The next risk for AI investors isn’t tech, it’s litigation

Key takeaway: Legal battles may reshape AI economics more than investors currently anticipate.

For the past three years, the investment case for AI has largely revolved around which developer could build the most powerful models.

Investors have poured hundreds of billions of dollars into companies developing AI chips, cloud infrastructure, large language models, and enterprise software, with the global AI market forecast to hit $540 billion in 2025, according to Grand View Research.

Morgan Stanley also forecasts around $2.9 trillion in global data center construction projected through 2028, however history suggests transformative technologies rarely reshape industries without eventually attracting legal scrutiny.

From tobacco and asbestos to opioids and social media, periods of rapid innovation have often been followed by years of litigation, regulatory intervention, and changing business economics.

While AI is fundamentally different from each of those industries, investors may be overlooking the increasing likelihood that litigation could become one of the defining investment risks of the AI era.

The legal challenges are already mounting. Copyright holders are accusing AI developers of unlawfully training models on protected works, and publishers, musicians, artists, and software developers are seeking compensation for the use of their intellectual property.

Privacy regulators are also questioning how companies collect training data, while consumer lawsuits are beginning to test who should be held responsible when AI systems allegedly cause harm.

With the number of regulator orders, bans, fines, and litigation against AI systems worldwide surpassing 214, the implications for investors extend well beyond legal expenses.

Litigation could influence licensing costs, compliance obligations, product design, disclosure requirements, competitive dynamics, and ultimately company valuations.

Litigation is accelerating

AI has moved from a largely academic technology to one of the fastest-growing areas of technology litigation.

Global AI law and policy tracker Regulations.ai found that 37 lawsuits have been filed against AI companies in the U.S. in the first half of 2026, compared to seven during the same period the year before.

All major AI developers have faced legal challenges, with disputes spanning several areas of law including copyright infringement, privacy and data protection, and consumer protection.

Defamation, product liability, competition law, and securities disclosure are also all emerging.

Many of the earliest lawsuits, including the New York Times’ 2023 lawsuit against OpenAI and Microsoft, centered on allegations of AI models being trained using copyrighted material without permission.

More recent examples include British bioethicist Thomas Shakespeare’s lawsuit against Anthropic, and Amargo Couture’s lawsuit alleging OpenAI violated several privacy acts by sharing users’ queries and personal identifying information with Meta and Google.

While the outcomes remain uncertain, the volume of litigation continues to grow as AI adoption accelerates.

The biggest legal battlegrounds

While AI litigation spans a wide range of legal issues, three core battlegrounds are emerging with the greatest implications for investors. Copyright, consumer harm, and privacy all have the potential to reshape the economics of AI development in different ways.

Copyright

Perhaps the most consequential legal question concerns the datasets used to train frontier AI models, representing 23 (62%) of lawsuits against U.S. AI developers in the first half of 2026.

Most models have been trained using enormous quantities of publicly available internet content, much of which is protected by copyright.

AI developers generally argue this constitutes fair use or falls within existing copyright exceptions because the models do not reproduce works in their original form.

Rights holders, ranging from authors and publishers to artists and software developers, disagree. They are increasingly arguing that AI companies have built commercial products using their intellectual property without consent or compensation.

In addition to the aforementioned New York Times lawsuit, landmark cases such as Getty Images vs Stability AI, and Universal Music Group vs Suno paved the way to challenge AI companies on their fair use claims.

Since then, additional copyright lawsuits have been filed against firms such as NVIDIA, Anthropic, and Perplexity AI, highlighting the accelerating pace of litigation.

Ultimately, if courts determine that licensing is required for model training, the economics of frontier AI could change significantly.

Rather than relying on freely available internet data, developers may need to negotiate licensing agreements across multiple industries, increasing both operating costs and barriers to entry.

Some AI developers are already moving towards licensed content. OpenAI has signed agreements with organizations including the Associated Press, Axel Springer, Shutterstock, and the Financial Times, allowing it to access copyrighted content under negotiated commercial terms.

These agreements suggest parts of the industry are already preparing for a future in which high-quality training data carries an explicit price.

Consumer harm

A second wave of litigation focuses on how AI systems interact with users. These represent 10 lawsuits (27%) against U.S. AI developers in the first half of 2026.

Unlike traditional software, generative AI produces unpredictable outputs that may influence human behavior in unexpected ways.

