Artificial intelligence companies are built to scale fast, and legal risk scales right along with them. A single risky design choice, made once and rolled out to a million users, can turn into a million identical fact patterns sitting out there, waiting for a plaintiffs’ firm to notice and exploit. The underlying issue doesn’t have to change for the exposure to explode; only the denominator does.

For companies in hypergrowth, this risk tends to show up at the worst possible time. Teams are heads-down on shipping features and hitting user milestones and have usually just completed a successful fundraising round (that they’ve publicly announced) when the risky design choice early on materializes into litigation exposure. At this point, the legal architecture behind the product, i.e., terms of service, arbitration provisions, consent flows, needs to work for the company to try to minimize the exposure. But this architecture often gets the least attention during the early build phase of the company, and so it often has not scaled with the company’s hypergrowth. With the right groundwork, hypergrowth companies can scale their legal architecture with their business.

The New Math of Consumer Scale

Consumer AI is hitting that denominator faster than any prior generation of software ever did, which is what makes this moment different from earlier tech cycles. Some of the category’s largest platforms have surpassed 900 million monthly users within a few years of launch, and even narrower companion apps see heavy users logging well over an hour inside the app every day. This adoption curve took prior consumer software categories the better part of a decade to reach, per one industry analysis.

For over two decades, the standard defense against this kind of exposure was an arbitration clause buried in the terms of service, and it largely worked, until plaintiffs’ firms figured out how to use arbitration’s own efficiency against the companies that required it. Instead of one class action, a company today can face thousands of individual arbitration demands filed the same day, each one triggering its own filing fee under the arbitration provider’s rules. Providers have processed hundreds of thousands of these individual claims in a single year. The clause meant to keep a company out of court can instead produce a bill for millions of dollars in fees before a single case is decided on the merits. That first generation of arbitration clauses were simply never built for this volume, and plaintiffs’ firms know it.

Three trends are converging to put this on the radar for consumer AI companies in particular. These products reach massive scale faster than prior software categories, with dedicated companion apps alone adding tens of millions of downloads within a single year of launch, per the same analysis. The plaintiffs’ bar has professionalized the mass arbitration playbook, with firms and even industry conferences built entirely around identifying and filing these campaigns. And regulators and legislatures are actively writing new AI-specific disclosure and consent requirements right now, handing plaintiffs’ firms fresh, very specific statutory hooks to work with. Companies should start thinking about this today, or face risks that increase with every new user.

How an Isolated Issue Becomes a Coordinated Claim

Not every consumer complaint scales, but the ones that do often involve uniformity. Anything a company does identically to every user creates identical fact patterns, and identical fact patterns are what plaintiffs’ firms are built to find and file at scale. With things like a standardized onboarding flow, a single terms-of-service clause, an algorithmic pricing or eligibility decision applied system-wide, an advertising promotion, or a fee quietly added to every invoice, if one user has a claim, every user probably has it too. Data incidents work the same way, where a single breach defines the class the moment it happens.

This dynamic shows up across four areas that consumer AI companies should watch closely:

• Product Representations: Marketing claims like “clinically proven,” “we never sell your data,” or similar are made identically to every user who sees that ad or landing page, so the claim is either true for everyone or false for everyone.
• Subscriptions: Failures cluster around unclear enrollment consent, a cancellation flow that’s harder than sign-up, and renewal notices that don’t reach the user. Regulators have been building enforcement actions around exactly these three failure points, and the underlying economics make the exposure larger than it might first appear. Consumer AI apps post annual retention of just 21.1%, versus 30.7% for non-AI apps, and refund rates that run roughly 20% higher (4.2% vs. 3.5%). In other words, a meaningfully larger share of the user base is actively trying to exit a subscription at any given moment, right at the friction points described above, per one 2026 app-monetization report.
• Privacy and Data Practices: The core issues are consent and secondary use. Was data shared with an ad network, or used to train a model, without the user actually agreeing to it?
• AI Functionality: This adds a new layer of risk: Was the user told they were talking to a bot, can the company be held to account when an AI output is wrong and someone relied on it, and is there bias baked into an automated decision? Because the AI feature is deployed identically to every user, any one of these defects is class-wide.

Coordinated claims often start with a pattern. A spike in near-identical complaints to customer support or in-app store reviews, often using suspiciously similar language, is usually a sign a plaintiffs’ firm has already put out intake instructions. The biggest accelerant today is social media, where a single viral post about a promotion, fee, feature, or policy change can generate thousands of claimants within days, because plaintiffs’ firms actively monitor and advertise for exactly these moments. Sometimes companies will receive an attorney demand letter referencing one specific practice, or a regulatory inquiry hitting a competitor doing the same thing. The lesson for legal teams is to act proactively, rather than waiting for the demand letter.

