to defeat the "reasonable measures" element. You cannot claim you took reasonable steps to keep something secret if you gave it to a third party without a confidentiality obligation on that third party.

The Trinidad v. OpenAI rule under 18 U.S.C. § 1839(3)(A) is not that companies cannot use AI tools with confidential data. It is that the contractual framework governing that use has to be in place before the input happens. Enterprise agreements with AI vendors that include data-use restrictions, no-training commitments, and confidentiality representations change the analysis. What the Trinidad court held as insufficient was ad hoc use without contractual protection.

In United States v. Heppner, No. 25-cr-00503-JSR (S.D.N.Y. Feb. 17, 2026), Judge Rakoff applied the same logic in the attorney-client privilege context. Documents created using generative AI tools where the vendor had no contractual confidentiality obligation were not protected by privilege because privilege presupposes confidential communication. If the AI platform can see the input and the platform's terms allow use of that input without confidentiality obligations, the communication is not confidential in the legal sense. The practical implications extend beyond legal work product: the reasoning applies to any confidential communication or information that passes through a platform without contractual secrecy obligations.

These two cases together establish a clear pattern. Courts are treating enterprise-tier AI agreements with data-use restrictions as the floor for "reasonable measures," not a compliance extra.

The Vendor Picture as of May 2026

The trade secret analysis turns on specific vendor terms, and those terms are not uniform. Here is where the major tools stand as of May 2026.

OpenAI (consumer tier, ChatGPT Free and Plus): OpenAI's terms of service for consumer products permit use of user inputs to train and improve its models unless the user opts out. As of May 2026, consumer-tier accounts do not carry the contractual no-training commitment enterprise accounts include. Feeding confidential materials into a consumer-tier ChatGPT account without opting out is, under the Trinidad reasoning, unlikely to satisfy the "reasonable measures" standard.

OpenAI Enterprise and API: The OpenAI enterprise agreement includes a data processing agreement that, when executed, prohibits using customer inputs to train models. An enterprise account with the no-training DPA in place provides the contractual protection Trinidad treats as required.

Anthropic (Claude): Anthropic's API and enterprise terms include a commitment that Anthropic will not use customer inputs to train models without consent. The Claude.ai enterprise tier carries similar restrictions. Consumer-tier Claude.ai does not carry the same commitment.

Google Gemini (enterprise): Google's enterprise terms for Workspace customers and Gemini API customers include commitments that inputs are not used for training. The consumer Gemini product does not include the same commitment.

Microsoft Copilot (enterprise): Microsoft's enterprise Copilot agreement includes data protection commitments consistent with the enterprise-tier standard. Consumer-facing Copilot products do not.

GitHub Copilot (effective June 1, 2026): GitHub Copilot's transition to usage-based billing, effective June 1, 2026, coincides with differentiated data-handling regimes across the three underlying model providers GitHub now supports. Anthropic-powered Copilot suggestions are covered by a zero-retention commitment. Google Gemini-powered suggestions are not used for training. OpenAI-powered suggestions operate under an opt-out model: unless the user or the organization administrator has opted out, OpenAI may use those interactions for training. A team using GitHub Copilot without understanding which model is active is operating under a split data-retention regime, one part of which may not satisfy the "reasonable measures" standard for confidential code.

Cursor: Cursor's enterprise offering includes data isolation and confidentiality commitments. Consumer-tier use does not carry the same terms. Cursor's privacy policy for consumer accounts does not include the equivalent of a no-training DPA.

The practical takeaway from this vendor map is that "we use enterprise AI tools" is not a single statement. Each tool has its own terms, each tier within that tool has its own data-handling rules, and the model-level split within orchestrated tools like GitHub Copilot means a team using a single product may be operating under different rules depending on which model processes a given prompt. The company that can describe its vendor terms at the model level has a defensible position. The company that can only say "we use GitHub Copilot" does not.

Why Does This Create a Business Decision, and When?

The risk shows up in four specific places, each with a different moment of consequence.

