Looking Ahead#
Predicting legal developments is hazardous. Courts are unpredictable, legislation is contingent on politics, and technology evolves in unexpected ways. But certain trends are visible enough that we can make informed projections about where AI liability is heading.
Here are ten predictions for AI liability in 2026, ranging from the nearly certain to the more speculative.
1. The First Major AI Malpractice Verdict#
Confidence: Very High
By the end of 2026, at least one case will produce a significant verdict (seven figures or more) specifically based on professional negligence in AI deployment. The most likely domains are healthcare (AI-assisted misdiagnosis) or legal services (AI-generated erroneous advice).
Why this matters: A headline verdict will catalyze insurance market responses, regulatory attention, and professional standard development. The specific facts will become the reference case that everyone discusses, whether or not they’re representative.
Watch for: Cases currently in discovery that involve clear AI failures with documented patient or client harm. The procedural timeline suggests trials in late 2025 or 2026 for cases filed in 2023-2024.
2. Section 230 Will Not Protect AI-Generated Content#
Confidence: High
Multiple courts will hold that Section 230 immunizes platforms for hosting and moderating user content but does not protect AI companies for content their systems generate. The distinction between distribution and creation will prove decisive.
The reasoning: Section 230 says platforms aren’t treated as publishers of “information provided by another information content provider.” When ChatGPT generates a response, there is no “another” provider, the AI company itself is creating the content. Courts will find this distinction dispositive.
Implications: AI companies will face defamation, product liability, and other claims without Section 230 as a defense. This will accelerate content moderation in AI outputs and may reshape business models.
3. Class Certification in AI Employment Discrimination Cases#
Confidence: High
At least one court will certify a class action challenging algorithmic hiring discrimination. The argument: when everyone screened by the same AI system faces the same alleged discrimination, common questions predominate over individual ones.
Key case: Mobley v. Workday or similar cases challenging AI hiring vendors. If the court finds that algorithmic discrimination can be proven through common evidence about the system itself rather than individual hiring decisions, class treatment becomes viable.
Impact: Class certification transforms litigation economics. Individual AI discrimination claims may not justify the expense of litigating against well-funded defendants. Class actions change that calculus dramatically.
4. State AI Legislation Will Proliferate#
Confidence: Very High
In the absence of federal action, states will continue enacting AI-specific legislation. By end of 2026, expect:
- 10+ states with AI hiring/employment laws
- 5+ states with AI disclosure requirements for consumer interactions
- 3+ states with healthcare-specific AI rules
- At least one major state with comprehensive AI legislation
The patchwork problem: This state-level activity will create compliance challenges for national companies. Conflicting requirements will drive calls for federal preemption, which may or may not come.
Watch: California, New York, Illinois, Colorado, and Texas as bellwether states.
5. Professional Boards Will Issue AI Practice Standards#
Confidence: High
State medical boards, bar associations, and engineering licensure boards will issue formal guidance on AI use in professional practice. These standards will:
- Define minimum oversight requirements
- Establish documentation obligations
- Specify disclosure duties to clients/patients
- Create safe harbors for compliant use
Why this matters: Professional standards become evidence of the standard of care in malpractice litigation. Once a board says “you must do X when using AI,” failure to do X becomes prima facie evidence of negligence.
First movers: California State Bar (already working on this), AMA, state medical boards in major jurisdictions.
6. AI Insurance Products Will Emerge:But Remain Expensive#
Confidence: High
Insurers will develop AI-specific coverage products, including:
- AI errors and omissions endorsements
- AI-specific professional liability policies
- AI product liability coverage for developers
But these products will be expensive (premiums significantly above traditional coverage) and restrictive (extensive exclusions, low sublimits, strict conditions).
The gap: Demand for AI coverage will exceed supply through 2026. Many professionals will remain underinsured for AI-related risks, either through coverage gaps or unaffordable premiums.
7. The Copyright Cases Will Not Resolve#
Confidence: Very High
Despite extensive litigation, the fundamental questions of AI copyright, whether training on copyrighted works is fair use, who owns AI-generated outputs, will not be definitively resolved in 2026.
Why the delay: The major generative AI copyright cases are complex, with extensive discovery, multiple parties, and difficult factual questions. District court decisions in 2025-2026 will be appealed. Circuit court decisions may conflict. Supreme Court review, if it comes, is years away.