Several lawsuits now allege that AI chatbots have contributed to emotional distress, misinformation, reputational harm, or dangerous advice.

One closely watched case involves Florida Attorney General James Uthmeier’s lawsuit against OpenAI alleging that interactions with ChatGPT contributed to the death of a Florida teenager after the system allegedly encouraged emotional dependency and self-harm.

The claims remain contested, but the case illustrates how courts may increasingly be asked to determine where responsibility lies when AI systems cause real-world harm.

The Florida case is not an isolated example, with similar allegations launched against Character.AI late last year.

These cases highlight key issues around product liability. If similar cases continue to proliferate

If similar cases continue to proliferate, AI developers may need to absorb not only higher legal expenses, but also invest much more heavily in safety testing, monitoring, age verification, human oversights, insurance, and risk management before deployment.

These requirements could slow product releases and increase compliance costs, particularly for consumer-facing AI applications.

Privacy

Privacy presents another legal challenge, albeit a less widespread one. Only one (2.7%) privacy-related lawsuit was filed against a U.S. AI developer in the first half of 2026.

Many AI systems rely on vast datasets scraped from websites, social media platforms, forums, and other online sources. Regulators are increasingly asking whether individuals provided meaningful consent for their personal information to become part of AI training datasets.

The most recent example is Amargo Couture's allegation that OpenAI disclosed users' personal identifying information to Meta and Google through embedded tracking technologies without users' consent. Perplexity AI also faced similar allegations in a March lawsuit.

Several European data protection authorities have already investigated AI developers over compliance with the General Data Protection Regulation (GDPR), while questions remain around biometric data, facial recognition, and data retention.

If courts or regulators impose stricter consent requirements for AI training or the handling of user interactions, developers may need to modify data collection practices, limit the use of publicly available information, or invest more heavily in data governance and compliance.

These changes could materially increase operating costs while reducing access to one of the industry's most valuable inputs: data.

Competition concerns & regulatory involvement

Private litigation has been accompanied by increasing scrutiny from competition authorities and policymakers.

As the industry becomes concentrated among a handful of large tech companies, regulators are examining both how AI markets are developing and how the technology should be governed.

Competition authorities are generally not seeking to slow AI innovation itself.

Rather, they are investigating whether dominant technology firms could use their existing market power to strengthen their positions in AI through cloud partnerships, exclusive chip supply arrangements, preferential access to computing infrastructure, or acquisitions of emerging competitors.

The U.S. Federal Trade Commission has examined partnerships between major AI developers and cloud providers, including Microsoft's relationship with OpenAI.

Similarly, the UK Competition and Markets Authority has scrutinized AI foundation model markets and investments involving Microsoft, Amazon and Anthropic.

These investigations may not result in significant financial penalties in the near term, but they could influence the industry's long-term structure by limiting acquisitions, imposing conditions on commercial partnerships, or requiring changes to business practices.

For investors, those outcomes could prove just as significant as traditional antitrust fines by affecting future growth strategies and competitive dynamics.

Alongside competition scrutiny, governments are also establishing new legal frameworks for AI.

The European Union's landmark AI Act introduces comprehensive obligations covering risk management, transparency, technical documentation, human oversight and prohibited AI applications, while regulators in the U.S., UK and Asia are developing their own approaches.

As such, future valuations may increasingly depend not only on technological leadership, but also on companies' ability to navigate litigation, regulation and competition oversight simultaneously.

Conclusion

History offers numerous examples of investors underestimating legal risk.

Tobacco companies spent decades defending product liability lawsuits before the Master Settlement Agreement reshaped the industry's economics.

Likewise, high-growth tech and social media companies that once appeared insulated from regulation later faced antitrust investigations, privacy legislation, and multi-billion-dollar fines. Transformative technologies often encounter their greatest legal challenges only after achieving widespread adoption.

The AI investment story has so far been driven by technological competition, with investors focused on model performance, chips, cloud infrastructure, and monetization.

Those factors will remain critical. However, the next phase of AI investing may be shaped just as much by courtrooms and regulators as by data centers.

Litigation is unlikely to halt AI adoption, just as regulation did not prevent the continued growth of social media, but it could reshape the economics of the industry through higher licensing costs, compliance requirements, and legal obligations.

For investors, the question is no longer simply who will build the most capable AI models. Increasingly, it may be which companies can build the most legally resilient businesses.

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