On 13 August, the SEC cancelled the open meeting scheduled for the following morning, at which its three commissioners were to decide whether to publish proposed rules creating a tailored offering regime for certain investment contracts involving crypto assets.¹ The formal notice gave no reason and no replacement date. What disappeared was not a final rule, but the vote needed to expose a proposal to public scrutiny. Everything known about the proposal’s substantive content comes from what Chair Paul Atkins has previewed, not from a published release, and I keep that distinction in view throughout.

A framework with six-year roots

The framework traces to the Token Safe Harbor Proposal that Commissioner Hester Peirce first floated in 2020 and revised in 2021, which would have given a token network a grace period to decentralise before securities rules applied.² Peirce now chairs the SEC’s Crypto Task Force. Atkins’s preview develops her earlier idea. It rests on the interpretation the SEC and CFTC issued on 17 March 2026, which set out a five-part crypto-asset taxonomy and explained that four categories ordinarily comprise non-security crypto assets, while separately addressing digital securities and investment contracts.³ The rulemaking remains under review at the White House Office of Information and Regulatory Affairs as RIN 3235-AN38. Its continued appearance there as a pending proposed rule suggests postponement rather than withdrawal, although the SEC has announced no new date.

The framework previewed by Atkins contemplated three possible routes. A startup exemption would last up to four years, with an illustrative $5 million aggregate cap and whitepaper-style disclosure in place of a registration statement. A fundraising exemption would carry an illustrative $75 million cap in any 12-month period, supported by principles-based disclosure, a discussion of financial condition and financial statements. An investment-contract safe harbour could apply once the issuer had completed or permanently ceased the essential managerial efforts represented or promised under the investment contract. The figures are illustrative because Atkins offered them as examples. The proposing release that would fix them was never published.

Where the remedy stops

The routes carry a cost that the caps obscure. Section 11 of the Securities Act gives a person acquiring a security issued under a registration statement an express claim for a material misstatement or omission in that statement, without requiring proof of scienter.⁴ An exempt offering carries no such claim. Federal antifraud provisions continue to apply, and other federal or state-law remedies might remain available depending on the facts, but none replicates the no-fault registration-statement claim that anchors investor protection in the registered market. Senators Warren and Van Hollen warned in April that the SEC’s direction risked exemptions that undermine decades of protection. Separately, former SEC Chief Accountant Lynn Turner described the CLARITY Act’s parallel statutory exemption framework as “severely deficient”.⁵ While the vote remains postponed, token issuers have the March interpretation but no tailored offering regime, leaving investors with neither the protections contemplated in Atkins’s preview nor a comprehensive statutory framework.

The European contrast

Europe addressed the same subject through legislation. MiCA, Regulation (EU) 2023/1114, has applied in full since 30 December 2024 and requires many offerors of crypto-assets, and persons seeking their admission to trading, to publish a crypto-asset white paper whose defective content can found civil liability under Article 15.⁶ That liability is not the equivalent of Section 11. A holder must establish that the information was not complete, fair or clear, or was misleading; that reliance affected the decision to acquire, sell or exchange the asset; and that loss resulted. Even so, the obligation sits in binding EU legislation. Changing it would require further legislative action. The US is trying to reach a comparable destination by rulemaking because Congress has not passed a statute, and that route is exposed in two ways. Peirce is due to leave in November for Regent University, which would reduce the Commission to two serving members unless another appointment is made.⁷ Any eventual rule would also face a post-Loper Bright court applying its own judgment to disputed questions of statutory authority, without Chevron deference. Neither point prevents rulemaking. Both show how much of the framework rests on administrative rather than legislative durability, since a later Commission could amend or rescind it through further rulemaking.

The near-term calendar

The weeks ahead will show which way the executive branch leans. The CFTC convenes its first Innovation Advisory Committee on 20 August, under the banner “Crypto’s Regulatory Evolution: From Uncertainty to Clarity”, a commodities regulator occupying ground a securities regulator has just stepped back from.⁸ The Digital Asset Market Clarity Act, meanwhile, is reported to face a cloture test on 15 September. Senator Lummis has warned that failure this year could push comprehensive federal legislation into the next Congress.⁹ For issuers and compliance teams the planning assumption should shift. Do not assume publication on the original timetable, watch the status of RIN 3235-AN38 on Reginfo.gov for the first firm signal and price in a securities framework that, if it arrives, can be revised by a later Commission. A statute-shaped problem is being met with an administrative instrument, and that mismatch is where the exposure lies.

Notes

1. SEC, notice of cancellation of the open meeting of 14 August 2026 (sec.gov); scheduling explanation reported by Reuters, 13 August 2026.

2. SEC Commissioner Hester M. Peirce, Token Safe Harbor Proposal (2020); revised proposal (2021).

3. SEC Press Release 2026-30, “SEC Clarifies the Application of Federal Securities Laws to Crypto Assets”, 17 March 2026 (joint SEC and CFTC interpretation).

4. Securities Act of 1933, s.11 (15 U.S.C. s.77k).

5. Senators Elizabeth Warren and Chris Van Hollen, letter to SEC Chair Paul Atkins, 27 April 2026 (US Senate Committee on Banking, Housing, and Urban Affairs, minority), written response requested by 8 May 2026. Lynn Turner, former SEC Chief Accountant, letter to the Senate Banking and Agriculture Committees, 13 January 2026, describing the CLARITY Act framework as “severely deficient”.

6. Regulation (EU) 2023/1114 (MiCA), Articles 4–15; applicable in full from 30 December 2024.

7. Regent University School of Law faculty announcement (Peirce to join November 2026); Bloomberg, 21 May 2026.

8. CFTC Press Release 9279-26; Federal Register notice 2026-16328 (11 August 2026).

9. Majority Leader John Thune filed cloture on the motion to proceed to the Digital Asset Market Clarity Act (H.R. 3633), first procedural vote set for 15 September 2026 (The Block; American Banker, 8 August 2026). Senator Lummis’s timing warning as reported (Tech Times, August 2026).

At 4:52 a.m. Eastern on Saturday 8 August 2026, at the close of an overnight session, Senate Majority Leader John Thune filed cloture on the motion to proceed to H.R. 3633, the Digital Asset Market Clarity Act.¹ The Senate then left for its summer recess. The cloture vote is set for 2:15 p.m. Eastern on Tuesday 15 September, the day after the chamber returns.² That vote will not pass the Act or even place the Senate formally on the bill. It will invoke cloture on the motion to proceed, limiting further debate on the preliminary question whether the chamber should take the legislation up.

That a statute of this consequence should turn on so preliminary a question captures the state of American digital-asset regulation. In eighteen months the United States has assembled most of a market-structure regime. One part is already law. The larger part has passed the House, cleared two Senate committees and been merged into a single text. It now waits on sixty votes to begin a debate.

Two statutes and a hierarchy

The first piece became law in July 2025. On 18 July the President signed the GENIUS Act, the first comprehensive federal statute devoted to a cryptoasset product, which established a licensing and reserve regime for payment stablecoins.³ The Senate had passed it 68 to 30 in June, the House 308 to 122 in July.⁴ Its provisions are presently scheduled to take effect on 18 January 2027, unless final implementing regulations trigger the alternative commencement date of 120 days after their issuance.⁵

GENIUS is narrow by design. A permitted payment stablecoin must be backed one for one by cash or short-dated government assets, and only a subsidiary of an insured depository institution, a federally qualified nonbank issuer or a state-qualified issuer may issue one, with state supervision confined to issuers below ten billion dollars in circulation. Issuers must publish their reserves monthly and disclose a redemption policy.⁶ The statute governs a single instrument, and governs it tightly.

The CLARITY Act is the wider project. The House passed it on 17 July 2025 by 294 to 134, with more than seventy Democrats joining the Republican majority, the broadest cross-party margin any digital-asset bill has drawn in a chamber of Congress.⁷ Where GENIUS governs one instrument, CLARITY attempts to allocate the principal categories of digital asset between federal regimes. It sets out a three-part statutory taxonomy. Digital commodities fall to the Commodity Futures Trading Commission. Assets sold pursuant to an investment contract fall to the Securities and Exchange Commission, though the operative distinction is between the asset and the transaction in which it is sold rather than a fixed property of the token. Permitted payment stablecoins sit in a third category, governed by GENIUS and outside both the securities and the commodity regimes.⁸

The analysis distinguishes where necessary between the House-passed text, the Senate Banking substitute and the proposed merged Senate substitute released in July. The three pursue the same jurisdictional settlement, but their definitions and transition mechanisms are not identical, and the version senators would consider is the proposed merged substitute rather than the bill the House passed.⁹

The taxonomy is the point of the exercise. For most of a decade the classification of a token turned on the Howey test, the investment-contract standard the Supreme Court devised in 1946 for orange groves and citrus-service contracts, applied to crypto case by case through enforcement.¹⁰

Regulation by enforcement

To understand why an industry lobbied for its own regulation, look at the decade that preceded it. Without a statute, classification was worked out in court. In SEC v. Ripple Labs Judge Analisa Torres held in July 2023 that institutional sales of the XRP token were unregistered securities offerings, while programmatic sales to anonymous buyers on exchanges were not, a distinction that turned on what each class of purchaser could have expected rather than on any property of the token itself.¹¹ The ruling satisfied few and bound no other court. Parallel actions against the major exchanges produced further fragments of doctrine, none of them a system.

The ground then shifted beneath the agencies. In Loper Bright Enterprises v. Raimondo the Supreme Court ended mandatory Chevron deference to an agency’s reasonable construction of an ambiguous statute.¹² The decision did not displace Howey, and courts considering SEC crypto cases were already required to decide independently whether the facts satisfied the investment-contract test. Its broader significance is nevertheless structural. An agency interpretation cannot supply the durable jurisdictional settlement that Congress has declined to enact.

The decentralisation pivot

The line between the two contested categories is decentralisation. In the House-passed text a digital commodity is an asset whose value is intrinsically linked to the use of its blockchain, excluding securities, derivatives and stablecoins.¹³ The Senate Banking substitute approaches the securities side through a different construct, the ancillary asset, a token sold with or as part of an investment contract but carrying its own disclosure, disposition and certification machinery rather than a simple relabelling of the House category.¹⁴ The regulatory treatment of transactions involving the same token can change over its life.

The mechanism for that movement, in the House-passed text, is the mature blockchain system. A network qualifies as mature if it is functional, composed of open-source code, governed by pre-established and transparent rules and not subject to the control of any single person or group, including through the holding of twenty per cent or more of the tokens.¹⁵ A token initially sold in a transaction constituting an investment contract may later trade as a digital commodity once the associated network satisfies the applicable decentralisation or maturity test.

The proposed Senate substitute uses a different certification mechanism. It begins with a rebuttable presumption that a network token is an ancillary asset. The token’s originator (or, subject to additional due-diligence conditions, a digital-asset intermediary) may submit evidence certifying that the token is not an ancillary asset. The certification becomes effective if the SEC does not object within sixty days, although the Commission may deny it on finding that the token remains an ancillary asset or carries a disqualifying financial right.¹⁶ The Senate mechanism therefore does not ask simply whether a network is mature. It asks whether the asset remains dependent on the entrepreneurial or managerial efforts that justify ancillary-asset treatment. The burden of initiative still moves towards the promoter or intermediary, but the proposition asserted and the statutory presumption differ materially from those in the House bill.

What the Act would build

Around the taxonomy the Act constructs a market-conduct regime. The proposed substitute creates a notice-of-intent-to-register regime for digital commodity exchanges, brokers and dealers while the CFTC completes its permanent rules. Qualifying firms would be treated as registered for specified purposes while complying with customer-asset, disclosure, financial-responsibility and supervisory requirements. Existing listings could continue until 180 days after the permanent registration rules take effect, subject to the Act’s conditions and delisting machinery.¹⁷

The grant to the CFTC is more novel than it appears. The Commission has long policed manipulation and fraud in commodity spot markets, but it has never held comprehensive registration and supervisory authority over a spot market as the SEC holds it over securities. CLARITY would give it that authority over digital commodity trading for the first time, and would have to fund the supervisory apparatus the role demands.¹⁸ On the securities side, issuers of ancillary assets would make tailored disclosures to the SEC rather than file full registration statements, a lighter regime calibrated to tokens rather than equities. The Act does not abolish securities regulation of crypto. It rescales it.

The text also draws a line around software itself. Its decentralised-finance provisions, carried in part by the Blockchain Regulatory Certainty Act, shield non-custodial software developers from money-transmitter obligations and Bank Secrecy Act requirements, and a separate exclusion exempts validators and open-source publishers from registration.¹⁹ The distinction is between those who hold customer assets and those who write or run code. The first are regulated intermediaries. The second, on the Act’s logic, are not intermediaries at all. That line will bear a great deal of weight, because much of what the market calls decentralised finance sits close to it.

The regulators moved first

While the bill has waited, the agencies have not. On 11 March 2026 SEC Chairman Paul Atkins and CFTC Chairman Michael Selig signed a memorandum of understanding to coordinate on matters of shared concern, the product of a joint initiative the two chairs called Project Crypto.²⁰ The memorandum created a Joint Harmonization Initiative across six workstreams, spanning product definitions, clearing and margin, dual-registration friction, a fit-for-purpose framework for crypto assets, regulatory reporting and cross-market surveillance and enforcement.²¹

Six days later, on 17 March, the two agencies issued a sixty-eight-page joint interpretation of how the federal securities laws apply to certain crypto assets and transactions, announced at the DC Blockchain Summit. It set out a five-part classification of digital commodities, digital collectibles, digital tools, stablecoins and digital securities, and identified sixteen major assets as digital commodities on the basis of their characteristics at the date of the release, among them Bitcoin, Ether, Solana and XRP. The CFTC stated that it would administer the Commodity Exchange Act consistently with the interpretation.²² The regulators have begun to draw the line the legislature has not yet enacted.

The durability of that administrative settlement is the open question. The interpretation does not bind a court and, after Loper Bright, cannot claim mandatory Chevron deference.²³ What the agencies have built by agreement, a later administration can unbuild by agreement. Only a statute can make the basic allocation materially less dependent on the personnel leading the two commissions. That is the argument for passing CLARITY even now that the regulators have acted, and it is the argument its supporters have found hardest to carry against a crowded Senate calendar.

What changes on enactment

For practitioners the practical effect is a change in default posture. A token originator today operates against the risk that the SEC will characterise transactions involving its token as investment-contract transactions after the fact, a risk priced into every launch and listing. Under the proposed substitute, an originator, or in specified circumstances a digital-asset intermediary, can seek to displace the presumption that a network token is an ancillary asset by certifying that the statutory criteria are no longer satisfied. The SEC must object within the statutory period or the certification becomes effective. Exchanges gain a notice-of-intent-to-register status under which they can operate lawfully while the CFTC completes its rules, rather than trading in the shadow of an enforcement theory. Custody, customer-asset segregation and disclosure move from a patchwork of state licensing requirements and agency positions towards a more coherent federal market-conduct regime.

The regime changes the character of legal risk rather than eliminating it. The contested question becomes whether a network token remains an ancillary asset, including whether continuing entrepreneurial or managerial efforts support that treatment, and the certification that answers it is an assertion the issuer must be prepared to defend. An unsuccessful or inaccurate certification provides no safe harbour. The originator or intermediary must defend it through the SEC process on the statutory timetable. A denial constitutes final agency action subject to judicial review. The Act replaces the diffuse uncertainty of classification by enforcement with the sharper, and more manageable, uncertainty of a defined test applied to particular facts.

