AI Disputes Are Multiplying Could Litigation Funding Be Part of the Solution

Every week appears to bring another dispute involving artificial intelligence into a courtroom, arbitration, regulatory investigation or settlement negotiation.

Authors and publishers are challenging the use of copyrighted works in model training. Technology companies are fighting over datasets, source code, confidential information and competitive advantage. Consumers and employees are questioning automated decisions. Businesses are confronting liability for AI-generated outputs, defective software, misleading claims and data misuse.

The legal questions are difficult. The financial questions may be just as important.

AI disputes can require specialist lawyers, technical experts, forensic analysis, extensive discovery and proceedings across several jurisdictions. A claimant may hold a credible legal right and possess strong evidence, yet still lack the capital or risk appetite required to pursue the case effectively.

That is why litigation funding may become increasingly relevant to the AI disputes economy. It will not finance every claim, and it cannot convert uncertainty into certainty. But for eligible cases with strong merits, substantial recoverable value and a credible enforcement path, external funding may help ensure that the ability to litigate is not determined solely by the size of the parties’ balance sheets.

AI litigation is not one category of dispute

The phrase AI litigation can be misleading because it suggests a single, coherent field. In practice, disputes involving AI arise through established areas of law that are being tested by new technologies, new evidence and new commercial models.

Copyright cases ask whether copying works for model training is permitted, whether particular outputs reproduce protected expression, and whether training or output markets have been harmed. Trade-secret claims focus on the acquisition or use of confidential datasets, model weights, source code, algorithms and business information. Contract disputes arise from AI-development agreements, cloud services, licensing arrangements, indemnities, performance commitments and allocation of ownership.

Other cases may concern privacy, data protection, discrimination, consumer protection, product liability, professional negligence, defamation or competition. The legal theory may be familiar, but the factual inquiry can be unusually technical. Parties may need to reconstruct data pipelines, explain model architecture, test outputs, identify provenance and separate the contribution of training data from later fine-tuning, retrieval or user prompting.

This diversity matters for funding. A funder does not invest in “AI litigation” as an abstract trend. It evaluates a defined cause of action, a particular claimant, identifiable defendants, evidence, damages, budget, duration and enforcement strategy.

What recent cases already show

The emerging case law does not provide one simple answer. Instead, it demonstrates why AI disputes may be expensive, evidence-intensive and highly sensitive to the factual record.

Thomson Reuters v ROSS Intelligence

In February 2025, the United States District Court for the District of Delaware revised an earlier ruling and granted partial summary judgment to Thomson Reuters in its dispute with ROSS Intelligence. The court held that certain Westlaw headnotes were protected and that ROSS had not established fair use for the copying at issue. The court stressed that ROSS sought to develop a competing legal-research product and treated market effects as particularly important. The opinion also expressly distinguished the non-generative system before it from generative AI.²

The decision is important not because it settles every AI-training dispute, but because it shows that the purpose of the use, the nature of the material, the competitive relationship and the evidential record can determine the result.

Bartz v Anthropic

In June 2025, the Northern District of California concluded that Anthropic’s use of books to train large language models was highly transformative and qualified as fair use on the record before the court. At the same time, the court treated Anthropic’s acquisition and retention of millions of pirated books for a general-purpose library as a separate use that was not justified merely because some books might later be used for training.³

The distinction illustrates a recurring feature of AI litigation: one technical project may involve several legally distinct acts. Acquiring data, retaining it, training a model, deploying the model and generating outputs may require separate analysis.

Kadrey v Meta

Two days later, another judge in the Northern District of California granted Meta partial summary judgment on fair use in a case brought by authors. The court emphasised that the plaintiffs had not produced meaningful evidence of the alleged market dilution that could have changed the fourth-factor analysis. The opinion warned that a different record, with evidence of market harm, could produce a different outcome.⁴

For funders and claimants, the lesson is direct: a compelling legal theory is not enough. The claimant must identify the relevant market, explain the economic injury and support the damages case with evidence capable of surviving expert and judicial scrutiny.