In a financing round. IP representations in Series A and Series B term sheets and purchase agreements have become more specific about AI-assisted work. Where IP reps in 2023 asked "does the company own its IP?", IP reps in 2026 increasingly ask: whether the company used AI in the development of core IP; whether the company can identify the human author or inventor of each material asset; whether the company has enterprise-tier agreements with no-training commitments for all AI tools used in development; and whether contractor assignments cover AI-assisted work. A founder who receives a diligence request keyed to those reps and cannot produce the records faces a negotiation problem that is not a legal technicality. It is the kind of gap that gives investors the right to require escrow, delay close, or reprice the deal.

In a patent filing. The inventorship problem surfaces at two moments: at the time of filing, when inventor declarations must be executed, and post-grant, when a challenger can argue for patent invalidity on inventorship grounds. A company that files based on inventor declarations not supported by contemporaneous human-conception records has a patent that is harder to defend. The USPTO's November 2025 guidance does not require companies to prove AI had nothing to do with the process. It does require that the human inventor's conception be documentable. A company that treats patent records as a post-hoc formality, reconstructing them in diligence, is taking a risk it cannot price at the time it takes it.

In a trade secret dispute. The moment of failure in a trade secret claim often comes years after the confidential information was disclosed to the AI tool. The company discovers at the time of litigation that the legal standard requires documentation of contemporaneous reasonable measures. The question is not what the policy said at the time. The question is what the actual practice was, evidenced by vendor agreements, employee training records, and tool-use logs. A policy that says "do not input confidential information into consumer AI tools" without corresponding evidence the policy was followed and enforced is unlikely to satisfy the "reasonable measures" standard under a Trinidad-type analysis.

In a contractor or agency relationship. The assignment problem is discovery-resistant until diligence. A company will not know its contractor's engagement letter did not cover AI-assisted work until someone looks at the letter in the context of a deal. The further back that gap reaches, the more reconstruction work is required, and the less likely the reconstruction is to be fully satisfying to a buyer or investor.

The counterpoint worth naming: not every company faces all four failure points with equal severity. A SaaS company whose core product is AI-native code, where the founding engineers wrote the architecture and used AI only for boilerplate functions, faces a different risk profile than a company whose customer-facing product experience was substantially AI-generated by contractors under vague agreements. The analysis has to be asset-by-asset. What the November 2025 guidance and the 2026 cases clarified is that "asset-by-asset" is the right level of analysis, not company-wide comfort with a policy statement.

What We're Seeing in Practice

The pattern across founder-stage companies this year is consistent: the documentation gap is almost never intentional. Most founders using AI tools made reasonable business choices about which products to use, when to use them, and how fast to move. The problem is that the legal record those choices created was not built at the time.

A few patterns recur across the diligence questions that surface. The first is the contractor assignment issue: engagement letters from 2022 and 2023, written before generative AI was a standard part of professional workflow, do not address AI-assisted output. The letters have standard IP assignment language, but the standard language was written when "work product" meant documents the human wrote. The second is the patent timing issue: invention disclosure records exist but do not capture AI tool use. Examiners are increasingly looking for that capture. The third is the vendor tier issue: founders who moved from consumer-tier to enterprise-tier AI tools at some point in the company's history cannot always identify exactly when the switch happened for each tool, which means the records for the period of consumer-tier use may be thin.

None of these are fatal on their own. Each is addressable before the financing conversation starts. The founders who have done the documentation work before a process opens are in a different position than the ones who are reconstructing it under deal pressure.

Decision Framework

These are the questions a founding team or board can work through before a financing process, acquisition discussion, or patent filing.

1. Have we inventoried AI-assisted work by IP type? Code, copy, design, patents, and internal commercial materials each carry different ownership requirements. The question is whether the company has separated them on an asset-by-asset basis. An answer of "we know roughly what we built" is not the same as a category-by-category inventory with ownership records attached to each category.

2. Can we document what the human team actually contributed? For copyright, the record needs to show human selection, arrangement, modification, or other original expression beyond prompt generation. For patent, the record needs to show human conception of the claimed invention, including when it formed and how the inventor evaluated and refined the AI-assisted output. The test here is whether the records exist contemporaneously, not whether the human contribution could be reconstructed in a narrative.