The politics of a motion to proceed

The Act’s difficulty is not conceptual. It is political, and it is concentrated in provisions that have little to do with token categorisation. There is a paradox in the timing. The March interpretation, by giving the market much of the certainty it sought, has drained some of the urgency from the legislative effort, and an industry that has what it needs administratively lobbies less hard for the statute that would make it permanent. The Senate Agriculture Committee advanced its portion in January 2026, and the Senate Banking Committee reported its version by 15 to 9 on 14 May.²⁴ On 22 July, Senate Republicans released a proposed merged substitute running to 616 pages, joining the Banking market-structure framework to the Agriculture commodity provisions and adding new titles on government ethics and law enforcement.²⁵

The ethics title is the obstacle. Senate Democrats have conditioned their support on a conflict-of-interest rule restraining officials, the President included, from personal crypto interests. The Senate Banking Committee’s Democratic minority staff calculates, from the President’s 2025 financial disclosure, that his crypto ventures generated more than 1.4 billion dollars in income that year.²⁶ The merged text answered with a provision that bars officials from issuing or sponsoring digital assets but permits them to invest, and that sunsets in 2029, so that it expires with the current presidential term. Seven of the Senate’s most pro-crypto Democrats rejected it in a joint statement.²⁷ A second front concerns deposit flight. Banks have warned that stablecoin reward programmes could draw potentially substantial deposits out of the banking system, and at least one Republican senator has withheld support until the point is addressed.²⁸

The arithmetic follows from the shape of the chamber. Cloture requires sixty votes. Republicans hold fifty-three seats. If every Republican votes to proceed, at least seven Democrats or independents must join, and the three unresolved questions of ethics, illicit finance and the folding-in of the Agriculture text all remain open going into the vote.²⁹ Galaxy Research, an industry analyst, has cut its estimate of the odds that the Act becomes law this year from fifty to thirty per cent, a projection rather than a settled fact.³⁰ The calendar is unforgiving. The Senate returns on 14 September, and attention turns quickly to the November midterms.

The view from London and Brussels

The contrast with Europe and the United Kingdom sharpens the design question. The European Union built its market structure, the Markets in Crypto-Assets Regulation, as a single passportable regime under which one authorisation lets a firm serve all twenty-seven member states, and the final EU-wide transitional period expired on 1 July 2026, though individual member states could shorten or decline the grandfathering they offered.³¹ The United Kingdom has taken a third path. Parliament made the Financial Services and Markets Act 2000 (Cryptoassets) Regulations 2026 on 4 February 2026, the Financial Conduct Authority gateway opens on 30 September 2026 and closes on 28 February 2027 ahead of a mandatory regime expected to begin on 25 October 2027, and the Bank of England took oversight of systemic sterling stablecoins on a standard backing of up to seventy per cent short-dated government debt and at least thirty per cent unremunerated central-bank deposits, with a systemic-at-launch step-up under which a new issuer may initially hold up to ninety-five per cent in eligible government debt.³²

Each jurisdiction has answered the same question differently. Europe created one rulebook and one licence. Britain divided the work between a conduct regulator and a financial-stability regulator within a single statutory perimeter. The United States has split authority between two established agencies with a long history of rivalry, joined by a decision tree. The American design keeps the market expertise and the enforcement records of the SEC and the CFTC. It also imports the boundary disputes a single regime avoids by definition.

The classification problem, relocated

What CLARITY resolves is the turf question. After enactment there would be a statutory answer to which agency supervises which asset, and a defined path by which an asset passes from one to the other. That is more than a decade of enforcement produced, and it should not be understated.

What the Act does not resolve is the classification problem itself. Howey asked whether the buyers of an instrument were led to expect profits from the efforts of others, and left the answer to be found in the facts. The House bill asks whether a blockchain has become sufficiently mature and decentralised. The proposed Senate substitute asks a related but different question, whether a network token remains an ancillary asset because its value still depends on an originator’s entrepreneurial or managerial efforts. In both versions the proponent may initiate a certification process, but the presumption, the evidence required and the status being certified are not the same. Whether either certification model proves more determinate than Howey, or merely relocates its factual ambiguity, is the question the legislation leaves open. The experiment cannot begin until the Senate decides, on 15 September, whether to take the Act up at all.

Notes

1. Senate Daily Press floor log, Friday 7 August 2026 (recording the 4:52 a.m. cloture filing on the motion to proceed to H.R. 3633), dailypress.senate.gov; reported in Zack Abrams, “Majority Leader Thune files cloture on Clarity Act, setting up Sept. 15 Senate vote,” The Block, 8 August 2026. Available at: The Block, 8 Aug 2026; Senate Daily Press.

2. Cloture vote on the motion to proceed scheduled for 2:15 p.m. Eastern, Tuesday 15 September 2026, the day after the Senate reconvenes on Monday 14 September. U.S. Senate Daily Press floor log, 7 August 2026 (adjournment to 3 p.m. Monday 14 September); U.S. Senate floor activity records; The Block, 8 August 2026. Available at: Senate floor activity.

3. GENIUS Act, Pub. L. No. 119-27, 139 Stat. 419, signed 18 July 2025. “Fact Sheet: President Donald J. Trump Signs GENIUS Act into Law,” The White House, 18 July 2025; Sullivan & Cromwell LLP, “GENIUS Act Enacted,” July 2025. Available at: GovInfo (Pub. L. 119-27); White House fact sheet; Sullivan & Cromwell.

4. GENIUS Act, S. 1582, 119th Congress; Senate passed 68–30 on 17 June 2025, House passed 308–122 on 17 July 2025. Congress.gov, S. 1582. Available at: Congress.gov, S. 1582; Covington & Burling.

5. GENIUS Act commencement: presently scheduled for 18 January 2027 (eighteen months after enactment), unless final implementing regulations issued by the primary federal payment-stablecoin regulators trigger the alternative date of 120 days after issuance. As at 14 August 2026 no such final regulations had been issued. Congressional Research Service, IN12553; FDIC, proposed rule, GENIUS Act Requirements and Standards for FDIC-Supervised Permitted Payment Stablecoin Issuers and Insured Depository Institutions, 91 Fed. Reg. 18534, 10 April 2026. Available at: CRS IN12553; FDIC proposed rule.

6. Congressional Research Service, “Stablecoin Legislation: An Overview of S. 1582, GENIUS Act of 2025,” IN12553; FDIC, proposed rule, “GENIUS Act Requirements and Standards for FDIC-Supervised Permitted Payment Stablecoin Issuers and Insured Depository Institutions,” 91 Fed. Reg. 18534, 10 April 2026. Available at: CRS IN12553; FDIC proposed rule.

7. H.R. 3633 passed the House on 17 July 2025 by 294–134, Roll Call 199; more than seventy Democrats voted in favour. U.S. House Clerk, Roll Call 199 (2025). Available at: House Clerk, Roll Call 199.

8. H.R. 3633 (as passed by the House). On the asset/transaction distinction, see the SEC–CFTC joint interpretation, SEC Release Nos. 33-11412 and 34-105020 (17 March 2026), and Congressional Research Service, IN12583. Available at: SEC Release Nos. 33-11412 and 34-105020; CRS IN12583.

9. Three iterations are in play: the House-passed text, H.R. 3633 (17 July 2025); the Senate Banking Committee substitute (market-structure discussion draft, May 2026, banking.senate.gov); and the proposed merged Senate substitute of 22 July 2026, drafted as an amendment in the nature of a substitute intended to be proposed to H.R. 3633 (the Lummis substitute, lummis.senate.gov), not yet offered or adopted on the floor. Propositions in the notes below identify the governing version where the texts diverge. Available at: Senate Banking substitute; Lummis substitute.

10. SEC v. W. J. Howey Co., 328 U.S. 293 (1946).

11. SEC v. Ripple Labs, Inc., No. 20-cv-10832 (S.D.N.Y. 13 July 2023) (Torres, J.), holding institutional sales of XRP to be unregistered securities offerings while programmatic exchange sales were not.

12. Loper Bright Enterprises v. Raimondo, 603 U.S. 369 (2024), overruling Chevron U.S.A. Inc. v. Natural Resources Defense Council, 467 U.S. 837 (1984). The decision ended mandatory deference to reasonable agency constructions of ambiguous statutes; it did not displace the Howey investment-contract test, which courts apply independently.

13. House-passed text, H.R. 3633. The digital commodity definition turns on value intrinsically linked to the use of the blockchain and excludes securities, derivatives and stablecoins. Available at: H.R. 3633 text.

14. Senate Banking Committee substitute (May 2026 discussion draft, banking.senate.gov). The ancillary-asset construct carries its own disclosure, disposition and certification provisions and is not a mere renaming of the House investment-contract-asset category. Available at: Senate Banking substitute.

15. House-passed text, H.R. 3633. The four-part maturity test requires a functional network, open-source code, pre-established transparent rules and the absence of unilateral control, including through holdings of twenty per cent or more of the tokens. Available at: H.R. 3633 text.

16. Proposed Lummis substitute, §10103(c)(5), pp. 29–36. A network token is rebuttably presumed to be an ancillary asset unless its originator, or a qualifying digital-asset intermediary, certifies with reasonable supporting evidence that it is not. The certification becomes effective upon SEC non-objection or, absent a timely rebuttal, sixty days after submission. The SEC may deny the certification on finding that the asset is an ancillary asset or includes a disqualifying financial right. This mechanism differs from the mature-blockchain certification in §205 of H.R. 3633 as passed by the House. A denial constitutes final agency action reviewable under applicable law. Available at: Lummis substitute.

17. Proposed Lummis substitute: a notice-of-intent-to-register regime for digital commodity exchanges, brokers and dealers. The CFTC must establish the filing process within thirty days of enactment, and existing listings may continue until 180 days after the permanent registration rules take effect, subject to the Act’s conditions and delisting machinery. The four-year provisional-registration sunset appears in H.R. 3633 as passed by the House, not in the proposed substitute. Available at: Lummis substitute.

18. H.R. 3633; Congressional Research Service, IN12583. The Act extends the CFTC’s remit beyond its traditional derivatives and anti-manipulation jurisdiction to registration and supervision of digital commodity spot markets. Available at: CRS IN12583.

19. Proposed merged Senate substitute of 22 July 2026 (Lummis substitute), incorporating the Blockchain Regulatory Certainty Act and a decentralised-finance exclusion for validators and open-source publishers; see also “What is in the merged CLARITY Act text, and what changed,” Cryptonews, 22 July 2026. Available at: Lummis substitute; Cryptonews.

20. SEC–CFTC Memorandum of Understanding, 11 March 2026, signed by SEC Chairman Paul Atkins and CFTC Chairman Michael Selig; Norton Rose Fulbright and Global Fintech & Digital Assets Blog client analyses, March 2026. Available at: Norton Rose Fulbright; Global Fintech & Digital Assets Blog.

21. The Joint Harmonization Initiative spans six workstreams and is co-led by Robert Teply for the SEC and Meghan Tente for the CFTC. Global Fintech & Digital Assets Blog, March 2026. Available at: Global Fintech & Digital Assets Blog.

22. SEC Release Nos. 33-11412 and 34-105020 (17 March 2026). The release identifies sixteen assets as examples of digital commodities on the basis of their characteristics at the date of the release: APT, AVAX, BTC, BCH, ADA, LINK, DOGE, ETH, HBAR, LTC, DOT, SHIB, SOL, XLM, XTZ and XRP. The CFTC has stated it will administer the Commodity Exchange Act consistently with the interpretation. Available at: SEC Release Nos. 33-11412 and 34-105020; Jenner & Block; Ropes & Gray.

23. The interpretation does not bind a court and, after Loper Bright (note 12), cannot claim mandatory Chevron deference; it may be revised by a later Commission.

24. Senate Committee on Banking, Housing, and Urban Affairs, committee action of 14 May 2026 (reported 15–9); Senate Committee on Agriculture, Nutrition, and Forestry action, January 2026. See also CoinDesk, 14 May 2026. Available at: CoinDesk, 14 May 2026; Elliptic.

25. Proposed merged Senate substitute released 22 July 2026 (616 pages), drafted as an amendment in the nature of a substitute (Lummis substitute), adding government-ethics and law-enforcement titles; “New Clarity Act emerges that’s a start on the final draft,” CoinDesk, 22 July 2026. Available at: CoinDesk, 22 July 2026.

26. Senate Committee on Banking, Housing, and Urban Affairs (Democratic minority), materials on the President’s crypto income and requests for updated financial disclosures, 2026, banking.senate.gov/newsroom/minority. The $1.4 billion figure is the minority staff’s calculation from the President’s 2025 financial disclosure and is a contested political estimate. Available at: Senate Banking minority; TechTimes.

27. Joint statement of seven pro-crypto Senate Democrats opposing the July ethics provision, August 2026; reported in “Crypto Democrats Are Ghosting the Industry,” The American Prospect, 10 August 2026. Available at: The American Prospect.

28. Bank-industry submissions warning that third-party stablecoin reward programmes could draw substantial deposits from insured institutions; Senator Josh Hawley has conditioned support on the point. The Block, August 2026. Available at: The Block, 8 Aug 2026.

29. U.S. Senate Standing Rule XXII (cloture requires three-fifths of senators duly chosen and sworn, ordinarily sixty votes); Republicans hold fifty-three seats. The outstanding issues are ethics, illicit finance and integration of the Senate Agriculture Committee text. The Block, 8 August 2026. Available at: The Block, 8 Aug 2026.

30. Galaxy Research probability estimate, revised from fifty to thirty per cent in mid-2026, reported by The Block. This is one analyst’s projection, not an independently verifiable fact. Available at: The Block, 8 Aug 2026.

31. Markets in Crypto-Assets Regulation (EU) 2023/1114. The final EU-wide transitional period expired on 1 July 2026, though member states could shorten or decline the grandfathering they offered. ESMA, “Statement on the end of transitional periods under MiCA,” 2026. Available at: ESMA statement.

32. Financial Services and Markets Act 2000 (Cryptoassets) Regulations 2026, SI 2026/102, made 4 February 2026, legislation.gov.uk. FCA authorisation gateway 30 September 2026 to 28 February 2027, mandatory regime expected 25 October 2027 (FCA press release, 2026). Bank of England, policy statement on sterling-denominated systemic stablecoins, June 2026 (standard backing up to 70% short-dated government debt and at least 30% unremunerated central-bank deposits, with a systemic-at-launch step-up to 95% eligible government debt). Available at: SI 2026/102; FCA press release; Bank of England policy statement.

References

Legislation and instruments

Digital Asset Market Clarity Act, H.R. 3633, 119th Congress (House-passed 17 July 2025; Senate Banking substitute, May 2026; proposed merged Senate substitute (Lummis substitute), 22 July 2026).

GENIUS Act, Pub. L. No. 119-27, 139 Stat. 419 (2025).

Markets in Crypto-Assets Regulation (EU) 2023/1114.

Financial Services and Markets Act 2000 (Cryptoassets) Regulations 2026, SI 2026/102 (made 4 February 2026).

Case law

SEC v. W. J. Howey Co., 328 U.S. 293 (1946).

SEC v. Ripple Labs, Inc., No. 20-cv-10832 (S.D.N.Y. 2023).

Loper Bright Enterprises v. Raimondo, 603 U.S. 369 (2024).

Regulatory and official sources

SEC and CFTC, Application of the Federal Securities Laws to Certain Types of Crypto Assets and Certain Transactions Involving Crypto Assets, SEC Release Nos. 33-11412 and 34-105020, 17 March 2026.

SEC–CFTC Memorandum of Understanding and Joint Harmonization Initiative, 11 March 2026.

U.S. Senate Daily Press floor log, 7 August 2026, and Senate floor activity records.

Senate Committee on Banking, Housing, and Urban Affairs (majority and minority) and Senate Committee on Agriculture, Nutrition, and Forestry records, 2026.

FDIC proposed stablecoin rule, 91 Fed. Reg. 18534, 10 April 2026; Congressional Research Service, IN12583 and IN12553.

Financial Conduct Authority cryptoasset regime materials, 2026; Bank of England policy statement on systemic sterling stablecoins, June 2026; ESMA statement on MiCA transitional periods, 2026.

In January 2025, the Texas Attorney General commenced proceedings against Allstate Corporation and its data subsidiary Arity International LLC under the Texas Data Privacy and Security Act. The complaint alleged that Arity had collected trillions of miles of location data from approximately 45 million consumers through software trackers embedded in third-party mobile applications, without adequate disclosure or consent. That data was then sold to insurers for use in premium pricing. A parallel class action survived a motion to dismiss in March 2026.¹ The proceedings represent the first enforcement action under the TDPSA and a further major regulatory challenge to the telematics data supply chain now used in algorithmic insurance pricing across the United States.