The New York Times and consolidated OpenAI litigation

The litigation involving The New York Times, authors, OpenAI and Microsoft has become a central test of how copyright law applies to generative-AI training and outputs. As of September 2026, parties in the consolidated federal proceedings in New York had moved for summary judgment, placing questions of transformative use, market substitution and licensing before the court.⁵

Whatever the eventual outcome, the scale of the dispute illustrates the resources required to litigate model training: document discovery, technical evidence, expert economics, representative works, output testing and analysis of potential licensing markets.

Why AI disputes can become unusually expensive

Complex commercial litigation is already costly. AI disputes add layers that can cause budgets to grow quickly.

The first layer is technical proof. Lawyers may need support from machine-learning specialists, data scientists, software engineers, cybersecurity professionals and digital-forensic experts. The court or tribunal must understand what the system did, which data it used, how the relevant output was produced and whether the disputed conduct can be traced to a particular defendant.

The second layer is information asymmetry. Much of the decisive evidence may be held by the AI provider or enterprise deployer. Training datasets, model documentation, logs, evaluation results, safety records and internal decision-making may not be publicly available. Obtaining and reviewing that material can generate discovery disputes, confidentiality regimes and large document-review exercises.

The third layer is damages. A claimant must move beyond announcing a large headline value. It may need to prove lost licence revenue, market substitution, diminished asset value, diverted sales, unjust enrichment, remediation costs or another legally recoverable measure. New AI markets can make counterfactual analysis difficult because pricing, licensing norms and competitive conditions are still developing.

The fourth layer is jurisdiction. Data may have been gathered in one country, processed in another, used to train a model elsewhere and delivered globally. Rights and remedies differ across borders. Separate proceedings may be required, while enforcement must focus on jurisdictions where defendants and assets can be reached.

The fifth layer is speed. In disputes involving confidential information, product launches, digital replicas or harmful automated decisions, delay may destroy much of the commercial value of relief. Interim injunctions, preservation applications or urgent expert work can front-load costs before liability has been determined.

The regulatory environment will generate additional disputes

AI litigation will not remain limited to copyright. The European Union’s revised Product Liability Directive expressly treats software, including AI systems, as products and addresses defects connected with updates and continued learning under the manufacturer’s control. Member States must transpose the Directive by 9 December 2026, and it applies to products placed on the market or put into service from that date.⁶

The EU AI Act also creates supervision, documentation, transparency and enforcement obligations for covered actors, while leaving other regimes such as data protection, consumer protection and product safety applicable.⁷

In the United States, federal enforcement agencies have stated that existing civil-rights, consumer-protection and competition laws apply to automated systems.⁸

These measures do not mean that every breach automatically produces a privately fundable damages claim. Regulatory fines are not a claimant recovery, and some statutes do not provide a private right of action. Nevertheless, new duties, documentation requirements and evidence-access mechanisms may influence civil claims, contractual disputes, collective actions and settlement dynamics.

Where litigation funding may fit

Third-party litigation funding is an arrangement under which an independent funder provides capital for some or all of the approved costs of pursuing a legal claim. In return, the funder receives an agreed return from a successful judgment, award, settlement or other defined recovery.

Commercial funding is commonly non-recourse. If the claim fails, the funder ordinarily loses the deployed capital, subject to the precise agreement and exceptions such as fraud, material non-disclosure or breach of funding obligations.

In an AI dispute, approved funding might cover lawyers, technical experts, digital forensics, data analysis, economic experts, court or tribunal fees, security for costs and enforcement. The arrangement can allow a claimant to preserve capital for its operating business while transferring part of the litigation risk.

Funding may also help balance bargaining power. A well-resourced technology defendant may be capable of sustaining prolonged discovery, multiple motions and appeals. A smaller creator, startup, data owner or technology business may have a credible claim but be unable to match that expenditure. Funding can provide endurance, although the claimant and counsel must retain proper authority and professional independence under the applicable law and agreement.

AI related claims that may attract funding

The most plausible candidates are not defined by the word AI. They are defined by legal merits and commercial recovery.