3. Do our contractor and agency agreements cover AI-assisted work? The question is whether the assignment language is broad enough to cover AI-assisted work product, whether the contractor represented that their AI vendor terms permit assignment of outputs, and whether the agreement covers both the AI-generated content and the human contribution that makes it protectable. Older agreements often fail this test.

4. Have we confirmed enterprise terms are in place for every tool that touched confidential data? Each AI tool the company uses has its own data-retention and training terms, and those terms vary by tier within a given product. The question for each tool is: does the current agreement include a no-training commitment and a confidentiality obligation that would satisfy a court applying the DTSA "reasonable measures" standard? This is a vendor-by-vendor review, not a policy statement.

5. Is the IP ownership schedule investor-ready before the process opens? A financing process starts with an IP rep. The IP rep in a 2026 term sheet is likely to ask specifically about AI assistance. The question is whether the company can produce the underlying schedule, the assignment records, the human contribution records, and the vendor terms, in response to that rep, before the diligence clock runs under a signed letter of intent. A company that builds this schedule in advance controls the diligence narrative. A company that builds it under LOI pressure does not.

Frequently Asked Questions

Who owns AI-generated code at a startup?

Ownership of AI-generated code depends on two things: who made the original human contribution to that code, and whether the rights were properly assigned to the company. An employee who uses GitHub Copilot or a similar tool and then reviews, edits, restructures, and integrates the output into the product creates a potentially copyrightable work; the protectable elements are the human contributions. But if the code was written by a contractor using an AI tool, the company needs both a written assignment and confirmation that the contractor's own AI vendor agreement permits commercial assignment of outputs. The Copyright Office's selection-arrangement-modification framework, applied since January 2025, is the operative test for what qualifies as protectable human authorship.

Does the USPTO require naming an AI system as a co-inventor?

No. The USPTO's November 28, 2025 guidance, published in the Federal Register, treats AI as a tool rather than a co-inventor. A human inventor using an AI tool in the inventive process is evaluated under the same conception test that applies to any human inventor: did the named person form a definite and permanent idea of the complete and operative invention? The Pannu joint-inventorship factors do not apply to single-human-plus-AI scenarios. What the guidance does require is contemporaneous documentation showing the human inventor's conception. That means records capturing the problem, the inventive idea, the AI tool's role, and how the human evaluated and refined the AI output.

Does feeding confidential information into an AI tool destroy trade secret protection?

It depends on the contractual framework in place at the time of input. Under the reasoning in Trinidad v. OpenAI (January 2026), feeding confidential materials into a consumer-tier AI platform whose terms permit use of inputs for model training, without opting out or having a separate confidentiality agreement, is unlikely to satisfy the Defend Trade Secrets Act's "reasonable measures" requirement under 18 U.S.C. § 1839(3)(A). Enterprise accounts with executed data processing agreements that include no-training and confidentiality commitments provide the contractual protection courts are treating as the floor. The critical point is that the contractual framework has to be in place before the input happens, not after a dispute arises.

What does "reasonable measures" mean for AI vendor contracts under the DTSA?

Courts applying the Defend Trade Secrets Act in the wake of Trinidad v. OpenAI and United States v. Heppner (both 2026) are treating enterprise-tier AI agreements with data-use restrictions as the floor for "reasonable measures," not a compliance extra. A company that can show a signed data processing addendum or data use amendment specifying no-training and confidentiality terms has a documentable basis for "reasonable measures." A company relying on its internal policy or the vendor's general privacy representations, without a signed contractual commitment, faces the argument that it did not take the legally required steps. The analysis is the same under most state Uniform Trade Secrets Act variants.

What does investor diligence on AI-assisted IP actually look like in a 2026 term sheet?