Telematics data is only one input among many. Insurers and their data vendors increasingly draw on credit-based information, postcode-level demographic data, telematics, app-derived behavioural signals and other external consumer data to inform underwriting and pricing. The result is a pricing architecture in which traditional actuarial variables coexist with machine learning models trained on consumer data of a breadth and granularity that existing regulatory frameworks were not designed to address. Regulators, courts and legislatures are responding across multiple jurisdictions, but they are doing so unevenly, with different legal traditions and different conceptions of what constitutes unlawful discrimination in insurance underwriting.

Insurance is becoming both a deployer of AI and a market for the liabilities AI creates. That dual role is producing a regulatory structure in which underwriting, discrimination law, privacy enforcement, product liability and insurance coverage are beginning to converge.

Algorithmic Pricing and Proxy Discrimination

The central risk in algorithmic insurance pricing is proxy discrimination. An insurer may exclude race, ethnicity or disability from its pricing model and still produce discriminatory outcomes if the model relies on variables that correlate with those protected characteristics. Credit-based insurance scores, postcode-level data, telematics records and app-derived behavioural signals can each serve as proxies for characteristics that insurers are prohibited from using directly. The difficulty is that the correlation is often invisible to the insurer itself. A model optimised for predictive accuracy may learn to weight variables that are statistically associated with protected characteristics without any explicit instruction to do so.

The mechanism is well documented in adjacent sectors. In 2024, SafeRent Solutions agreed to a class-action settlement of approximately $2.3 million in Louis v SafeRent Solutions, after plaintiffs alleged that its tenant-screening algorithm disproportionately harmed Black and Hispanic rental applicants using housing vouchers. The Department of Justice was not the claimant, although it had earlier filed a statement of interest on the application of the Fair Housing Act to algorithmic screening.² In July 2025, the Massachusetts Attorney General secured a $2.5 million settlement from Earnest Operations LLC after an investigation found that the company’s algorithmic student lending model incorporated variables that served as proxies for race, resulting in less favourable terms for borrowers of colour.³

The pattern is now visible. Algorithmic discrimination claims are succeeding where claimants or regulators can show that facially neutral variables produce disparate outcomes along the lines of protected characteristics. The analytical framework is transferable to insurance. The same data sources that created liability in housing and lending are now embedded in insurance pricing models across personal lines. No insurance commissioner has yet imposed a fine specifically for algorithmic pricing discrimination. The enforcement gap is narrowing. The question is when the first action arrives, not whether it will.

The US Regulatory Response

Three regulatory developments define the current US position.

Colorado was the first state to legislate directly on algorithmic discrimination in insurance. Senate Bill 21-169, enacted in 2021, prohibited insurers from using external consumer data sources, algorithms or predictive models in a manner that unfairly discriminates on protected grounds. Regulation 10-1-1 initially focused on life insurers. Amendments adopted in August 2025 extended the governance and risk-management framework to private passenger automobile insurers and health benefit plan insurers, with line-specific compliance timing.⁴ The compliance obligation is granular. Insurers must identify the external data sources and algorithms used in underwriting, test for disparate impact across protected characteristics and document the results. Separately, Colorado’s broader AI regime has changed again. SB 24-205 was first deferred and then superseded by SB 26-189, signed in May 2026, which recasts the state’s framework around automated decision-making technology and creates obligations beginning on 1 January 2027. That broader regime will intersect with the insurance-specific requirements, but the operative structure is no longer the original SB 24-205 high-risk AI framework.

The New York Department of Financial Services issued Circular Letter No. 7 in 2024, setting out supervisory expectations for insurers using external consumer data and information sources, algorithms and predictive models in underwriting and pricing. The Circular Letter requires multi-step proxy-discrimination testing using a disproportionate-impact, justification and less-discriminatory-alternative methodology, documented governance, contractual controls over vendors and detailed adverse-decision explanations. It also creates a specific 15-day notice obligation where an applicant cannot be underwritten through an External Consumer Data and Information Sources/Algorithmic and Innovative Systems (ECDIS/AIS) process and must instead proceed through a non-ECDIS/AIS process.⁵ The DFS approach is notable for its specificity. It prescribes the testing methodology by which compliance is to be demonstrated and extends the governance obligation to the insurer’s supply chain. A carrier that purchases pricing data from a vendor such as Arity is expected to hold contractual rights sufficient to audit the vendor’s data collection and modelling practices.

Through the state-regulatory coordination process, the National Association of Insurance Commissioners published a Model Bulletin on the Use of Artificial Intelligence Systems by Insurers on 4 December 2023. The Model Bulletin sets supervisory expectations that insurers maintain a written AI programme, conduct impact assessments and ensure human oversight of algorithmic decisions. More than 24 states have adopted or issued guidance based on the Model Bulletin.⁶ A 12-state pilot examination tool, designed to standardise supervisory review of insurer AI systems, commenced in March 2026 and is expected to run until September 2026, with formal adoption anticipated in the autumn. Once adopted, the examination tool is likely to become the baseline standard against which state regulators assess insurer AI compliance.

The US regulatory framework is therefore layered. State-level legislation, supervisory circulars and model standards adopted through the National Association of Insurance Commissioners (NAIC) process operate concurrently, with varying degrees of enforcement rigour and different definitions of key terms. An insurer writing business across multiple states faces a patchwork of obligations that differ in scope, in the specificity of testing requirements and in the consequences of non-compliance.

The EU Framework

The European Union has taken a different approach, embedding insurance AI within a horizontal regulatory architecture rather than addressing it through sector-specific legislation.

The EU AI Act classifies AI systems used for risk assessment and pricing in relation to natural persons in life and health insurance as high-risk under Annex III, point 5(c).⁷ High-risk classification triggers conformity assessments, risk management systems, data governance requirements, transparency obligations and human oversight. Under Regulation (EU) 2026/1744, the Digital Omnibus on AI, stand-alone Annex III high-risk AI obligations will apply from 2 December 2027, while obligations for high-risk AI systems embedded in regulated products will apply from 2 August 2028. Insurers operating in the EU therefore have a defined implementation window, but the obligations themselves remain substantial and require significant investment in documentation, testing and governance infrastructure. An insurer that uses a third-party AI model for pricing will need to satisfy itself that the model meets the conformity requirements, even if the insurer did not develop the model.

The AI Liability Directive, which would have established a harmonised framework for civil liability arising from AI systems, was withdrawn in October 2025. Its withdrawal leaves the liability position fragmented across national tort regimes. The revised Product Liability Directive (2024/2853) partially fills the gap. The Directive extends the definition of “product” to include software and AI systems, and it introduces a concept of defect that applies to self-learning systems whose behaviour changes after placing on the market.⁸ The revised Product Liability Directive is relevant where an AI system causes damage falling within the Directive’s scope, including defects in software or self-learning systems. It is less likely to serve as the principal civil-liability route for discriminatory insurance pricing, which will more naturally arise through the AI Act, sectoral insurance supervision, equality law, consumer protection, data protection and national tort regimes. Member states must transpose the Directive by December 2026.

The European Insurance and Occupational Pensions Authority published an Opinion on AI Governance in August 2025, setting out supervisory expectations for national competent authorities.⁹ The EIOPA Opinion adopts a risk-based and proportionate approach, emphasising that the intensity of supervisory scrutiny should reflect the materiality of the AI system’s impact on policyholders. It does not create directly binding obligations on insurers but signals the direction of supervisory engagement across the single market.

The EU position therefore combines ex ante classification under the AI Act, product liability exposure under the revised Directive and supervisory expectations through EIOPA. The withdrawal of the AI Liability Directive creates a gap in the harmonised civil liability framework that national courts and legislatures may address differently across member states.

The UK Position

The United Kingdom has no AI-specific insurance regulation. The Financial Conduct Authority has relied on existing conduct rules, principally the General Insurance Pricing Practices rules (PS21/5), which require insurers to offer renewal prices no higher than equivalent new business prices, and the Consumer Duty (introduced in July 2023), which imposes a general obligation to deliver good outcomes for retail customers.¹⁰

The Consumer Duty is broad enough in principle to capture algorithmic pricing discrimination. If an insurer’s pricing model produces systematically worse outcomes for customers who share a protected characteristic, there is a credible argument that the insurer has failed to deliver the good outcomes the Duty requires. The FCA has acknowledged that bias in algorithmic decision-making is a “live issue” for the insurance sector. The Treasury Committee has called on the FCA to produce specific guidance on AI in financial services by the end of 2026.

The UK approach is reactive rather than prescriptive. The legal tools to challenge algorithmic discrimination in insurance pricing exist within the current framework. What is absent is the specific regulatory guidance that would tell insurers how to test for proxy discrimination, what governance structures to maintain and what disclosures to make to affected consumers. The contrast with the New York DFS Circular Letter is instructive. An insurer operating in New York has a prescribed testing methodology, defined vendor audit obligations and a specific 15-day notice obligation where an applicant cannot be underwritten through an ECDIS/AIS process. An insurer operating in the United Kingdom has a general duty to produce good outcomes and an acknowledgement from the regulator that bias is a live issue. The gap between the two positions is one that regulated firms and their advisers must manage without regulatory guidance.

AI in Healthcare Coverage Decisions

The application of AI to coverage determinations in health insurance raises distinct concerns that go beyond pricing.

In Estate of Lokken v UnitedHealth Group Inc., proceedings before the United States District Court for the District of Minnesota, the plaintiffs alleged that UnitedHealth used an algorithm called nH Predict to deny post-acute care benefits to Medicare Advantage members. The plaintiffs allege that internal data showed the algorithm’s denial recommendations were overturned on appeal at a rate of approximately 90 per cent, and that UnitedHealth continued to use the system despite knowledge of this error rate. UnitedHealth disputes the allegations. The Court ordered discovery in March 2026, permitting the plaintiffs to obtain documents concerning the development, design, creation, approval, implementation, use and oversight of nH Predict, while not requiring production of the data, rules, source code or medical guidelines on which the system was based.¹¹

The case is significant for two reasons. First, it moves the algorithmic accountability inquiry from pricing to coverage, an area where the consequences of an erroneous decision are immediate and potentially irreversible for the policyholder denied treatment. Second, the discovery order opens the development, deployment and oversight of the algorithm to judicial scrutiny in a way that ordinary regulatory examination has not yet achieved. If the litigation proceeds to trial, it is likely to produce the most detailed judicial analysis of an insurer’s AI system to date.

California enacted SB 1120 in 2024, effective from January 2025, requiring health plans and insurers using AI, algorithms or software tools in utilisation review to preserve human clinical decision-making, use patient-specific clinical information and prevent discriminatory application. The statute also requires disclosure of AI use in utilisation-review policies and procedures. It goes beyond transparency: it imposes substantive constraints on how AI may be used in coverage determinations, including a prohibition on denying care based solely on algorithmic output.

Insuring AI

As insurers adopt AI in their own operations, a parallel market is developing to insure the liabilities that AI systems create for their deployers across all sectors.

Armilla AI, in partnership with Chaucer, launched an AI liability insurance product in April 2025. Munich Re markets aiSure, a suite of AI performance insurance products. The Artificial Intelligence Underwriting Company (AIUC) was reported in 2026 to offer up to $50 million of product-liability coverage for AI agent failures, backed by Beazley paper. Testudo, a managing general agent specialising in AI liability, launched in January 2026.¹² These new entrants are building products for a risk category that the traditional insurance market has been slow to address.

Standard commercial general liability and professional indemnity policies were not drafted with algorithmic decision-making in mind. Exclusions for technology errors, data processing failures and software performance are common and may operate to deny coverage for losses caused by AI systems. The traditional market response has been twofold. First, absolute AI exclusions are appearing in professional indemnity and technology errors and omissions policies, removing any ambiguity about whether existing wordings respond to AI-related claims. Second, insurers are eliminating “silent AI” exposure by expressly addressing AI in policy wordings, ensuring that coverage is either affirmatively granted and priced or explicitly excluded.

The market is splitting. Specialist providers are writing affirmative AI liability coverage at rates that reflect the novelty and uncertainty of the risk. Traditional insurers are restricting or excluding coverage for the same risk. Organisations that identify the gap and purchase specialist cover will have protection. Those that assume their existing policies respond to AI-related claims may discover at the point of loss that they do not. The timing is acute. Regulatory obligations are increasing, litigation risk is rising and the insurance market that would ordinarily absorb those risks is simultaneously repricing and restricting.

Strategic Outlook

The regulatory framework for AI in insurance is fragmented across jurisdictions, between sectoral and horizontal regulation and between legislative rules and supervisory expectations. That fragmentation creates compliance cost for multinational insurers and uncertainty for consumers whose rights depend on the jurisdiction in which their policy is written.

Three consequences follow.

First, proxy discrimination is likely to be the primary litigation vector for the next several years. The analytical framework is established. Enforcement precedents from housing and lending are accumulating. The data sources that create proxy discrimination risk are becoming more central to insurance pricing models. The absence of an insurance-specific enforcement action to date reflects regulatory caution and the difficulty of demonstrating algorithmic causation, not regulatory acceptance of discriminatory outcomes.

Second, the structural gap between the US and EU approaches is unlikely to close. The EU has opted for ex ante classification and conformity assessment under the AI Act. The US is developing a mixture of state-level legislation, supervisory guidance and enforcement actions through litigation. The UK has adopted neither approach in a meaningful form. Each has limitations. Ex ante classification imposes compliance cost regardless of whether a particular system creates risk. State-level variation creates regulatory arbitrage opportunities. Reliance on existing conduct rules leaves regulated firms without clear standards and exposes them to enforcement actions based on rules that were drafted before the technology they are applied to existed.

Third, the AI liability insurance market is developing faster than the regulatory framework it is responding to. If coverage contraction in the traditional market accelerates, organisations deploying AI in insurance and adjacent sectors may face a period in which meaningful coverage is available only from specialist providers at premium rates that reflect the absence of actuarial data on AI-related losses. The convergence of rising regulatory obligations, increasing litigation risk and tightening insurance coverage creates a compounding exposure that boards and risk committees should be addressing now.

The period between mid-2026 and the end of 2027, when the NAIC examination tool is expected to reach formal adoption and stand-alone Annex III obligations under the EU AI Act begin to apply under the Digital Omnibus timetable, is likely to define the structural terms on which AI in insurance operates for the following decade. The regulatory choices made in that window will determine whether algorithmic underwriting remains a competitive advantage available to insurers with robust governance or becomes a source of systemic liability for those without it.

Notes

¹ Texas v Allstate Corp. and Arity International LLC, proceedings commenced January 2025 in the State of Texas. The complaint alleged violations of the Texas Data Privacy and Security Act (TDPSA). A parallel class action survived a motion to dismiss in March 2026.

² Louis v SafeRent Solutions LLC, class-action settlement 2024, approximately $2.3 million. The plaintiffs alleged that SafeRent’s tenant-screening algorithm disproportionately harmed Black and Hispanic rental applicants using housing vouchers. The Department of Justice filed a statement of interest on the application of the Fair Housing Act to algorithmic screening but was not the claimant.

³ Massachusetts AG v Earnest Operations LLC, settlement July 2025, $2.5 million. The Massachusetts Attorney General’s investigation found that Earnest’s algorithmic student lending model used variables that served as proxies for race.

⁴ Colorado SB 21-169 (2021); Regulation 10-1-1 amended August 2025 to extend the governance and risk-management framework to private passenger automobile insurers and health benefit plan insurers, with line-specific compliance timing. Colorado SB 26-189, signed May 2026, superseded the earlier SB 24-205 framework and creates broader automated decision-making technology obligations from 1 January 2027.

⁵ New York Department of Financial Services, Circular Letter No. 7 (2024), on the use of artificial intelligence systems and external consumer data and information sources in insurance underwriting and pricing.

⁶ National Association of Insurance Commissioners, Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, 4 December 2023. A 12-state pilot examination tool commenced March 2026, with formal adoption expected autumn 2026.

⁷ Regulation (EU) 2024/1689 (the AI Act), Annex III, point 5(c). Regulation (EU) 2026/1744, the Digital Omnibus on AI, entered into force on 27 July 2026 and provides that stand-alone high-risk AI obligations apply from 2 December 2027, while high-risk AI systems embedded in regulated products apply from 2 August 2028. The AI Liability Directive was formally withdrawn in October 2025.