Copyright and database claims may be candidates where a claimant can establish ownership, copying or other actionable use, a defensible answer to fair-use or statutory-exception arguments, meaningful market harm and a realistic damages or licensing outcome.

Trade-secret claims may be attractive where the confidential information is clearly identified, reasonable protection measures were used, misappropriation can be traced and the loss or defendant gain is substantial. Claims involving model weights, proprietary datasets, source code or confidential research may require urgent relief and extensive forensic work.

Contract and licensing disputes may offer clearer funding economics. Agreements may allocate ownership of training data, outputs, fine-tuned models, improvements or intellectual property. They may also contain payment obligations, audit rights, warranties, indemnities and performance standards. A defined royalty or payment stream can be easier to value than a novel tort theory.

Competition and business-tort claims may be considered when alleged data acquisition, exclusionary conduct or misuse of confidential information caused substantial measurable loss. Product-liability and consumer claims may also develop, particularly where software or an AI-enabled product causes recognised damage and an appropriate defendant can satisfy a judgment.

Collective or portfolio structures may be relevant when individual claims are too small to finance separately but share common facts, defendants or legal questions. Whether aggregation is permitted and commercially workable depends on procedural law, class or representative-action rules, standing and funding regulation.

How a funder will assess an AI dispute

A funder begins with legal merits. The analysis must identify the governing law, cause of action, available defences and procedural posture. Novelty may increase potential significance, but it also increases uncertainty. A case built entirely on an untested legal proposition may require stronger economics and evidence to compensate for that risk.

Evidence is equally important. The claimant should be able to explain what data, work, code, model or decision is disputed; who controlled it; how it was used; and how the conduct can be proven. Technical assertions must be translated into a case theory that judges, arbitrators and experts can test.

The damages model must be realistic. A funder will distinguish the value of an entire company or IP portfolio from the compensation legally recoverable in the specific case. It will test assumptions about licensing markets, causation, apportionment, mitigation, competing products, attribution and time to recovery.

Budget proportionality is critical. The expected recovery must be sufficient to cover the legal budget, the funder’s contractual return and the claimant’s meaningful net recovery. A socially important or precedent-setting case may still be unsuitable for commercial funding if its monetary value is too low or too uncertain.

The defendant’s ability to pay and the enforcement route also matter. A judgment against an insolvent startup may have little funding value. Conversely, an award against a solvent enterprise is not enough if assets are unreachable or enforcement faces substantial legal barriers.

Finally, the funder will assess counsel, strategy, duration, interim-relief risk, counterclaims, regulatory overlap, confidentiality and settlement prospects. In AI cases, it may also test whether the requested access to source code, models or data is realistically obtainable and usable in proceedings.

Why some AI claims will not be fundable

Litigation funding is not a general subsidy for every person harmed or concerned by AI. Conventional commercial funding usually requires a credible monetary recovery.

A case may be unsuitable where the primary objective is a declaration, policy change or injunction that produces no monetisable benefit. It may also fail funding review where ownership is uncertain, the evidence is speculative, damages are remote, costs are disproportionate or the defendant lacks recoverable assets.

Some claims face a mismatch between public importance and private economics. Bias, transparency or accountability disputes may raise significant societal questions but provide limited individual damages. Those cases may depend on legal aid, public-interest organisations, contingency arrangements, collective procedures or specialised funding models rather than conventional commercial litigation finance.

The involvement of regulators also requires care. Administrative enforcement may strengthen the factual record, but fines paid to the state do not create proceeds for a private funder. The claimant must still establish its own legal right and recoverable loss.

Confidentiality privilege and control

AI funding diligence may require the disclosure of highly sensitive materials: unpublished technology, datasets, source code, model documentation, legal opinions, security information and commercial forecasts. The claimant and counsel should establish confidentiality protections before substantive disclosure.

Documents should be shared in stages. An initial memorandum, chronology, damages summary, budget and selected evidence may allow preliminary assessment before access to the most sensitive technical information is granted. Privilege, professional secrecy, data-protection restrictions, contractual confidentiality and export-control obligations must be assessed under the applicable law.