IP representations in Series A and Series B financing documents have become materially more specific about AI-assisted work since 2023. The 2026 version of the IP rep increasingly asks: whether the company used AI in the development of core IP; whether the company can identify the human author or inventor of each material asset; whether enterprise-tier agreements with no-training commitments are in place for all AI tools used in development; and whether contractor assignments expressly cover AI-assisted work product. A company that can produce an asset-by-asset IP ownership schedule addressing each of those layers before the letter of intent is signed is in a materially different diligence position than one that builds the schedule under deal pressure.

Audience-Specific Implications

For Founders

The chain-of-title problem is a pre-financing problem, not a financing problem. The documentation a 2026 investor will ask for was generated at the time the work was created: inventor declarations, contractor assignments, vendor agreements, and human contribution records. A company that waits until term sheet to understand what its IP records say is starting from the hardest possible position. The practical question is whether the current state of the company's records supports the IP representations that will appear in the purchase or investment agreement, and whether any gaps can be closed before a process begins. The answer to that question should be known before the first investor meeting, not after.

For Investors and Boards

The IP diligence question for AI-using startups has changed in a specific way. The earlier question was "does the company own its IP?", answered by reviewing assignment agreements. The 2026 version of that question has four components: does the company own its IP (assignment), can it prove protectable human authorship or inventorship (copyright and patent layer), has it maintained trade secret protection on confidential inputs (vendor terms layer), and can it produce contemporaneous documentation for each layer (records layer)? A portfolio company board that has not built the IP ownership schedule described in The Brief has an audit committee agenda item that is not a hypothetical risk. It is a deal-process risk that can price into the next round if it surfaces in diligence before the company addresses it.

Practical Takeaways

Build a category-by-category AI-assisted IP inventory before the next financing process, because investors are asking for it at the rep level. The inventory should separate code, copy, design, patent-track inventions, and internal commercial materials, and attach the relevant ownership record (assignment, contributor identity, human contribution documentation) to each category. An inventory that exists before a process opens gives the company time to close gaps; one built under LOI pressure does not.
Audit contractor and agency engagement letters for AI coverage, particularly letters predating 2024, because the standard assignment language in those letters likely does not cover AI-assisted output. If the letter does not expressly assign AI-assisted work product, or does not contain the contractor's representation that their vendor terms permit assignment, the assignment may not be complete. The fix is an amendment or a retroactive IP assignment agreement with the contractor, executed before diligence.
For each patent application in progress or planned, confirm the inventor's contemporaneous records capture the human conception narrative at the tool-use level, because the November 2025 USPTO guidance means examiners expect that record to exist. The record should include: the date the conception formed, the inventor's name and role, the problem being solved, the specific inventive idea the human formed, what the AI tool was used for, how the human evaluated and modified the AI output, and how the refinement led to the claimed invention. Reconstructing this record after filing is harder and less reliable than building it at the time of conception.
Run a vendor-by-vendor review of data-retention and training terms for every AI tool that touched confidential code, customer data, pricing logic, or product roadmap information, because Trinidad v. OpenAI established that consumer-tier use without contractual protection is unlikely to satisfy the DTSA "reasonable measures" standard. For each tool, the question is: does the current agreement include a no-training commitment and a confidentiality obligation? For GitHub Copilot users effective June 1, 2026, the review should identify which underlying model is active, because the data-retention regime varies by model.
Confirm that any enterprise AI agreements include a written data processing addendum or data use amendment that specifies no-training and confidentiality terms, and do not treat verbal assurances or product documentation as a substitute for a signed agreement, because courts have held the contractual commitment is what satisfies the "reasonable measures" element, not the company's internal policy or the vendor's general privacy representations.
Update standard contractor and consulting agreements to expressly address AI-assisted work product going forward, covering the assignment of AI-generated content, the contractor's representation that vendor terms permit commercial assignment of outputs, and the contractor's cooperation obligation for patent filings. The goal is to make sure the assignment mechanism covers the actual work product the contractor is producing, not to prohibit contractor use of AI tools.
If the company is preparing for a Series A or Series B process, build the investor-ready IP ownership schedule now, as a standalone document, and have counsel review the representations it would support before the term sheet is signed. The schedule should identify each major AI-assisted asset, the contributor, the assignment record, the human contribution record, the relevant vendor terms, and any remaining risk noted. A company that presents this schedule proactively in a financing process, rather than producing it reactively under diligence pressure, controls the IP conversation.
For board and audit committee reporting, present the AI-assisted IP inventory and the vendor-terms map as standing agenda items now, not only in connection with a financing or acquisition, because the gaps that create diligence problems are typically discovered after the fact. The board does not need to review every prompt and output. It should see the ownership schedule by category, the contractor agreement audit status, and the vendor terms map with identified gaps, at least annually and before any material transaction process begins.