⁸ Directive 2024/2853 on liability for defective products (revised Product Liability Directive). Member state transposition deadline December 2026. The Directive extends the definition of “product” to include software and AI systems and introduces a concept of self-learning defect extending beyond placing on the market.

⁹ European Insurance and Occupational Pensions Authority, Opinion on AI Governance, August 2025.

¹⁰ FCA, PS21/5, General Insurance Pricing Practices (2021). The Consumer Duty was introduced in July 2023. The FCA has acknowledged algorithmic bias as a “live issue”. The Treasury Committee has called for AI-specific guidance by the end of 2026.

¹¹ Estate of Lokken v UnitedHealth Group Inc. (D. Minn.). The complaint alleged that UnitedHealth’s nH Predict algorithm denied post-acute care to Medicare Advantage members with an approximately 90 per cent error rate on appeal. Discovery was ordered in March 2026.

¹² Armilla AI, in partnership with Chaucer, launched an AI liability insurance product in April 2025. Munich Re markets aiSure as an AI performance insurance suite. The Artificial Intelligence Underwriting Company (AIUC) was reported in 2026 to offer up to $50 million of product-liability coverage for AI agent failures, backed by Beazley paper. Testudo, a specialist AI liability MGA, launched in January 2026.

A class action now before the Southern District of New York gathers two questions that the law of digital assets has not yet answered. The first is whether a project can be held to account for advertising machine autonomy it did not possess. The second is whether a decentralised autonomous organisation is a defendant that can be served, held to answer and made to pay. Pikabea v. Walters puts both in issue at once. The procedural skirmishing in its opening months shows why neither is straightforward.¹

The complaint

The AI16Z token launched on the Solana blockchain in October 2024. It was marketed around an autonomous artificial intelligence agent said to make its own investment decisions and to manage an on-chain treasury. It also borrowed the cachet of Andreessen Horowitz, the venture firm whose a16z branding the project name evoked without any affiliation.² By early January 2025 the token carried a market capitalisation of about $2.6 billion.³ It then collapsed. The token was later rebranded ELIZAOS.

Gorka Pikabea, a buyer resident in Spain, commenced proceedings in April 2026 on behalf of a proposed class of purchasers between 24 October 2024 and 20 April 2026. The complaint identifies at least 3,945 wallet addresses said to have sustained losses.⁴ Its central factual allegation is arresting in its simplicity. The autonomous agent, it says, was operated by hand. The machine agency that gave the token its narrative was, on the plaintiff’s account, a person.

The pleaded causes of action are reported as deceptive acts and false advertising under New York General Business Law, unfair competition and false advertising under California law, negligent misrepresentation and unjust enrichment.⁵ The named defendants are Eliza Labs, Inc., its founder Shaw Walters, Sebastian Quinn-Watson, the ai16z DAO itself and fifty unnamed persons. The relief sought includes damages, restitution and disgorgement. Every allegation remains unproven. A motion to dismiss was made in June 2026.⁶

AI-washing as a pleaded wrong

The choice of causes of action is the first point of interest. The plaintiff has not led with the federal securities laws. The wrong is framed as deception. The project said the product did something it did not do. That framing places the case in a lineage that predates the token boom and reaches well beyond it.

In March 2024 the Securities and Exchange Commission brought its first enforcement actions for what it called AI-washing, settling charges against two investment advisers, Delphia (USA) Inc. and Global Predictions Inc., for false and misleading statements about their use of artificial intelligence. Delphia had claimed to use an algorithm to analyse client data that it had not built. Global Predictions had described itself as the first regulated AI financial adviser. The firms paid $400,000 in total penalties without admitting or denying the findings.⁷ The regulatory point was that the misrepresentation of artificial intelligence is an old wrong wearing new clothes. A false statement about capability is actionable whether the capability claimed is human or machine.

Pikabea takes that logic into private litigation and into the crypto market. If the pleaded facts are made out, the deception is not in the token mechanics or the price action. It is in the representation of autonomy. That reframing carries consequences for proof and for reach. Consumer-protection and false-advertising claims turn on statements and their effect on the audience and do not carry the definitional battles over what is a security that have occupied the crypto docket for a decade. A claimant who can plead a clear misstatement of capability sidesteps the harder question of characterisation and gets to causation and loss.

The DAO as defendant

The second question is the one the court reached first and the more novel. The plaintiff named the ai16z DAO as a defendant in its own right, alongside the identifiable people behind it. That raises a problem the law of unincorporated collectives has been circling for several years. Who accepts service for an on-chain organisation with no registered office, no directors and no agent. At the initial pretrial conference the court questioned why the founder had not been served after a single attempt in San Francisco, noted that a co-defendant resides in Australia and heard the position, advanced for the defence, that the DAO could not be sued at all.⁸

The last submission is not fanciful, but it runs against the recent authorities. Two lines of cases now bear on whether a DAO is a defendant with legal capacity.

The first treats a DAO as an unincorporated association. In CFTC v. Ooki DAO the Commodity Futures Trading Commission obtained a default judgment against a DAO that had ignored the proceedings. Judge Orrick held that Ooki DAO was an unincorporated association subject to suit under the Commodity Exchange Act, entered judgment for $643,542, imposed trading and registration bans and ordered the associated website content removed.⁹ The ruling’s importance lay in a single holding, that the collective was a person the statute could reach.

The second line is more consequential for members and runs through the general partnership. In Sarcuni v. bZx DAO the Southern District of California declined to dismiss a negligence claim brought by users who had lost about $1.7 million in a protocol hack, finding that the plaintiffs had sufficiently alleged that bZx DAO was a general partnership under the California Corporations Code and that its members were the partners.¹⁰ A general partnership carries joint and several liability. On that theory the liabilities of the collective become the personal exposure of those who compose it.

The general partnership problem

The general partnership theory was sharpened a year later. In Samuels v. Lido DAO, a claim that the LDO token was an unregistered security, Judge Chhabria held that Lido DAO could be a general partnership and, more pointedly, that certain institutional participants were plausibly its general partners. The court distinguished passive holding from active governance. Paradigm Operations, Andreessen Horowitz and Dragonfly, which held large blocks of governance tokens and had held themselves out as active in the organisation, were plausibly partners. Another investor, Robot Ventures, was dismissed for want of sufficient allegations of active participation.¹¹ The general counsel of a16z crypto called the decision a serious blow to decentralised governance, on the view that any participation, down to a forum post, might fix a member with the liabilities of the whole.¹²

That distinction is the legal core of the coming fight and disciplines the loose language that surrounds these cases. Liability does not attach to token holders as such. It attaches to members or active participants, to those who exercise or hold out that they exercise control. The question in each case is not who owned a token but who ran the enterprise. For ai16z, where the allegation is precisely that a human hand directed what was sold as an autonomous system, the general partnership analysis and the AI-washing analysis converge on the same small group of people.

What it means

Three consequences follow for anyone building or funding in this space.

For AI-token projects the exposure is not confined to the collapse of the token. It sits in the marketing. A project that advertises autonomy it does not have invites a deception claim that may be easier to plead than a securities case because it does not first require the claimant to win the token-classification fight. The Commission has already shown that a regulator will treat the misdescription of artificial intelligence as a discrete wrong. The gap between what a system is said to do and what it does is now a legal surface.

For DAOs the case tests whether the structure absorbs a claim or merely makes the defendants harder to find. The recent authorities suggest the latter. A court that regards a DAO as an unincorporated association may let it be sued. A court that regards it as a general partnership may look through it to the participants who directed it. Decentralisation complicates service. It does not, on the current cases, dissolve liability.

For founders and insiders the practical lesson is that the collective form is not obviously a shield. The claimant strategy these cases invite is straightforward. Name the reachable humans, plead the general partnership to widen the net and let the difficulty of serving a decentralised entity fall on the defence rather than the claim.

The open question

What remains unresolved is whether the merits will be reached at all. The motion to dismiss will test whether AI-washing dressed as consumer protection states a claim. The DAO-capacity issue may arise separately through service, representation and any later challenge to the DAO’s amenability to suit. But the case may turn on service and settlement before it turns on doctrine. If the DAO cannot be served or represented, the claim falls back onto the identifiable people the court can reach. The fork the court has not resolved is whether naming the DAO widens liability to its members or active participants or simply exposes how little of a decentralised collective the law can, in practice, touch.

Code on Trial is a weekly briefing on AI, crypto and digital disputes.

Notes

1 Pikabea v. Walters et al, No. 1:26-cv-03238 (S.D.N.Y.), before Judge Rakoff. Docket via Justia and PacerMonitor. All allegations unproven.

2 Cointelegraph (22 May 2026); secondary crypto reporting on the launch and marketing of the AI16Z token.

3 Cointelegraph, 22 May 2026 (peak market capitalisation of about $2.6 billion, January 2025).

4 Complaint as summarised in secondary reporting (Claim Depot; KuCoin). The class period and the figure of at least 3,945 wallet addresses should be confirmed against the complaint.

5 Pleaded counts per secondary summaries of the complaint: New York General Business Law ss 349 and 350; California Unfair Competition Law and False Advertising Law; negligent misrepresentation; unjust enrichment.

6 Docket: motion to dismiss made 18 June 2026; opposition due 3 July; replies due 13 July 2026; telephonic pretrial conference listed 31 July 2026.

7 SEC, Press Release 2024-36 (18 March 2024); settled orders In re Delphia (USA) Inc. and In re Global Predictions Inc.; combined penalties of $400,000.

8 Inner City Press, report of the 19 May 2026 conference before Judge Rakoff; Cointelegraph, 22 May 2026.

9 CFTC v. Ooki DAO, No. 3:22-cv-05416-WHO (N.D. Cal.), default judgment 8 June 2023 (Orrick, J.); judgment for $643,542 with trading and registration bans and removal of website content.

10 Sarcuni v. bZx DAO, No. 3:22-cv-00618, 664 F. Supp. 3d 1100 (S.D. Cal. 2023), order of 27 March 2023 (partial denial of motions to dismiss).

11 Samuels v. Lido DAO, No. 3:23-cv-06492 (N.D. Cal.), order of 18 November 2024 (Chhabria, J.); Securities Act 1933 s 12(a)(1) claim; general partnership finding as to certain participants.

12 Miles Jennings, General Counsel, a16z crypto, public commentary on the Lido decision (reported by Decrypt, November 2024).

A federal court in Manhattan has certified a class of Bitcoin and other cryptocommodity buyers to pursue market manipulation and antitrust claims against the entities behind the world’s largest stablecoin. The certification order in In re Tether and Bitfinex Crypto Asset Litigation, No. 19 Civ. 9236 (S.D.N.Y.), signed on 23 February 2026 and issued in redacted form in March, survived its first appellate test on 2 July 2026, when the Second Circuit declined the defendants’ Rule 23(f) petition for interlocutory review.¹ The order rewards closer reading, both for what it decides and for what it leaves for another day.

A procedural win with a reserved question

The plaintiffs allege that Tether issued hundreds of millions of USDT that were not backed one to one by US dollars, then used the tokens to buy Bitcoin as prices fell, manufacturing demand and inflating the market. They sue under the Sherman Act on the cryptocommodity purchases and under the Commodity Exchange Act on the futures. Judge Katherine Polk Failla held that common questions of conduct and intent predominated under Rule 23(b)(3), excluded the plaintiffs’ event study because its statistical test violated its own independence assumption and admitted the regression and overcharge models. She then reserved the decisive question, whether unbacked issuance actually caused the alleged inflation, for summary judgment, citing Tyson Foods v Bouaphakeo, 577 U.S. 442 (2016).² Certification here is a finding that the claims can be tried together, not that they will succeed.

One point of doctrine did the heavy lifting. The court reasoned that this is at its core an antitrust case, where injury flows from the overcharge itself, rather than a securities case, where a plaintiff must show realised economic loss. On that footing the class cleared predominance. The court still narrowed it, confining the classes to buyers who paid in fiat or stablecoins and excluding anyone who acquired assets only by mining, forks or gifts.

The precedent chain the court had to climb

The harder obstacle was territorial. The Commodity Exchange Act does not apply extraterritorially. The Second Circuit imports only the transaction-based prong of Morrison v National Australia Bank, 561 U.S. 247 (2010), through Absolute Activist Value Master Fund v Ficeto (2d Cir 2012), Choi v Tower Research Capital (2d Cir 2018) and In re Platinum and Palladium Antitrust Litigation (2d Cir 2023). Domesticity turns on where irrevocable liability is incurred or title passes. Judge Failla split the putative class into purchasers on domestic exchanges, on foreign exchanges and on stateless exchanges, held that foreign-exchange futures required individualised proof and fell away, then assessed stateless venues exchange by exchange rather than trader by trader.³

The timeline frames the exposure. In 2021 the CFTC penalised Tether US$41 million and the New York Attorney General settled for US$18.5 million, both over misstatements about the reserves said to back USDT.⁴ The certified class period runs from March 2017 to February 2019. Judge Failla signed the certification order in February 2026 and the Second Circuit refused interlocutory review in July 2026. Nine years after the conduct, the merits question of causation is only now approaching trial.

England, collective redress and the road ahead

England has no general opt-out damages class action. The nearest analogue is the collective proceedings regime before the Competition Appeal Tribunal under section 47B of the Competition Act 1998, opened up by Merricks v Mastercard [2020] UKSC 51.⁵ A stablecoin manipulation claim pleaded as an abuse of dominance or a cartel could in principle be certified there. A freestanding market-manipulation class of the Tether kind, resting on commodities and antitrust theory, has no clean English home, which is one reason this litigation is unfolding in New York and not London.

The forward-looking point is regulatory. The 2025 US federal stablecoin statute, the GENIUS Act, now imposes reserve-backing and disclosure obligations on payment stablecoin issuers, changing the evidential landscape for future conduct even as this case litigates a period that predates it. For issuers the immediate lesson is procedural. A certification that survives Rule 23(f) review sharpens settlement pressure well before any judgment on the merits, whatever the causation ruling ultimately holds.

Notes

¹ In re Tether and Bitfinex Crypto Asset Litigation, No. 19 Civ. 9236 (S.D.N.Y.); class-certification order signed 23 February 2026, redacted order entered March 2026. Second Circuit denial of the Rule 23(f) petition reported 2 July 2026 (Law360; MLex).

² Tyson Foods, Inc. v Bouaphakeo, 577 U.S. 442, 457 (2016) (failure of proof as to an element is a question for summary judgment, not class certification).

³ See Morrison v National Australia Bank Ltd, 561 U.S. 247 (2010); Absolute Activist Value Master Fund Ltd v Ficeto, 677 F.3d 60 (2d Cir 2012); Choi v Tower Research Capital LLC, 890 F.3d 60 (2d Cir 2018); In re Platinum and Palladium Antitrust Litigation, 61 F.4th 242 (2d Cir 2023); Williams v Binance, 96 F.4th 129 (2d Cir 2024) (test for domesticity of transactions on stateless exchanges).

⁴ CFTC Order, In re Tether Holdings Ltd (15 October 2021); Office of the New York State Attorney General, settlement with Bitfinex and Tether (23 February 2021).

⁵ Merricks v Mastercard Inc [2020] UKSC 51; Competition Act 1998, s 47B.

Introduction

At CES 2025, John Deere demonstrated a new generation of autonomous agricultural machines, including the 9RX tractor for large-scale tillage and the 5ML orchard tractor for air-blast spraying, each designed to operate without a human driver in the cab.¹ Deere’s second-generation autonomy kit uses computer vision, AI and camera-based perception; the 5ML orchard tractor adds LiDAR sensors for dense orchard canopies. The machines identify obstacles, adjust implement settings and report their progress to a remote operator through a digital interface. Deere has been deploying autonomous capability incrementally across its equipment line since 2022, when it launched the 8R autonomous tractor with tillage capability. By 2026, Deere was marketing autonomous tillage and orchard-spraying capability and extending automation features across additional equipment classes, but availability depends on product line, geography and deployment configuration.¹

Outside mining, agriculture is one of the clearest commercial use cases for autonomous heavy equipment. The operational logic is similar: repetitive tasks across large areas, labour shortages and the economic case for precision application of inputs. But the liability framework is different in kind. Mining operates on private land under industrial safety regulation. Agriculture operates on private land adjacent to public roads, residential property and waterways, manages biological systems that respond unpredictably to inputs and generates chemical-drift and environmental-contamination exposures that mining does not. An autonomous mining truck that leaves its designated circuit strikes a manned excavator on the same site. An autonomous tractor that leaves its designated field strikes a cyclist on an adjacent road.