The funding agreement should define information rights, budgeting, material developments, settlement consultation, termination and distribution of recoveries. It should not compromise counsel’s professional duties or permit improper control of the proceedings.

The UAE funding perspective

For UAE-connected disputes, the relevant forum matters. Onshore court proceedings, DIFC Courts, ADGM Courts and UAE-seated arbitrations do not operate under one identical procedural framework.

ADGM Courts issued Litigation Funding Rules in 2019, describing a framework intended to provide certainty for third-party financing of litigation and arbitration proceedings. The rules address funder qualifications, minimum terms, conflicts, settlement involvement and dealings with lawyers.⁹

The DIFC Courts address third-party funding through Practice Direction No. 2 of 2017. Among other requirements, a funded party must notify the other parties of the existence of a litigation funding agreement and identify the funder, while the agreement itself is not automatically disclosed unless ordered.¹⁰

These frameworks demonstrate that funding must be structured around the jurisdiction and forum. For an AI dispute, the analysis must additionally consider where the relevant rights arose, where the conduct occurred, the seat of any arbitration, governing-law clauses, confidentiality, available remedies and the location of enforceable assets.

WinJustice evaluates qualifying litigation and arbitration funding opportunities connected to the UAE. Global AI cases provide useful legal and commercial signals, but they do not establish that a foreign legal rule applies in the UAE or that a particular claim will qualify for funding.

Preparing an AI dispute for funding review

A claimant should begin by reducing a technically complex story to a disciplined legal and commercial submission. The funder must understand the parties, the disputed system or information, the alleged conduct, the cause of action, the evidence, the procedural stage and the remedy sought.

Ownership and authority should be documented. If the claim concerns copyrighted works, data, software or trade secrets, the chain of title, licences, employment provisions and confidentiality measures should be organised before approaching a funder.

The technical record should identify what is known, what remains inferred and what requires discovery. Unsupported assumptions about how a model was trained or why it generated an output can undermine credibility. Where possible, testing methods should be reproducible and supported by qualified experts.

The damages analysis should provide a realistic range rather than a headline number. It should explain causation, the available legal measure, key assumptions, potential deductions, settlement scenarios and the claimant’s expected net recovery after costs and funding return.

The legal budget should cover the full path, not merely the next procedural stage. It should consider experts, disclosure, interim applications, trial or arbitration, appeals and enforcement. A funder will also expect a clear description of the defendant’s assets and the proposed recovery strategy.

A new category of disputes requires a new financing conversation

AI is creating disputes faster than courts can produce settled answers. The cases already decided show that outcomes can turn on narrow distinctions: generative or non-generative use, training or acquisition, transformation or substitution, alleged harm or proven market evidence.

That uncertainty makes litigation funding both relevant and selective. Funding can provide the resources required to test serious claims, obtain expert evidence and sustain complex proceedings. At the same time, novelty, technical opacity and uncertain damages can make AI cases difficult investments.

The strongest funding opportunities are therefore likely to combine established legal rights with new technological facts: clear ownership, credible misconduct, reliable evidence, substantial monetary loss, proportionate costs and a solvent defendant.

As AI disputes multiply, the decisive question will not only be how courts interpret the law. It will also be whether meritorious claimants have the financial capacity to bring their cases, withstand the process and enforce the result.

Litigation funding may be part of that solution. But its role begins with disciplined assessment, not automatic approval.

About WinJustice

WinJustice is a UAE-based litigation funding company that evaluates funding opportunities involving qualifying commercial disputes, litigation and arbitration claims connected to the UAE. Funding decisions remain subject to legal, financial and enforcement due diligence, internal approval and agreed contractual terms.

To learn more or submit a claim for preliminary assessment, visit WinJustice.

Disclaimer

This article is provided for general informational purposes only and does not constitute legal, financial, tax, Sharia or investment advice. References to foreign cases and legislation are comparative and do not imply their application in the UAE. The availability, legality and structure of litigation funding depend on the applicable jurisdiction, forum, governing law and facts of each dispute.