Closing Perspective

The chain-of-title problem in AI-assisted IP is not a failure of law. The law is, if anything, clearer now than it was three years ago. Copyright requires human authorship and the Copyright Office has a working test for measuring it. Patent requires human conception and the USPTO has a working framework for documenting it. Trade secret requires reasonable measures and the courts have told companies what those measures look like in an AI-vendor context. The failure is documentation. It is the gap between what founders believed they owned and what the records actually say.

What I keep coming back to is that this is a pre-financing problem in the same way a cap table cleanup is a pre-financing problem. The company that discovers the issue under LOI pressure is not in a position to close it cleanly. The company that addressed it six months before a process opened is. The founders who treat the IP ownership schedule as a living document rather than a diligence artifact, who update it every time a contractor agreement is signed, every time a patent is filed, and every time the company adds or changes a material AI tool, will close faster and at better terms than the founders who reconstruct it under time pressure.

The next signal worth watching is how the Copyright Office's Part 2 framework gets applied in contested registration proceedings over the next twelve months. The selection-arrangement-modification test is the operative framework, but the Copyright Office has not yet worked through a full cycle of contested applications applying it to the range of AI-assisted work patterns enterprise teams actually use. The registration decisions from late 2025 through mid-2026 will tell us where the practical floor sits.

"We own our IP" has always been a record, not a sentence. AI just made the record harder to build and easier to audit.

Sources

U.S. Congress, Copyright Act, 17 U.S.C. §§ 101, 102. Available at U.S. Code, Title 17.
U.S. Copyright Office. "Works Containing Material Generated by Artificial Intelligence: Copyright Registration Guidance." March 2023. Federal Register.
U.S. Copyright Office. "Copyright and Artificial Intelligence, Part 2: Copyrightability." January 29, 2025. Available at copyright.gov.
Thaler v. Perlmutter, No. 23-5233, U.S. Court of Appeals for the D.C. Circuit, March 18, 2025. Cert. denied, U.S. Supreme Court, March 2, 2026. Available at Justia.
Thaler v. Vidal, 43 F.4th 1207, U.S. Court of Appeals for the Federal Circuit, 2022.
Pannu v. Iolab Corp., 155 F.3d 1344, U.S. Court of Appeals for the Federal Circuit, 1998.
U.S. Congress, Patent Act, 35 U.S.C. § 100(f). Available at U.S. Code, Title 35.
U.S. Patent and Trademark Office. "Revised Inventorship Guidance for AI-Assisted Inventions." Federal Register, November 28, 2025. Available at federalregister.gov.
U.S. Congress, Defend Trade Secrets Act, 18 U.S.C. §§ 1836, 1839(3)(A). Available at U.S. Code, Title 18.
Trinidad v. OpenAI, decision reported January 2026. Discussed in IPWatchdog, April 5, 2026.
United States v. Heppner, No. 25-cr-00503-JSR (S.D.N.Y. Feb. 17, 2026) (Rakoff, J.).
GitHub. "GitHub Copilot Plans and Data Retention." Updated May 2026. Available at github.com/features/copilot/plans.
Uniform Trade Secrets Act (1985, as amended 1985). Adopted in 48 states and the District of Columbia.

This article is general educational analysis. It does not provide individualized legal advice, client-specific recommendations, outcome guarantees, or jurisdiction-specific directives without factual context.

This article is for informational purposes only and does not constitute legal advice. Every company's situation is different, and you should consult with qualified legal counsel before making compliance decisions based on the developments discussed here.