Simultaneously, the genetic engineering of crops has moved from transgenic modification (inserting foreign DNA) to gene editing (modifying the organism’s own genome using CRISPR-Cas9 and related tools). The regulatory and intellectual property frameworks governing gene-edited crops are diverging across jurisdictions in ways that will shape global agricultural trade for decades. The CRISPR patent dispute between the Broad Institute and the University of California, one of the most commercially significant patent disputes in biotechnology, remains commercially unresolved despite the Broad Institute’s victory before the Patent Trial and Appeal Board.²

These two technologies are converging on the same farms. A precision agriculture operation in 2026 may use AI to determine which gene-edited seed variety to plant in which part of a field, deploy autonomous equipment to plant and tend it and use machine-learning models to optimise irrigation, fertilisation and pest management throughout the growing season. The liability framework for this integrated system spans product liability, environmental law, intellectual property, data privacy and the emerging regulation of autonomous vehicles on and adjacent to public roads.

Gene editing is not itself AI, but its commercial deployment increasingly sits inside AI-enabled farming systems: seed selection, field zoning, input optimisation, traceability and yield prediction. The legal significance lies less in any single technology than in the integrated decision chain across seed, software, machinery and data.

Autonomous Agricultural Equipment and Liability

The deployment of autonomous tractors does not simply shift liability from operator to manufacturer. It changes the factual inquiry. A conventional tractor accident usually begins with the conduct of the person operating the machine and the farm business that deployed it. An autonomous-equipment incident would add questions about product design, software performance, remote supervision, field boundaries, obstacle detection, service history and the adequacy of the farm operator’s deployment controls. When an autonomous tractor causes injury, the physical driver may not exist, but there may still be a farm operator, a remote supervisor, a dealer or service network and one or more technology suppliers. The liability analysis does not transfer from one party to another. It layers across the manufacturer (for product defect), the software provider (for navigation or obstacle-detection failure), the farm operator (for the decision to deploy and the adequacy of perimeter safety measures) and, where the machine was remotely supervised, the person exercising the supervisory function.

John Deere’s autonomous systems currently require a human to set up the machine in the field and monitor its operation remotely. The operator can stop the machine at any time through the remote interface. If the machine encounters an obstacle it cannot classify, it stops and alerts the operator. This supervised-autonomy model preserves a human in the loop, but the human’s role is supervisory rather than operational. Whether remote supervision satisfies the duty of care that would apply to a human operator physically present on the equipment has not been tested.

US state law governing agricultural equipment liability varies significantly. Several states have enacted “right to farm” statutes that limit nuisance claims, and in some cases related claims, against established agricultural operations. There appears to be no state-level autonomous-agricultural-equipment liability regime comparable to the EU product-safety model; existing state law instead operates through product-liability, negligence, farm-safety, traffic and right-to-farm principles. The Supporting Innovation in Agriculture Act of 2025 (HR 1705, 119th Congress) proposed tax incentives for investment in innovative agricultural technology projects, including equipment and software used in precision or controlled-environment agriculture, but did not create a safety or liability framework for autonomous agricultural equipment.³

The European Union has legislated directly. The Machinery Regulation (EU) 2023/1230, replacing the 2006 Machinery Directive from 20 January 2027, introduces specific requirements for autonomous mobile machinery: a mandatory supervisory function, defined working areas with physical borders or obstacle detection and a risk assessment that accounts for machines with self-evolving behaviour after placement on the market.⁷ Safety components with fully or partially self-evolving behaviour using machine-learning approaches and ensuring safety functions fall within Annex I Part A of the Machinery Regulation and require notified-body conformity assessment. The EU AI Act compounds the obligation. Annex I lists both the Machinery Regulation and Regulation (EU) No 167/2013 on agricultural and forestry vehicles. Where an AI system is a product, or a safety component of a product, covered by Annex I Union harmonisation legislation and subject to third-party conformity assessment, it falls into the AI Act’s high-risk regime. In agricultural machinery, the key practical question will be whether the AI element performs a safety function or materially affects safe operation.⁸ European manufacturers of autonomous agricultural equipment face a layered compliance structure that has no current equivalent in US federal law.

The United Kingdom’s Automated Vehicles Act 2024 addresses automated vehicles on roads and in other public places.⁹ It does not provide a bespoke regime for autonomous agricultural equipment operating solely on private farmland. An autonomous tractor operating in a field is outside its scope. The same machine crossing a public road to reach an adjacent field enters a different regulatory regime. The regulatory difficulty arises at the boundary between private-field operation and use on roads or other public places.

The CRISPR Patent Landscape

The foundational CRISPR-Cas9 gene-editing patents are the subject of one of the most consequential intellectual property disputes in the history of biotechnology. The Broad Institute of MIT and Harvard holds US patents covering the use of CRISPR-Cas9 in eukaryotic cells (including plant and animal cells), based on the work of Feng Zhang. The University of California, Berkeley, Jennifer Doudna and Emmanuelle Charpentier hold patents covering the CRISPR-Cas9 system itself, based on their earlier work demonstrating the system’s gene-editing capability in vitro.²

The US Patent Trial and Appeal Board ruled in 2022 that the Broad Institute had priority over UC Berkeley for the use of CRISPR-Cas9 in eukaryotic cells. The Federal Circuit remanded the case in May 2025, finding the PTAB had applied the wrong standard. The PTAB reaffirmed its original decision in March 2026. The practical consequence is overlapping patent positions rather than a single clean winner: Broad holds priority for eukaryotic-cell applications in the US interference, while UC-related portfolios remain relevant to the underlying CRISPR-Cas9 system and to other jurisdictions. Commercial applications in plants and animals may require engagement with both patent portfolios, depending on jurisdiction, claim scope, product design and licensing route.²

For agricultural biotechnology companies developing gene-edited crop varieties, the patent landscape creates a licensing cost structure that sits beneath every commercial product. Since 2017, Corteva Agriscience and the Broad Institute have jointly offered non-exclusive licences for agricultural applications of CRISPR-Cas9. Other companies may need to negotiate access through that joint licensing framework or through other portfolio-specific licensing arrangements. Some of that licensing cost may be passed through to farmers through seed pricing, creating an economic dynamic comparable to the technology licensing fees embedded in precision agriculture equipment.

Gene-Edited Crops and Regulatory Divergence

The regulatory treatment of gene-edited crops has diverged sharply across jurisdictions. The United States adopted the USDA’s SECURE rule in 2020, which exempted gene-edited plants from the regulatory oversight that applies to transgenic organisms, provided the genetic modification could have been achieved through conventional breeding. In December 2024, a federal district court in the Northern District of California vacated the SECURE rule, finding that the USDA had failed to articulate a reasoned basis for certain provisions under the Administrative Procedure Act.⁴ The vacatur was prospective; crops already reviewed under the SECURE process need not be re-reviewed. Following the vacatur, APHIS had to reassess gene-edited plants under the reinstated pre-2020 biotechnology framework, while prior responses and active permits issued before 2 December 2024 remained valid. The practical effect is that the US regulatory posture toward gene-edited crops is now less settled than it appeared twelve months ago.

Europe took the opposite position until recently. The Court of Justice of the European Union ruled in 2018 (Case C-528/16) that organisms produced by gene editing fall within the scope of the EU’s GMO Directive and are subject to its full regulatory requirements.⁴ This placed gene-edited crops under the EU’s full GMO authorisation regime and materially constrained commercial cultivation. The European Parliament voted in February 2024 to support a revised framework for new genomic techniques. In December 2025, Parliament and the Council reached a provisional agreement distinguishing between NGT-1 plants (equivalent to conventionally bred organisms, exempt from most GMO requirements) and NGT-2 plants (with more extensive modifications, subject to full GMO assessment). The Council adopted the agreed text on 21 April 2026 and the European Parliament adopted the rules on 17 June 2026. Regulation (EU) 2026/1388 was published in the Official Journal on 26 June 2026, enters into force on 16 July 2026 and applies from 17 July 2028, with Articles 29, 30 and 31 applying from 16 July 2026.⁴

The United Kingdom followed a distinct route. The Genetic Technology (Precision Breeding) Act 2023 received Royal Assent in March 2023. The implementing regulations took effect on 13 November 2025, creating a streamlined authorisation pathway for precision-bred organisms in England where the genetic modification could have been achieved through conventional breeding.¹⁰ The Act applies to England only. No equivalent precision-breeding regime currently applies in Scotland, Wales or Northern Ireland. In England, authorised precision-bred food and feed are not treated as GMO products for the purposes of the new regime. In Scotland and Wales, such organisms remain within GMO classification, but UK Internal Market Act principles may allow products lawfully marketed in England to be sold there; UKIMA does not apply to further processing after sale. Northern Ireland is different: under the Windsor Framework, precision-bred products authorised in England cannot currently be sold in Northern Ireland, and precision-bred products may not undergo significant processing, including cultivation, there without satisfying the applicable Northern Ireland/EU regime. The regulatory divergence within a single state adds a further layer of fragmentation to the transatlantic divide.

The cumulative effect is that a gene-edited crop variety may be cleared under a streamlined pathway in England, face an uncertain domestic regulatory path in the United States following the SECURE vacatur, remain classified as a GMO in Scotland and Wales subject to UKIMA market-access issues, be restricted in Northern Ireland under the Windsor Framework, and fall under the new NGT framework in the EU. For global agricultural commodity traders, the compliance burden may include segregation, identity preservation, traceability and jurisdiction-specific documentation for gene-edited product. AI-enabled traceability systems may assist with that task, but the obligation will depend on the product, market and applicable regulatory classification.

The Right to Repair and Data Ownership

The right-to-repair movement in agriculture addresses the question of who controls the equipment after the sale. The dispute arose from the allegation that Deere’s software restrictions limited farmers’ and independent repair providers’ ability to diagnose and repair equipment without Deere-authorised tools, software or dealers.⁵

In January 2023, the American Farm Bureau Federation and John Deere signed a memorandum of understanding in which Deere committed to making diagnostic and repair tools available to farmers and independent repair providers.⁵ The MOU did not end the dispute. In January 2025, the Federal Trade Commission, together with Illinois and Minnesota, sued Deere, alleging that its repair restrictions forced farmers towards Deere-authorised dealers. In July 2026, the FTC and five states secured a settlement requiring Deere, for 10 years and under public supervision, to provide farmers and independent repair providers with repair resources equivalent to those available to authorised Deere dealers. That public enforcement settlement followed Deere’s separate USD 99 million private class-action settlement earlier in 2026.⁵ Several US states have enacted or introduced right-to-repair legislation covering agricultural equipment. Colorado’s Consumer Right to Repair Act (2023) was the first state law to cover agricultural equipment explicitly.⁵

The right-to-repair question intersects with liability in a direct way. If a farmer repairs autonomous navigation software without manufacturer authorisation and the equipment subsequently causes injury, the manufacturer will argue that the unauthorised modification voids its liability. If the manufacturer restricts repair access in a way that forces the farmer to operate defective equipment while waiting for authorised service, the manufacturer’s restriction may itself ground a claim. The EU’s right-to-repair directive, adopted in 2024, is primarily a consumer-goods measure. It does not create a general commercial repair regime for autonomous agricultural machinery, although the wider repair-access debate will influence agricultural-equipment regulation and contracting.

The data question is equally unresolved. Precision agriculture systems generate vast quantities of operational data: soil conditions, moisture levels, yield maps, input application rates, weather responses. This data has commercial value for seed companies, input suppliers, commodity traders and insurers. The language of “ownership” remains unsettled, but the access position is no longer wholly unregulated. In the EU, the Data Act, which applies from 12 September 2025, gives users of connected products rights to access and share certain data generated by those products.¹¹ That does not resolve every proprietary or competitive question around farm data, but it materially changes the legal analysis for EU-connected agricultural machinery. In the United States, the American Farm Bureau Federation’s Privacy and Security Principles for Farm Data provide a voluntary framework, but voluntary frameworks do not survive a commercial dispute over data access.

AI-Enabled Crop Management and Environmental Liability

AI systems that optimise fertiliser and pesticide application promise to reduce chemical inputs by applying them only where and when they are needed. Variable-rate application technology, guided by AI analysis of drone imagery, soil sensors and crop-growth models, can materially reduce herbicide use in appropriate applications compared with uniform broadcast application.⁶ The environmental case for precision agriculture is strong.

The liability dimension arises in two directions. If an AI crop-management system under-applies pesticide and a pest outbreak destroys a crop, the farmer may look to the system provider, agronomist or platform vendor, depending on the contract, the recommendation made, the user settings and the extent of any human override. If the system over-applies or misapplies a pesticide and the chemical contaminates an adjacent waterway or harms a neighbouring organic farm, the exposure may involve the farmer who applied the chemical, the platform provider whose recommendation was followed and, in some cases, the chemical manufacturer if the product was applied outside its labelled use parameters or the label warnings were inadequate.

Organic certification is particularly sensitive to chemical contamination. An organic farm that loses its certification because AI-guided spraying on an adjacent conventional farm caused drift contamination may pursue damages that include not only the immediate crop loss but the multi-year revenue impact of lost organic status. The legal categories are familiar — nuisance, negligence, statutory environmental obligations and product-related claims — but proof of causation, drift pathway, algorithmic recommendation, label compliance and certification loss may be heavily contested.

Strategic Outlook

Agriculture’s adoption of autonomous and AI-enabled technology is proceeding under regulatory frameworks that range from layered and technology-specific to fragmented and technology-neutral. The European Union’s layered approach, combining the Machinery Regulation’s autonomous-equipment provisions with the AI Act’s high-risk classification, represents the most structured regime. The United Kingdom has legislated for road vehicles but not for agricultural equipment on private land. The United States has no federal framework for autonomous agricultural equipment at all. Gene-edited crops face a patchwork of national and sub-national regulations that range from England’s streamlined precision-breeding pathway to full GMO-equivalent oversight in Scotland, Wales and Northern Ireland, and, until the NGT regulation applies, the EU. Farm data accumulates in proprietary platforms. Ownership remains unsettled, but access is no longer wholly unregulated: in the EU, the Data Act gives users of connected products rights to access and share certain product-generated data, while the United States still relies largely on contract and voluntary principles.

A serious autonomous-equipment incident would not simply transfer liability from farmer to manufacturer. It would test the allocation of responsibility between the farm operator who deployed the machine, the person supervising it remotely, the manufacturer, the software provider, the dealer or service network and any regulatory regime governing the machine’s operation at the point of incident. Beyond that single event, the convergence of gene-editing and AI-enabled crop management is creating integrated agricultural systems in which a single growing-season decision chain involves intellectual property licences, autonomous equipment, algorithmic input recommendations and regulatory compliance across multiple frameworks. Disputes over access, permitted use and competitive exploitation of farm data are likely to arise as its commercial value increases and the number of parties with platform access multiplies.

Farming is one of law’s oldest commercial environments. It is now absorbing the same autonomous and algorithmic technologies that are transforming shipping, mining, aviation and energy. The liability framework that emerges will reflect agriculture’s distinctive features. Farms are open environments adjacent to public spaces, managing biological systems that respond unpredictably to inputs, under regulatory frameworks that vary between nations and within them. The legal infrastructure is now being asked to allocate risks that were previously absorbed informally by farmers, contractors, dealers and seasonal labour.

Notes

1. John Deere CES 2025 autonomous machines announcement: 9RX tractor (large-scale tillage) and 5ML orchard tractor (air-blast spraying) with second-generation autonomy kit; 5ML adds LiDAR for dense orchard canopies. 8R autonomous tractor with tillage launched CES 2022. By 2026, Deere marketing autonomous tillage and orchard-spraying capability; availability varies by product line, geography and deployment configuration. Computer vision, AI and camera-based perception; remote operator monitoring via Operations Center Mobile (PR Newswire, 6 January 2025; Deere.com; Forbes, 16 January 2025).