Designing for Scale: What Legal Teams Should Do Now

Despite these risks, companies don’t need to entirely abandon their arbitration clauses. Arbitration providers have already updated their own rules to deal with mass filings, and companies should similarly incorporate provisions relating to pre- and post-filing procedures to create a more manageable process. Courts have even started upholding these structural provisions when they’re drafted properly.

User assent and the contracting experience around it are also important. If a court finds that a user never meaningfully agreed to an arbitration provision, because it was buried or the sign-up flow didn’t clearly present it, the entire protection disappears before the dispute is ever reached on the merits. For an AI product where sign-up might happen through a chat interface, a mobile app, or an API integration rather than a traditional checkout page, companies need to be deliberate about how and where that assent is captured, and be able to prove it later. This is an area where product and legal teams should be equally involved in the design process.

As a user base grows from thousands to millions, dispute resolution procedures have to grow with it. Provisions that work fine for a modest user base can become the very mechanism that creates unmanageable exposure at scale. Legal teams should consider building fee allocation and consolidation mechanics for volume, including informal resolution or pre-arbitration notice-and-cure steps that filter out claims before they’re ever filed, and updating agreements to reflect current provider rules rather than language drafted for a much smaller company years ago. Scale is also a legal architecture problem, and the solution should keep pace with growth instead of trailing behind it. That’s especially true given how concentrated revenue already is in this category. The top 10% of consumer AI apps capture nearly 95% of all subscription revenue, per the same report, which means the handful of companies operating at this scale are also the ones carrying most of the exposure.

For legal teams looking to get ahead of this before a mass arbitration threat materializes, a few concrete steps stand out:

• Audit every standardized, high-volume touchpoint, including sign-up, pricing, cancellation, AI disclosures, and data consent, and stress-test each one as if it already affected the entire user base, because eventually it will.
• Update arbitration and terms-of-service language to reflect current provider rules on procedures and fees.
• Put a monitoring process in place for complaint spikes and demand-letter patterns, with legal, product, and customer support sharing what each is seeing.
• Treat any new AI feature the way a new product launch would be treated from a legal standpoint by reviewing it before it ships to a million users.

What This Means for Founders and VCs

Everything above is written from the legal team’s side of the table. But by the time a mass arbitration campaign shows up, it’s already touched the cap table, either because a lawsuit affects a valuation, or because the underlying flaw should have been caught months earlier in diligence. That puts two groups closer to this than they might think.

For Founders

Founders often treat the terms of service and arbitration clause as something outside counsel handles once, at incorporation, and nobody looks at again. But for a fast-moving consumer AI product, that approach may not work. Typically, the onboarding flow, the cancellation flow, and whatever disclosure covers the AI feature get written once and then run, unchanged, in front of every new user, so an early flaw doesn’t stay small — it just waits. To help address this, consider getting consent flows reviewed before they ship instead of after a complaint pattern forces the issue. Also consider revisiting the arbitration language itself every time the user base takes a real step up, not just when a financing round happens to make it convenient. It’s worth building a simple habit of watching for complaint spikes in support tickets, app reviews, and social media, since that’s usually where the pattern shows up first. Founders who can speak clearly about all of this when investors are doing due diligence may tend to find it works in their favor.

For VCs

For investors, the same dynamic cuts the other way. Fast, uncapped growth makes a consumer AI investment attractive, but it also determines how much exposure sits underneath it. A standardized onboarding flow or subscription trick that would generate a handful of complaints at 10,000 users becomes a fully formed class the moment the company crosses a much bigger threshold. It’s worth asking directly in diligence whether the standard onboarding, subscription, privacy, and AI-disclosure flows have been reviewed, and whether the arbitration language has been touched since the company raised its seed round, rather than left as boilerplate written for a much smaller business. Investors should also take note of a spike in near-identical complaints or reviews at a portfolio company and remember that the most concentrated exposure tends to sit with the biggest winners in the portfolio. The same scale that drives returns is what turns one flawed flow into a class-wide claim.

The Bottom Line

Consumer AI companies are built to scale, but scale cuts both ways. The same design choices that let a product reach a million users overnight can turn one flawed disclosure, subscription flow, or AI feature into a million identical legal claims just as fast. Companies looking to come out ahead should consider building their arbitration provisions, terms of service, and internal monitoring to scale with the product, instead of retrofitting them after a mass claim campaign has already started.