The uncertainty tax: This ongoing uncertainty will function as a tax on AI development. Companies will operate under legal risk, either licensing conservatively, building litigation reserves, or accepting exposure.
8. At Least One AI Company Will Face Existential Litigation#
Confidence: Medium-High
One significant AI company will face litigation threatening its survival, either through a catastrophic verdict, regulatory action, or copyright/IP judgment that undermines its business model.
Candidates: Mid-sized AI companies without the resources of OpenAI or Google are most vulnerable. Companies whose training data provenance is questionable face particular exposure.
Implications: An existential threat to an AI company will ripple through the ecosystem, affecting investors, competitors, customers, and the broader narrative about AI risk.
9. Regulatory AI Will Face Constitutional Challenges#
Confidence: Medium
Government use of AI in benefits determinations, sentencing, and regulatory enforcement will face due process and equal protection challenges. At least one such challenge will succeed at the appellate level.
The arguments: Procedural due process requires notice and an opportunity to respond. When AI systems make consequential government decisions, affected individuals may have no meaningful opportunity to contest the AI’s reasoning. Courts may require explanation, override mechanisms, or human review.
Watch: Social Security disability determinations, SNAP/TANF eligibility screening, pretrial risk assessment tools, and immigration case processing all use AI in ways that may trigger constitutional scrutiny.
10. AI Liability Will Become a Political Issue#
Confidence: High
AI liability, who’s responsible when AI causes harm, will emerge as a political issue in the 2026 election cycle. Expect:
- Campaign rhetoric about protecting consumers from “Big AI”
- Counter-arguments about not stifling American innovation
- Congressional hearings on AI harms and accountability
- Proposed legislation that may or may not pass
The politics: AI liability doesn’t map cleanly onto partisan lines. Consumer protection and corporate accountability themes appeal to progressives; anti-regulation and innovation themes appeal to conservatives. Tech industry political engagement will intensify.
Bonus Prediction: The Unexpected Case#
Confidence: Certain
At least one AI liability development in 2026 will be completely unexpected, a novel fact pattern, an unusual legal theory, a surprising judicial ruling, or a technological development that creates new liability exposure no one anticipated.
Why this matters: AI liability is evolving faster than analysis can keep up. Prudent practitioners build flexibility into their compliance and risk management approaches. The case that reshapes the field may not be on anyone’s radar yet.
What These Predictions Mean for Practice#
If these predictions prove accurate, professionals should prepare for:
Increased Compliance Burden#
State legislation, professional board standards, and insurance requirements will compound. AI governance that seemed optional in 2024-2025 will become mandatory in 2026.
Higher Liability Exposure#
As Section 230 protection narrows, class certification becomes available, and standards crystallize, AI-related liability will become more likely and more expensive.
Documentation Requirements#
Every prediction implies increased documentation needs. AI selection, validation, oversight, and review processes should be recorded contemporaneously. The paper trail will matter in litigation and regulatory inquiries.
Insurance Attention#
Review coverage annually. New products and requirements will emerge. Don’t assume 2025 coverage adequately addresses 2026 risks.
Political Awareness#
Monitor legislative and regulatory developments. The political salience of AI means rules may change quickly and unpredictably.
The Bigger Picture#
These ten predictions describe a maturing liability landscape. AI is transitioning from a novel technology with unclear legal treatment to a mainstream capability with defined responsibilities. The uncertainty that characterizes 2025 will begin to resolve, not completely, but meaningfully.
This maturation is neither good nor bad; it’s inevitable. Technologies that become important enough attract legal attention. The automobile, the computer, the internet, and now AI all follow this path.
Practitioners who understand where this trajectory leads can position themselves advantageously. Those who assume 2025 conditions will persist will be caught off guard when the predictions materialize.
The question isn’t whether AI liability law will develop. It’s whether you’ll be ready when it does.
A Note on Confidence#
These predictions carry different confidence levels because predicting the future is inherently uncertain. High-confidence predictions reflect trends already visible in current cases, legislation, and regulatory activity. Medium-confidence predictions involve more contingency, they depend on specific cases breaking certain ways or political conditions aligning.
Readers should weight accordingly. But even low-confidence predictions are worth considering because they identify the kinds of developments that could reshape the field if they occur.
Check back in January 2027. We’ll score these predictions and explain what we got right, what we got wrong, and what we learned from both.