2. CRISPR-Cas9 patent dispute: Broad Institute of MIT and Harvard (Feng Zhang) vs University of California, Berkeley (Jennifer Doudna and Emmanuelle Charpentier). PTAB ruled February 2022 that Broad Institute had priority for eukaryotic cell applications. Federal Circuit remanded 12 May 2025, finding PTAB applied wrong conception standard (Berkeley News). PTAB reaffirmed 26 March 2026 (IPWatchdog). Both patent families coexist; agricultural applications may require engagement with multiple patent portfolios, depending on jurisdiction, claim scope, product design and licensing route. Corteva Agriscience and the Broad Institute have jointly offered non-exclusive agricultural licences since October 2017 (Corteva; Simplot press release, 2018).

3. Supporting Innovation in Agriculture Act of 2025, HR 1705, 119th Congress, introduced 27 February 2025; defined precision agriculture technology to include software with data-management and AI systems, GPS guidance, sensors and variable-rate technology (Congress.gov). State “right to farm” statutes provide varying degrees of protection for agricultural operations.

4. USDA SECURE rule adopted 2020 (phased implementation through October 2021), exempting gene-edited plants achievable through conventional breeding from transgenic regulatory oversight. US District Court, Northern District of California, vacated SECURE rule 2 December 2024 under APA (prospective vacatur; crops already reviewed not affected); following the vacatur, APHIS’s pathway for gene-edited plants required renewed analysis under the reinstated biotechnology framework at 7 CFR Part 340 (2019). APHIS programme update; Hogan Lovells analysis. CJEU Case C-528/16 (2018), gene-edited organisms fall within EU GMO Directive. European Parliament vote February 2024 supporting revised NGT framework; Parliament/Council provisional agreement December 2025 distinguishing NGT-1 (exempt from most GMO requirements) and NGT-2 (full GMO assessment). European Parliament press room, Consilium press release reporting. Council adopted the new rules on 21 April 2026 (Consilium press release); Parliament gave final approval on 17 June 2026 (European Parliament press room). Regulation (EU) 2026/1388 published in the Official Journal on 26 June 2026; enters into force 16 July 2026; applies from 17 July 2028, with Articles 29, 30 and 31 applying from 16 July 2026 (EUR-Lex).

5. American Farm Bureau Federation / John Deere memorandum of understanding, signed 8 January 2023, on diagnostic and repair tool access (AFBF; Iowa State CALT). FTC, together with Illinois and Minnesota, sued Deere January 2025 over repair restrictions (FTC press release, 14 January 2025). FTC and five states secured settlement with Deere 8 July 2026 requiring 10 years of equivalent repair-resource access for farmers and independent providers (FTC press release, 8 July 2026). Separate private class-action settlement of USD 99 million earlier in 2026, Deere denying wrongdoing (AP News). Colorado Consumer Right to Repair Act (HB23-1011, 2023), first US state law covering agricultural equipment (Dentons). EU right-to-repair directive adopted 2024.

6. Variable-rate application technology materially reducing herbicide use compared with uniform broadcast application. A 2024 robotic spot-spraying field trial reported 35% average herbicide reduction in sugarcane, with up to 65% reduction in lower-weed-pressure strips (Subeesh et al., arXiv:2401.13931, 2024). AI analysis of drone imagery, soil sensors and crop-growth models for precision input management.

7. Regulation (EU) 2023/1230 on machinery products, published 29 June 2023, replacing Machinery Directive 2006/42/EC from 20 January 2027. New requirements for autonomous mobile machinery include mandatory supervisory function, defined working areas (physical borders or obstacle detection) and risk assessment for machines with self-evolving behaviour after placement on the market. Safety components with fully or partially self-evolving behaviour using machine-learning approaches and ensuring safety functions fall within Annex I Part A and require notified-body conformity assessment (CEMA; EUR-Lex). Harmonised standards list expected by end of 2026.

8. EU AI Act (Regulation (EU) 2024/1689). Annex I lists Regulation (EU) 2023/1230 and Regulation (EU) No 167/2013. Under Article 6, an AI system is high-risk where it is a product, or safety component of a product, covered by Annex I legislation and the relevant product or safety component is subject to third-party conformity assessment. Current Commission guidance states that rules for systems integrated into products such as robotics and industrial machinery apply from 2 August 2028 (European Commission guidelines on high-risk classification; artificialintelligenceact.eu).

9. Automated Vehicles Act 2024 (c. 10), received Royal Assent 20 May 2024. Establishes regulatory framework for automated vehicles on roads and in other public places in Great Britain, covering SAE Level 3 and Level 4 automation. Commencement No. 1 Regulations 2025 (SI 2025/1339). The Act does not provide a bespoke regime for vehicles operating exclusively on private land (legislation.gov.uk; Hogan Lovells analysis).

10. Genetic Technology (Precision Breeding) Act 2023 (c. 6), Royal Assent 23 March 2023. Genetic Technology (Precision Breeding) Regulations 2025, in force 13 November 2025. Creates streamlined authorisation pathway for precision-bred organisms in England where the genetic modification could have been achieved through conventional breeding. Applies to England only; Scotland and Wales retain GMO classification. Precision-bred products lawfully marketed in England may be sold in Scotland and Wales under UKIMA market-access principles, but cannot be grown or substantially processed there without devolved legislation. Northern Ireland remains distinct under the Windsor Framework; precision-bred products cannot currently be sold there under the England-only regime (Defra; Morrison Foerster; Chemistry World; House of Commons Library Research Briefing CBP-9557; UK Explanatory Memorandum on Regulation (EU) 2026/1388).

11. EU Data Act (Regulation (EU) 2023/2854), applicable from 12 September 2025. Gives users of connected products rights to access data generated by those products and to share it with third parties, subject to trade-secret and competitive-sensitivity protections. Applies to agricultural machinery as connected products (European Commission, Digital Strategy).

Introduction

In August 2024, Rio Tinto accepted its 300th autonomous-haulage-system truck for its Pilbara operations, with the fleet distributed across ten Australian mine sites.¹ The initial autonomous-haulage trial began in 2008. By the 2024 milestone, the fleet had completed 8.9 million operating hours and moved more than 4.8 billion tonnes of material. For scale, a Komatsu 930E-5SE has a rated gross vehicle weight of 521.6 tonnes.¹ BHP operates a comparable autonomous fleet at its Jimblebar and Goonyella mines.² Caterpillar and Komatsu supply the vehicles and the autonomous haulage systems that control them. Autonomous haulage is now deployed at scale across mines in Australia, Canada, Chile and China.³

The safety record has been strong. BHP reported a 65% reduction in events with fatal potential across its Western Australian Iron Ore operations between FY2018 and FY2022, attributed in part to autonomous haulage deployment.² But it is not unblemished. In August 2023, an autonomous haul truck at BHP Mitsubishi Alliance’s Goonyella open-pit coal mine in Queensland collided with a manually operated excavator after a loss of communications on the autonomous circuit.⁴ No one was injured.⁴

In a separate Pilbara incident in November 2018, a BHP iron-ore train stopped automatically after a communications failure. The driver left the cab to apply handbrakes to individual wagons, after which a timed shutdown sequence released the locomotive brakes and the train rolled more than 90 kilometres before being deliberately derailed near Port Hedland.⁵ That was a conventional runaway rather than an autonomous system failure, but it illustrates the consequence of assumptions about machine control at industrial scale.

The legal framework governing autonomous mining equipment sits at the intersection of workplace health and safety regulation, product liability, environmental law and the contractual allocation of risk between mine operators and technology suppliers. Western Australia published a dedicated Code of Practice for Safe Mobile Autonomous Mining in 2015, updated most recently in February 2025.⁶ Queensland has its own guidance framework.⁷

Autonomous Haulage and the Duty Holders

Autonomous haulage was driven by safety as much as by economics. Mining is among the most hazardous industrial activities. Haul truck rollovers, collisions and pedestrian strikes account for a significant proportion of mine fatalities worldwide.

The automation that reduces aggregate risk creates a different category of dispute when it fails. A conventional haul-truck accident engages the operator’s statutory duties, potential regulatory enforcement and, in serious cases, prosecution of the corporate operator and relevant officers or other duty holders. An autonomous-system failure distributes the analysis across multiple participants. The mine operator selected and deployed the system. The technology supplier may have designed or supplied the autonomous-control software. The equipment manufacturer built the truck. Where separately engaged, a systems integrator configured the system for the particular mine site. The mine’s safety management system defined the operating parameters, exclusion zones and interaction protocols between autonomous and human-operated equipment.

Western Australia’s Code of Practice, originally approved under the former Mines Safety and Inspection Act 1994, continues in force as a transitional code of practice under the Work Health and Safety Act 2020.⁶ It allocates interlocking responsibilities to system builders and system operators, while site-specific obligations remain non-exclusive. Operators, designers, manufacturers, importers and suppliers may each carry distinct statutory, contractual and tort obligations regarding plant design, safety information and risk management.⁸ The question in any autonomous mining dispute will be which duty holder’s conduct, omission or system-design decision was causally relevant to the harm.

Queensland’s guidance takes a similar outcome-based approach, requiring proactive risk assessment before deployment and ongoing monitoring of autonomous system performance.⁷ QGN33 is a guidance note rather than a Recognised Standard. It assists mine operators to meet their legislative obligations but does not displace the statutory requirement to keep risk within acceptable limits and as low as reasonably achievable. Neither instrument was drafted as a bespoke regime for machine-learning models that are periodically retrained. Both nevertheless address change management, system updates and upgrades, verification and validation, communications integrity and cyber-security. The unresolved issue is whether those broad controls are sufficiently granular for model versioning, data drift, retraining validation and post-deployment monitoring.

Tailings Monitoring and the Brumadinho Legacy

The Fundao tailings dam at Mariana, operated by Samarco (a joint venture between BHP and Vale), collapsed on 5 November 2015, killing nineteen people.⁹ The environmental contamination extended across 600 kilometres of river. In October 2024, BHP, Vale, Samarco and Brazilian public authorities executed a reparation agreement valued at R$170 billion (approximately USD 31.7 billion), covering prior expenditure and future remediation obligations.⁹ That agreement addressed the public-authority claims.

Separately, over 600,000 claimants, including individuals, businesses, institutions, municipalities, utilities and Indigenous and Quilombola communities, brought proceedings against BHP in the English courts. In November 2025, the English High Court found BHP liable under Brazilian statutory environmental liability, including the polluter principle and strict liability under the applicable environmental legislation, and on fault-based grounds under the Brazilian Civil Code.¹⁰ In May 2026, the Court of Appeal refused BHP permission to appeal on the principal liability grounds, though a narrow costs-interest issue remained.¹⁰

The Brumadinho tailings dam in Minas Gerais, also operated by Vale, collapsed on 25 January 2019, killing 270 people.¹¹ Brumadinho reinforced scrutiny of tailings monitoring, governance and escalation arrangements across the industry. The Global Industry Standard on Tailings Management, launched in 2020, established the first global standard for tailings management and requires integrated monitoring arrangements.¹¹ Vale, for example, has operated Geotechnical Monitoring Centres since 2019, using radar, robotic stations, automated piezometers, satellite monitoring, inspection drones and video cameras using artificial intelligence.¹¹

The legal question generated by AI-assisted monitoring turns on knowledge and response. Once a monitoring system is deployed, its alarms, calibration records, data-retention arrangements and escalation procedures may become central evidence of what the operator knew, or ought reasonably to have known, and whether its response met the applicable statutory, contractual and operational standard. If a monitoring system detects anomalous piezometric readings consistent with internal erosion but the mine operator does not evacuate or modify operations, the failure to act on the system’s output will be examined against the operator’s permit conditions, safety case and escalation protocols. The converse question is also live: if a monitoring system fails to detect instability that a competent engineer would have identified, the adequacy of the system and the operator’s reliance on it without sufficient human oversight will both be in issue.

Automated monitoring, sensor networks and dashboard alerts should not be conflated with AI. The distinct legal questions arise where an algorithmic model interprets data, predicts instability, prioritises risk or recommends operational intervention beyond rules-based alerting.

The English proceedings arising from Mariana were determined under Brazilian substantive law and are not, in themselves, a new English-law parent company duty decision. Where English law governs, Vedanta Resources plc v Lungowe confirms that parent exposure turns on ordinary duty of care principles applied to the parent’s own conduct.¹² A group-wide mandate of a monitoring platform may be relevant to that inquiry, but it will not be determinative. The deployment of AI monitoring systems across a global mining group adds a dimension: if the parent specifies a particular platform across its operations and that platform proves inadequate, the parent’s selection, specification and oversight of the technology may form part of the factual matrix in which an ordinary duty of care analysis is conducted.

AI in Geological Modelling and Exploration

AI is now used across the exploration lifecycle. Machine-learning models integrate geological, geophysical and geochemical data to identify prospective mineral deposits. ALS Geoanalytics, formerly ALS GoldSpot and GoldSpot Discoveries, and other specialist providers offer AI-assisted exploration and targeting tools.¹³ Caterpillar’s MineStar suite illustrates a different part of the value chain: autonomous operations, fleet management and operational data analysis. Specialist providers are marketing AI-assisted tools for drill-hole targeting, ore-body modelling and resource-estimation workflows, but adoption rates vary by commodity, jurisdiction and the maturity of the underlying geological data.

Mining companies raise capital on the basis of resource and reserve estimates disclosed under reporting codes, including JORC in Australia, NI 43-101 in Canada and SAMREC in South Africa. These regimes require relevant technical disclosure to be prepared by, under the supervision of or, where the applicable instrument permits, approved by a designated Competent Person or Qualified Person, subject to their respective consent, certification and disclosure requirements.¹⁴ Professional sign-off, the scope of the professional’s supervision, applicable disclosure obligations, investor reliance, causation and the relevant securities law will together determine the exposure of each participant if an AI-assisted estimate proves materially wrong. If the responsible professional relies on an AI-generated geological model without appropriate validation of the model’s inputs, assumptions, performance and limitations, that reliance will itself be examined against the professional standard.

The risk is established in conventional geological misrepresentation. Bre-X illustrates how false or manipulated geological results can generate investor claims and securities litigation.¹⁵ AI does not alter the underlying disclosure framework, but it adds questions about model validation, data provenance, version control and the disclosure of material limitations to investors.

Strategic Outlook

Autonomous mining is currently governed through general workplace-safety duties, autonomous-mining codes and guidance, site-specific safety arrangements and supplier obligations, rather than a bespoke regime for model governance. A serious incident would test how those overlapping obligations apply to software updates, sensor failures, degraded communications, inadequate escalation and the allocation of risk within the operator–supplier contract stack.

A fatal autonomous-mining incident could generate litigation concerning the respective obligations of the mine operator, technology supplier, equipment manufacturer and, where relevant, systems integrator, against the applicable regulatory framework and the site-specific safety arrangements governing the deployment. Beyond haulage, the expansion of AI-assisted tools into geological modelling, environmental monitoring and tailings management may give rise to claims under securities law and environmental regulation and, on appropriate facts, parent-company duty principles. The regulatory frameworks in Western Australia and Queensland address change management, system updates and verification, but whether those controls are sufficiently granular for AI-specific risks remains untested.

The disputes will not turn on whether a mine used AI in the abstract. They will turn on system design, approved operating limits, version control, sensor integrity, change management, human override, alarm escalation and the allocation of responsibility across the operator-supplier contract stack.

Notes

1. Rio Tinto accepted its 300th autonomous haul truck in the Pilbara in August 2024, across ten mine sites; by that milestone the fleet had completed 8.9 million operating hours and moved more than 4.8 billion tonnes of material (Komatsu media release, 12 August 2024). Komatsu 930E-5SE rated gross vehicle weight 521.6 tonnes. Autonomous haulage trials at Rio Tinto Pilbara operations since 2008 (Rio Tinto).

2. BHP Western Australian Iron Ore operations; 65% reduction in events with fatal potential FY2018–2022, attributed in part to autonomous haulage deployment (Haight & Burgess-Limerick, CDC/NIOSH, 2023). BHP autonomous fleet deployed at Jimblebar and Goonyella mines.

3. GlobalData’s Mining Intelligence Center tracked 3,832 autonomous haul trucks operating at surface mines globally in July 2025, including systems classified as autonomous-ready as well as those operating autonomously. China accounted for 2,090 units, followed by Australia, Canada and Chile (GlobalData, Development of Autonomous Trucks in the Global Mining Sector, 2025).

