Anthropic, the San Francisco-originated AI company behind the Claude model, recently faced a class action lawsuit over alleged unauthorised use of books to train its systems. The $1.5 billion settlement, covering approximately $3,000 per book across hundreds of thousands of titles, is now the largest copyright recovery in history and the first major settlement of its kind in the AI-era.

AI firms that feed models with massive, scraped datasets are entering a period of sharp legal scrutiny. This coincides with what economists and tech leaders describe as an emerging ‘AI-driven tech bubble’ wherein valuations are inflating faster than revenues or defensible moats. Prominent tech CEOs, including Sam Altman and Sundar Pichai, have cautioned that investor enthusiasm is becoming irrational. With legal risks escalating in parallel with market exuberance, it remains to be seen how market actors will respond.

Rising tides and training dilemma

Anthropic is not alone in facing AI and copyright-related litigation. Other notable cases include lawsuits against Meta’s LLaMA model for unauthorised use of copyright content, and against Stability AI, where artists and the visual media company, Getty Images, alleged their works were used to train AI art generators without permission.

At the heart of Anthropic’s settlement circulates a fundamental question: Can AI be trained in a fully lawful and ethical manner? Many companies, including Anthropic and Meta, have argued that training AI using copyrighted material constitutes fair use, and the recent US federal ruling in Anthropic’s case partly supports this, finding that the input-side use of legally acquired books to train AI can be transformative and thus protected under fair use. The court, however, drew clear lines: Pirated or illegally sourced material remains unlawful, and fair use does not automatically shield AI outputs that reproduce copyrighted content.

While the decision provides some level of guidance for the industry, it underscores the perils of relying solely on the fair use argument, particularly as other lawsuits against OpenAI, Stability AI and Meta illustrate a growing wave of legal scrutiny. Whilst fair use may shield parts of the training process, it will not immunise companies from output-side liability or sloppy data sourcing. It is therefore not a stretch to predict that litigation in this context could accelerate and become a routine consideration for AI developers.

Legally adjusted training cost

The Anthropic settlement does more than establish a legal precedent; it exposes a new category of financial exposure for Big Tech: AI training-related litigation as a balance-sheet liability. Copyright lawsuits specifically were once perceived as reputational annoyances, but now they are material financial events. A class action of this scale forces companies to provision litigation reserves, alter quarterly reporting, and disclose new risk factors to investors. In effect, AI development would come not only under research and development expense but also under a variety of compliance-intensive activities that could swing earnings, depress valuations, or trigger regulatory scrutiny in capital markets.

AI copyright litigation is indeed, by extension, becoming a fast, measurable financial shock that capital markets can neither ignore nor fully price. First, lawsuits turn hidden training-data practices into clear, quantifiable risks, in Anthropic’s case, a clear $1.5 billion. This becomes a metric that investors can no longer treat as a footnote.

Second, markets quickly translate those risks into cash, meaning that balance sheets must carry contingent liabilities, expand due diligence checklists and tighten funding terms. Third, price discovery follows. Valuations, insurance policies and litigation funding begin to reflect legal exposure the same way they reflect technical debt or compliance shortfalls.

Training-data risks would sit alongside data-privacy penalties and product-liability exposures, forcing companies to disclose new classes of contingent liabilities. As AI-related litigation in this context emerges, and as courts clarify the boundary between fair use and infringement, investors will increasingly price legal exposure into valuations and funding terms.

As AI adoption accelerates across sectors, the inevitability of further class actions is a strategic reality. Companies cannot afford to treat copyright litigation as a downstream inconvenience and thus would benefit from proactive licensing strategies, rigorous dataset audits and comprehensive risk assessments. An example is Microsoft’s approach to training GPT models: The company negotiated licensing agreements with multiple content providers, including OpenAI’s partnership with the US-based Associated Press, the largest American news wire service, explicitly to secure legal certainty over journalistic material. This action illustrates how foresight in licensing and governance can materially reduce risk exposure while enabling AI innovation at scale.

Regulating with benefit of hindsight

For emerging markets such as Nepal, where AI adoption is accelerating, the implications are uniquely significant. AI adoption is rising, be it in fintech, digital payments, education platforms or government digitalisation, but the legal infrastructure regulating data provenance, model training and algorithmic accountability remains comparatively underdeveloped. This could create a structural paradox whereby Nepal increases its import of advanced AI systems from jurisdictions with sophisticated legal battles while operating with domestic frameworks that have not yet internalised those global risks. As a result, companies may unknowingly onboard technologies trained on datasets carrying unresolved copyright liabilities or embedded governance risks.

The almost inherent benefit, however, of emerging markets is that they can leapfrog by designing governance frameworks with the benefit of hindsight. In this case, it would look like anticipating risk and building policies shaped by real litigation experienced elsewhere. Saudi Arabia illustrates this opportunity. By investing billions in sovereign cloud capacity and, most interestingly, developing early data-governance rules, Saudi Arabia is rapidly demonstrating how an emerging market can take a proactive rather than reactive stance, whilst setting standards rather than inheriting risks. It appears that, undoubtedly, in an era where AI training is as legally fraught as it is technologically ambitious, success depends not only on computational sophistication but on the strategic integration of law, ethics and risk management into the very architecture of innovation.