4. BHP Mitsubishi Alliance, Goonyella open-pit coal mine, Queensland, August 2023; autonomous truck collided with manually operated excavator following loss of communications on autonomous circuit; no injuries (Mining Monthly, January 2024).

5. BHP Pilbara iron-ore train runaway, 5 November 2018. Train stopped automatically on communications failure; driver left cab to apply handbrakes to individual wagons; 60-minute locomotive shutdown sequence subsequently released brakes; train travelled more than 90 km before controlled derailment near Port Hedland. ATSB investigation report RO-2018-018.

6. Western Australia Code of Practice for Safe Mobile Autonomous Mining, WorkSafe WA, first published 2015, updated 17 February 2025. Originally approved under the Mines Safety and Inspection Act 1994; continues in force as a transitional code of practice under the Work Health and Safety Act 2020 (WorkSafe WA).

7. Queensland Guidance Note QGN33, Autonomous mobile machinery and vehicle introduction in coal mines (Resources Safety & Health Queensland).

8. Under WA’s Work Health and Safety Act, duties extend to persons conducting a business or undertaking, designers, manufacturers, importers and suppliers of plant, each with obligations regarding design safety, information, installation and use (WorkSafe WA).

9. Fundao tailings dam (Samarco/BHP/Vale), Mariana, collapse 5 November 2015; 19 fatalities. Reparation agreement with Brazilian public authorities signed 25 October 2024, ratified by the Brazilian Supreme Federal Court 6 November 2024; valued at R$170 billion (approx. USD 31.7 billion), covering prior expenditure and future remediation obligations (BHP media release, November 2024).

10. Municipio de Mariana & Ors v BHP Group (UK) Ltd [2025] EWHC 3001 (TCC), judgment 14 November 2025; BHP found liable under Brazilian statutory environmental liability (polluter principle, strict liability) and on fault-based grounds under the Brazilian Civil Code; over 600,000 claimants. BHP Group (UK) Ltd v Municipio de Mariana & Ors [2026] EWCA Civ 502; Court of Appeal refused BHP permission to appeal on principal liability grounds, May 2026; narrow costs-interest issue remained.

11. Brumadinho tailings dam (Vale), Minas Gerais, collapsed on 25 January 2019, causing 270 fatalities; two victims remain missing. Global Industry Standard on Tailings Management launched 5 August 2020 by UNEP, PRI and ICMM. Vale Geotechnical Monitoring Centres operating since 2019, using radar, robotic stations, automated piezometers, satellite monitoring, inspection drones and video cameras using artificial intelligence (Vale, “Vale concludes de-characterization of the first of nine upstream dams as announced earlier this year”, 27 November 2019).

12. Vedanta Resources PLC v Lungowe [2019] UKSC 20; parent-company liability in English law turns on ordinary duty-of-care principles, including the parent’s own conduct, control, group policies and assumption of responsibility.

13. ALS Geoanalytics (formerly ALS GoldSpot and GoldSpot Discoveries; renamed October 2024) offers AI-assisted exploration and targeting tools; Caterpillar MineStar suite covers autonomous operations, fleet management and operational data analysis. Precise industry-wide adoption rates vary by survey methodology and definition.

14. JORC (2012) and SAMREC require a Competent Person; NI 43-101 requires a Qualified Person. Relevant technical disclosure must be prepared by, or under the responsibility or supervision of, that person. Issuers and directors or officers may bear separate disclosure responsibility under applicable securities law.

15. McNamara v Bre-X Minerals Ltd, 57 F Supp 2d 396 (ED Tex 1999); securities-fraud claims arising from alleged fabrication of geological assay results at the Busang gold deposit, Indonesia.

The federal-state boundary for prediction markets is unsettled, but it is not yet the subject of an appellate circuit split. One US court of appeals has ruled on whether the Commodity Exchange Act pre-empts state gambling law as applied to sports-event contracts. The other courts positioned to answer have not. The recent appellate orders that read like a contrary signal in fact decided something narrower.

The Third Circuit’s ruling and its limits

In KalshiEX LLC v Flaherty, No. 25-1922, the Third Circuit on 6 April 2026 affirmed a preliminary injunction preventing New Jersey from applying its gambling laws to Kalshi’s sports-event contracts.[1] Writing for a 2-1 majority, Judge Porter, joined by Chief Judge Chagares, held that Kalshi had demonstrated a reasonable chance of success on its argument that the Commodity Exchange Act pre-empts the relevant application of New Jersey law, and that both field and conflict pre-emption applied. The posture is important. The court granted that relief on a likelihood-of-success standard, before any trial on the merits. Judge Roth dissented, invoking the presumption against pre-emption with “special force” in gambling regulation and treating the products as functionally identical to online sportsbooks.

Why the Ninth Circuit’s May orders are not a contrary answer

It is tempting to read the Ninth Circuit as having gone the other way. It has not done so yet. The court heard the consolidated Nevada appeals involving Kalshi, Crypto.com and Robinhood on 16 April 2026.[2] Separately, in May, it denied interim stays pending appeal from remand orders. In the Washington matter the panel reasoned that CEA pre-emption is an affirmative defence and therefore does not itself create federal-question jurisdiction for removal.[3] Those orders let the remands take effect and left the state-enforcement actions in state court. They did not decide whether the CEA ultimately pre-empts the relevant state gambling laws. The current landscape is best described as an emerging inter-district and inter-state conflict rather than an appellate circuit split. The Fourth and Sixth Circuits are weighing the same issue, the latter on appeals from conflicting Tennessee and Ohio decisions, so a genuine split could still form.

The savings clauses, the criminal front and what a rule cannot do

The sharpest intra-panel disagreement concerns the CEA’s savings clauses. The majority treated them as preserving state common-law actions and state-court jurisdiction without retaining concurrent state regulatory authority over trading on a CFTC-registered designated contract market. Judge Roth regarded the clauses as incompatible with complete field pre-emption. That disagreement, rather than any clash between circuits, is where the doctrine is currently unsettled.

Criminal enforcement runs alongside the civil dispute. On 17 March 2026 Arizona brought a 20-count misdemeanour criminal information against Kalshi entities alleging unlicensed gambling activity, four counts of which concerned election-event contracts, including the 2028 presidential election.[4] The CFTC and the United States answered with state-specific pre-emption actions, beginning with Arizona, Connecticut and Illinois on 2 April 2026 and extending to further states, most recently Kentucky on 23 June 2026.[5] In Arizona, federal relief temporarily restrained the State from enforcing its gambling laws against event contracts listed on CFTC-regulated markets while the pre-emption question is litigated.

The CFTC has now entered the same boundary dispute through rulemaking. On 10 June 2026 it announced a notice of proposed rulemaking to amend Regulation 40.11 and introduce Appendix F governing event contracts, published in the Federal Register on 12 June with comments due by 27 July.[6] The proposal concerns how the agency treats event contracts under its public-interest framework, including a proposed definition of “gaming” and a proposed standard for when an event contract “involves” an underlying activity. It does not amend the statutory definition of “swap” in 7 U.S.C. § 1a(47), nor the CEA’s jurisdictional and savings-clause provisions. The proposal cannot itself conclusively resolve whether the CEA pre-empts a particular application of state gambling law. The narrower question is what persuasive weight a reviewing court will give the CFTC’s eventual reasoning when construing the statute.

Dodd-Frank expressly addressed event contracts and authorised a special CFTC review process for contracts involving gaming. The statute anticipated the category. The harder issue is whether the federal regime for trading those contracts displaces state gambling law where the products functionally resemble sportsbook wagers. Congress may yet redraw the line. The proposed Prediction Markets Are Gambling Act, introduced on 23 March 2026, would amend the CEA to prohibit CFTC-registered entities from listing specified sports and casino-style event contracts.[7] Until Congress acts or an appellate court resolves the merits with a nationally controlling answer, the position remains state-by-state and court-by-court, and the comment period is the immediate formal opportunity for exchanges, platforms, state regulators and market participants to shape the federal framework.

[1] KalshiEX, LLC v Flaherty, 172 F.4th 220 (3d Cir. 2026) (No. 25-1922), 2-1 (Porter, J., joined by Chagares, C.J.; Roth, J., dissenting), affirming a preliminary injunction. Swap definition: Commodity Exchange Act, 7 U.S.C. § 1a(47).

[2] KalshiEX, LLC v Hendrick, No. 25-7516 (9th Cir., argued 16 April 2026), consolidated Nevada appeals heard with the Crypto.com and Robinhood matters.

[3] Washington v KalshiEX, LLC, No. 26-3106 (9th Cir., order denying stay pending appeal, 21 May 2026).

[4] State of Arizona, 20-count misdemeanour criminal information against KalshiEX LLC and Kalshi Trading LLC, 17 March 2026; four counts concerned election-event contracts, including the 2028 presidential election.

[5] CFTC and the United States, state-specific pre-emption actions, commencing against Arizona, Connecticut and Illinois on 2 April 2026 and extending to further states, including Kentucky, the ninth state as at 23 June 2026. Arizona criminal proceeding temporarily restrained by federal relief.

[6] Prediction Markets; Public Interest Determinations, 91 Fed. Reg. 35,806 (proposed 12 June 2026) (amendments to Regulation 40.11; proposed Appendix F; comments due 27 July 2026).

[7] Prediction Markets Are Gambling Act (introduced 23 March 2026; Sens. Schiff and Curtis), proposing to amend the CEA to prohibit CFTC-registered entities from listing specified sports and casino-style event contracts.

Introduction

In May 2026, LOT Polish Airlines became the first airline to take Boeing to a jury trial over the 737 MAX grounding.¹ The airline sought approximately USD 153 million in damages arising from the worldwide grounding of the MAX fleet following the Lion Air and Ethiopian Airlines crashes that killed 346 people. The jury found that LOT had failed to substantiate its claims of purposeful misrepresentation regarding Boeing’s disclosure of the Maneuvering Characteristics Augmentation System.¹ The verdict was a defence win for Boeing on the airline’s commercial claims, but it resolved nothing about the underlying automated-systems liability that produced the two crashes.

Publicly reported MAX-related DOJ resolutions alone exceed USD 3.6 billion, before taking account of confidential civil settlements. The federal criminal case was dismissed in November 2025 after the Department of Justice requested dismissal. As part of the resolution, Boeing agreed to pay or invest an additional USD 1.1 billion in fines, victim family compensation and internal safety measures.¹ Separately, Boeing has resolved more than 90% of the individual civil complaints arising from the two crashes. The wrongful death trial of Michael Ryan is scheduled to commence on 3 August 2026.¹ A jury awarded USD 49.5 million to the family of Samya Stumo, killed in the Ethiopian Airlines crash, in May 2026.¹

The 737 MAX is not an AI case. MCAS was a deterministic automated system that responded to angle-of-attack sensor data according to pre-programmed logic. It did not learn or adapt. It executed its programming and its programming was fatally flawed. But the liability framework that the MAX litigation has constructed applies directly to the next generation of aviation AI: adaptive flight-management systems, AI-enabled predictive maintenance, machine-learning-based air traffic management and the autonomous flight capabilities being developed for the eVTOL air taxi sector. The allocation question is the same in each context: when an automated system fails and people die, how does the law distribute responsibility between the manufacturer, the certifier, the operator, the software supplier and the system integrator?

The Certification Gap: FAA and EASA

Aviation is the most heavily regulated transport sector. Aircraft certification requires demonstration of compliance with airworthiness standards through a process that can take years and cost hundreds of millions of dollars. The certification framework was designed for deterministic systems: mechanical and electronic components that behave predictably and can be tested exhaustively against failure-mode analysis.

AI-enabled systems strain this model. A machine-learning system whose behaviour depends on training data, operational context and later updates cannot be characterised in the same way as a conventional deterministic component. The European Union Aviation Safety Agency recognised this in its AI Roadmap and in November 2025 published Notice of Proposed Amendment 2025-07, the first regulatory proposal for AI trustworthiness in aviation.² The NPA distinguishes between Level 1 AI (AI-based assistance, where the human retains full authority) and Level 2 AI (human-AI teaming, where the AI shares decision-making with the pilot).² The consultation closed in March 2026, after an extension from the original February deadline, and a second NPA addressing domain-specific regulations is expected later in 2026.²

The FAA has taken a slower path. Its Roadmap for Artificial Intelligence Safety Assurance sets out principles for the safety assurance of AI in aircraft and aircraft operations, but has not produced binding certification standards for AI in flight-critical systems.² SAE G-34/EUROCAE WG-114 is developing ARP6983/ED-324 as a recommended practice for AI/ML in aeronautical systems, but the first version is limited and does not solve certification of the highest-criticality adaptive systems.² The current scope extends to Design Assurance Level C, corresponding to “major” failure conditions. It does not cover DAL-A (catastrophic failure) or DAL-B (hazardous failure), which govern the systems most likely to kill people.

The gap between what AI can do in aviation and what regulators can certify is widening. The technology is advancing more quickly than the regulatory framework that must validate it for flight.

The eVTOL Certification Race

Electric vertical takeoff and landing aircraft are the first new commercial aviation category in which highly automated flight-control software, autonomy roadmaps and certification constraints converge from the outset. The eVTOL sector has attracted approximately USD 13 billion in investment since 2019.³ Four concurrent certification applications are being processed by the FAA: Joby Aviation, Archer Aviation, Beta Technologies and Wisk Aero.³

In November 2025, Joby began power-on testing of the first FAA-conforming aircraft being built for Type Inspection Authorization testing. In March 2026, it began flight testing the first aircraft intended for FAA certification testing, with FAA pilots expected to conduct “for credit” TIA testing later in 2026.³ As of the first quarter of 2026, no US eVTOL manufacturer has received a full type certificate for commercial passenger operations.³

Lilium’s principal German operating subsidiaries filed for insolvency in late October 2024, and Lilium N.V. later authorised insolvency proceedings after failed fundraising efforts.³ The company had received EASA Design Organisation Approval in November 2023 but pursued a novel jet-propulsion architecture that fell outside any existing certification framework, resulting in extended timelines and unsustainable cash burn. Lilium’s failure demonstrates that the certification pathway is itself a commercial risk of the first order: a technically viable aircraft that runs out of capital before it completes certification cannot generate revenue.

The product liability implications for eVTOL are significant. These are new-category aircraft operating in urban environments over populated areas. The first passenger fatality will produce litigation in which the manufacturer, the battery supplier, the flight-control software provider and the operator will all be in play, while the FAA’s certification decision may form part of the factual and regulatory background. The automated flight-control systems that manage hover-to-cruise transition, motor failure redistribution and autonomous emergency landing are the systems most likely to be at the centre of that litigation.

AI in Maintenance and Predictive Failure

AI-enabled predictive maintenance is one of the earliest and most commercially advanced applications of machine learning in aviation. Airlines and MRO (maintenance, repair and overhaul) providers use AI systems to analyse engine sensor data, flight-data recorder outputs, component wear patterns and environmental exposure data to predict when parts will fail and schedule maintenance before failure occurs.

The hard issue for courts will arise when a predictive system fails to predict. If an AI maintenance system assesses a turbine blade as having adequate remaining life and the blade subsequently fails in flight, the causation analysis involves the manufacturer of the AI system, the airline that relied on its output, the MRO provider that implemented the AI-recommended maintenance schedule and the regulatory framework that permitted or required AI-driven maintenance planning.

The standard of care for maintenance decisions is well established in aviation law. Maintenance must comply with the manufacturer’s maintenance programme and the applicable airworthiness directives. AI-enabled predictive maintenance introduces a tension between approved maintenance intervals and tool-generated assessments of component condition. If an operator seeks to extend service life or alter inspection timing on the basis of AI analysis and the component subsequently fails, the critical question will be whether that decision sat within an approved maintenance or reliability programme. The operator’s deviation from published maintenance intervals will be a central issue in the litigation.

Drone Delivery and Airspace Liability

Drone delivery in the United States currently operates through a combination of Part 135 air-carrier certification, Part 107 small-UAS rules, exemptions and individual airspace authorisations for each delivery zone. Operators including Wing, UPS Flight Forward, Amazon Prime Air and Zipline have used the Part 135 pathway.⁴ FAA environmental and operational review materials for Zipline projects contemplate operations of up to 400 delivery flights per operating day from each site and, in some cases, 24-hour operations.⁴ The proposed Part 108 rule for routine beyond-visual-line-of-sight operations is intended to simplify the fragmented structure, but until it is finalised the regulatory path remains piecemeal.⁴

Liability for drone delivery incidents operates under conventional aviation and product liability principles. The drone operator will face the primary operational and regulatory exposure under its Part 135 authority. The manufacturer is liable for design and manufacturing defects. The question that has not been tested is the liability of the AI navigation system that determines the drone’s flight path, altitude, obstacle avoidance and delivery approach. If an autonomous delivery drone strikes a person or property, the liability allocation between the operator, the manufacturer, the navigation software provider and the airspace authority that approved the delivery zone will depend on whether the cause was a software defect, a sensor failure, an environmental condition outside the system’s design parameters or a regulatory failure in approving the operating zone. Any claim against the FAA or another airspace authority would face public-law and sovereign-immunity barriers, so the practical liability focus is likely to remain on the operator, manufacturer and software suppliers.

Strategic Outlook

Aviation’s regulatory culture of exhaustive certification and conservative adoption of new technology has slowed the integration of AI into flight-critical systems. This conservatism has so far helped avoid AI-related aviation disasters in flight-critical civil aviation systems. It has also created a growing disparity between what AI can do in aviation and what the certification framework permits.

The 737 MAX litigation established that automated-system design defects, combined with certification failures, can produce multi-billion-dollar exposure across regulatory enforcement and civil proceedings. The eVTOL certification race may produce the first highly automated new-category aircraft type certificates within the next two years, although the extent to which those certificates involve AI rather than conventional flight-control automation will vary by platform. The expansion of drone delivery to 24-hour, high-volume operations over populated areas is likely to produce significant drone-injury litigation.

Aviation law is not creating a new liability framework for AI. It is applying existing certification, product liability and carrier liability principles to increasingly autonomous systems. The question is whether those principles, designed for deterministic systems that behave predictably, can accommodate AI-enabled systems whose behaviour may be difficult to characterise fully in advance and which may fail in ways their designers did not anticipate.

Notes

1. LOT Polish Airlines v Boeing, US District Court, Western District of Washington (Seattle), trial in May 2026; jury found LOT failed to prove purposeful misrepresentation regarding MCAS disclosure; Boeing cleared of approximately USD 153 million claim. Boeing federal criminal case dismissed November 2025 at DOJ request; Boeing agreed to pay/invest additional USD 1.1 billion in fines, victim family compensation and safety measures. Publicly reported MAX-related DOJ resolutions exceed USD 3.6 billion, comprising the 2021 deferred prosecution agreement of more than USD 2.5 billion and the 2025 non-prosecution/dismissal resolution involving approximately USD 1.1 billion in additional payments, compensation and safety/compliance investment. CNBC, DOJ Criminal Division, NPR reporting. Michael Ryan wrongful death trial scheduled 3 August 2026. Jury verdict of USD 49.5 million to family of Samya Stumo (Ethiopian Airlines crash), May 2026 (Reuters). JD Journal, National Trial Lawyers, Law Fold, Boeing settlement reporting.

2. EASA Notice of Proposed Amendment 2025-07, AI trustworthiness in aviation, published November 2025, original consultation deadline February 2026, extended to March 2026; Level 1 (AI assistance) and Level 2 (human-AI teaming) classification. FAA Roadmap for Artificial Intelligence Safety Assurance. SAE G34/EUROCAE WG114 developing ARP-6983 for AI integration up to DAL-C. Aviation Week, JDA Solutions reporting.

3. Joby Aviation began power-on testing of first FAA-conforming TIA aircraft November 2025 (Joby Q3 2025 results); began flight testing first certification aircraft March 2026 (Reuters). Archer Aviation, Beta Technologies and Wisk Aero also have concurrent FAA certification applications. No US eVTOL manufacturer has received full type certificate for commercial passenger operations as of Q1 2026. Lilium subsidiaries filed for insolvency late October 2024; N.V. board authorised insolvency 4 November 2024; cause was failure to secure EUR 100 million government funding commitment (German parliament budget committee blocked loan); Lilium had received EASA Design Organisation Approval November 2023 (Vertical Mag, AOPA reporting). eVTOL sector has attracted approximately USD 13 billion in investment since 2019 (Reuters, sector reporting). Altitudes Magazine, Airwaysmag reporting.

4. FAA Part 135 certification route used for drone package delivery; operators include Wing Aviation, UPS Flight Forward, Amazon Prime Air and Zipline (FAA). FAA environmental and operational review materials for Zipline projects contemplate up to 400 delivery flights per operating day and, in some cases, 24-hour operations. Proposed Part 108 rule for routine BVLOS operations pending. FAA reporting.

Introduction

On 28 April 2025, the continental electricity systems of Spain and Portugal collapsed.¹ The Iberian Peninsula blackout cut power to more than 55 million people. Electricity was interrupted for approximately ten hours in most areas and up to twenty hours in others. Parts of southern France connected to the Iberian grid were also affected. It was the largest grid failure in Western Europe in decades.¹

The cause was not a cyberattack, nor was it a single catastrophic equipment failure. The European Network of Transmission System Operators for Electricity’s (ENTSO-E) final report, published in March 2026, identified a sequence of interacting factors: oscillations, gaps in voltage and reactive-power control, differences in voltage-regulation practice, rapid output reductions and generator disconnections, producing fast voltage increases and cascading loss of generation across continental Spain and Portugal.¹

The Iberian blackout was not caused by AI. But it exposed the vulnerability that AI is now being deployed to address and, in doing so, it illuminated the liability framework that will govern AI failures in grid management. Grid operators across Europe, North America and Asia are deploying AI systems for load forecasting, demand response, frequency regulation, renewable integration and real-time grid optimisation. When those systems fail and the lights go out, the liability analysis will be materially more complex than the conventional regulatory failure at issue in the Iberian case.

AI in Grid Management: The Current Deployment

AI integration into electricity grid management is proceeding on three tracks. Load forecasting, where machine-learning models predict electricity demand to optimise generation dispatch and reduce the need for expensive peaking capacity. Renewable integration, where AI systems manage the variability of wind and solar generation by predicting output, coordinating battery storage and adjusting grid parameters in real time. And autonomous grid control, where AI systems make operational decisions about load shedding, frequency regulation and voltage management without waiting for human operator approval.²

Operationally, the case for AI grid management is strong. Replacement of synchronous generators with inverter-based renewable resources has diminished overall system inertia, making grids more vulnerable to frequency disturbances.² Traditional grid management relied on the inherent stability provided by large rotating generators. A grid powered substantially by solar panels and wind turbines requires active management to maintain the frequency and voltage stability that rotating mass previously provided. Real-time grid stability increasingly depends on automated control systems operating at speeds human operators cannot match. AI may sit around that control layer through forecasting, optimisation and decision-support, but the fastest stability interventions remain heavily dependent on engineered protection and control systems.

The regulatory exposure tracks the capability. A grid operator that deploys AI for real-time frequency management has adopted a system capable of responding to disturbances faster than any human operator. If that system fails to respond, or responds incorrectly, the operator’s liability will be assessed against the capability of the system it chose to deploy rather than against the slower human-operator standard it replaced.

The Iberian Blackout and Grid Operator Liability

The liability framework for grid failures varies across jurisdictions, but the common principle is that the transmission system operator bears a duty to maintain system security. In Spain, Red Eléctrica operates under obligations imposed by the Spanish Electricity Act and the European Network Codes. In the United States, the North American Electric Reliability Corporation (NERC) administers and enforces mandatory reliability standards under Federal Energy Regulatory Commission (FERC) oversight.³ In the United Kingdom, National Grid ESO (now the National Energy System Operator) operates under licence conditions imposed by Ofgem.³

ENTSO-E’s investigation confirmed that excess renewable generation did not trigger the blackout. The failure sequence lay in voltage control, reactive-power management, stabilisation capability and generator-disconnection dynamics.¹ The allocation questions will take years to resolve. European grid operators reported that a decade-long legal battle over responsibility was plausible.¹

For AI-managed grids, the Iberian precedent establishes the baseline: the grid operator is responsible for system security regardless of the tools it uses. AI does not transfer that responsibility to the technology provider. If a grid operator deploys an AI system for voltage control and the system fails to prevent a cascading blackout, the grid operator remains liable for the system failure because it chose to rely on that system. The technology provider may face secondary claims in product liability or under contractual indemnities, but the primary duty to the public sits with the operator.

Algorithmic Energy Trading and Market Manipulation

AI is now widely used in wholesale energy markets for automated trading, price forecasting and arbitrage between spot and futures markets. The parallels with algorithmic trading in financial markets are direct, but energy markets have sector-specific manipulation risks because energy is a physical commodity that must be generated, transmitted and consumed in real time.⁴

FERC regulates wholesale energy markets in the United States under the Federal Power Act. Market manipulation in FERC-regulated wholesale electricity markets is prohibited principally through Federal Power Act § 222, 16 U.S.C. § 824v, and FERC’s Anti-Manipulation Rule, 18 C.F.R. § 1c.2. The question of whether an AI trading system that exploits structural features of market design constitutes manipulation mirrors the Mango Markets problem in cryptocurrency: if the algorithm operates within the rules of the market as designed, is exploiting an inefficiency fraud or is it rational trading?⁴

The distinction from crypto is that energy markets are subject to mandatory regulatory oversight. FERC can impose civil penalties of at least USD 1 million per day per violation, with the statutory maximum subject to inflation adjustment, and can refer matters for criminal prosecution. The deployment of AI trading systems in energy markets creates a duty of supervision on the market participant that deploys the system. If an algorithm identifies and exploits a pricing anomaly in a way that distorts the market, the participant cannot claim that the algorithm acted independently. The participant designed, deployed and profited from the system.

AI Data Centres and Grid Capacity

On 23 October 2025, the Department of Energy sent a Section 403 directive to FERC to commence a rulemaking to accelerate the interconnection of large loads, explicitly including AI data centres.⁵ The directive responded to the reality that AI data centre construction is consuming grid capacity at a rate that threatens reliability in multiple regions. PJM Interconnection, which operates the largest wholesale electricity market in the United States, has reported a sharp increase in large-load and data-centre-driven interconnection pressure, making AI data-centre demand a central reliability and planning issue.⁵

FERC’s challenge is to balance the economic demand for AI data centre capacity against the reliability obligations imposed by its statutory mandate. The liability dimension arises when grid capacity allocated to AI data centres reduces the reserve margin available for residential and commercial consumers. If a grid operator approves data centre interconnections that compromise system reliability and a blackout results, the operator’s allocation decision will be scrutinised against its reliability obligations.

The political dimension is explicit. The DOE’s Section 403 letter characterised grid access for AI data centres as a matter of economic competitiveness and national security.⁵ The regulatory framework must accommodate political pressure for rapid interconnection while maintaining the engineering margin necessary to prevent cascading failures.

The EU AI Act and Energy Infrastructure

Under the EU AI Act, AI systems intended to be used as safety components in the management and operation of critical infrastructure, including electricity supply, are classified as high-risk.⁶ AI systems used for grid control, load management, energy dispatch and network operation will fall within the high-risk regime where they are intended to operate as safety components in the management or operation of electricity supply or other covered critical infrastructure. Those dates are now politically in flux. The May 2026 Digital Omnibus provisional agreement would move standalone high-risk AI obligations to 2 December 2027 and product-embedded high-risk AI obligations to 2 August 2028, subject to formal adoption.⁶

The practical consequence is that AI systems already deployed in European grid operations will need to be brought into compliance with the Act’s requirements, including conformity assessments, technical documentation and post-market monitoring. For grid operators, this creates a dual regulatory burden: compliance with the existing energy regulatory framework (the European Network Codes, national electricity legislation and ENTSO-E standards) and compliance with the AI Act’s horizontal requirements for high-risk AI systems.

How these two regulatory layers interact has not been fully mapped. The energy regulatory framework prescribes what the grid operator must achieve (system security, frequency stability, supply adequacy). The AI Act prescribes how the AI tools used to achieve those outcomes must be built, tested and monitored. A grid operator could be compliant with the AI Act’s requirements for its AI system and still suffer a blackout because the AI system, while meeting the Act’s technical standards, was not adequate for the specific grid conditions it encountered.

Strategic Outlook

The electrification of transport, heating and industrial processes is increasing demand on grids that are simultaneously transitioning from synchronous generation to variable renewable sources. AI is one of the technologies now being used to make that transition operationally manageable. The liability framework for AI-managed grids will be shaped by three dynamics.

Grid operator liability will not diminish because the operator delegates operational decisions to AI. The Iberian blackout demonstrates that the operator bears responsibility for system security regardless of the tools it deploys. The operator’s duty of care includes the selection, validation and monitoring of AI systems and the maintenance of human oversight sufficient to intervene when those systems fail.

Energy trading liability will follow the pattern established in financial markets and, more recently, in DeFi: the deployer of an algorithmic trading system is responsible for the system’s market conduct. FERC’s enforcement powers provide a sector-specific regulatory mechanism that DeFi markets lacked in the Mango Markets litigation, where the attempt to apply conventional market-manipulation doctrine produced a contested and ultimately unstable criminal result.

The AI Act’s classification of grid management AI as high-risk will create compliance obligations that go beyond existing energy regulation. The interaction between the two regulatory frameworks will produce interpretive disputes. European grid operators will face the challenge of meeting energy-security obligations with AI tools that must simultaneously satisfy the Act’s trustworthiness requirements.

The grid is the infrastructure on which every other sector depends. When AI-managed grids fail, the economic, social and political consequences will exceed those of any other AI failure outside military applications. The liability framework for that failure is being constructed now, through regulatory rulemaking, insurance market pricing and the contractual allocation of risk between grid operators, technology providers and the governments that are simultaneously mandating the energy transition and the AI deployment intended to make it work.

Notes

1. 2025 Iberian Peninsula blackout, 28 April 2025; power lost across Spain, Portugal and parts of southern France; approximately 55 million people affected; power interrupted for 10-20 hours. ENTSO-E expert panel factual report October 2025; final report published 20 March 2026 identified sequence of interacting factors including oscillations, voltage and reactive-power control gaps, differences in voltage-regulation practice, rapid output reductions, generator disconnections and uneven stabilisation capabilities. ENTSO-E confirmed excess renewable generation did not trigger the blackout. IEEFA, pv-magazine, ENTSO-E reporting.

2. AI grid management applications: load forecasting, renewable integration, autonomous grid control. Replacement of synchronous generators with inverter-based resources reduces system inertia and increases vulnerability to frequency disturbances. AI Frontiers, academic literature on AI grid stability.

3. NERC mandatory reliability standards administered under FERC oversight (United States). National Energy System Operator under Ofgem licence conditions (United Kingdom). European Network Codes and ENTSO-E standards (EU).

4. Federal Power Act § 222, 16 U.S.C. § 824v; FERC Anti-Manipulation Rule, 18 C.F.R. § 1c.2. FERC civil penalties of up to USD 1 million per violation per day. Mango Markets/Eisenberg: convictions subsequently vacated by the district court (Venable, June 2025 reporting).

5. Department of Energy Section 403 directive to FERC, 23 October 2025, directing rulemaking to accelerate large-load interconnection including AI data centres. PJM Interconnection data centre interconnection demand. Technostatecraft, Utility Dive reporting.

6. EU AI Act (Regulation (EU) 2024/1689); critical infrastructure AI classified as high-risk under Article 6 and Annex III. High-risk obligations originally scheduled for August 2026. Digital Omnibus provisional agreement 7 May 2026 (Consilium press release): standalone high-risk AI obligations moved to 2 December 2027; product-embedded high-risk AI obligations moved to 2 August 2028; subject to formal adoption. Kennedys Law, Gardner Law, Consilium reporting.

Introduction

On 22 May 2026, the International Maritime Organization adopted the first global safety code for autonomous and AI-enabled commercial ships.¹ The Maritime Autonomous Surface Ships Code, agreed at the 111th session of the Maritime Safety Committee in London, establishes a goal-based framework for the design, construction, certification and operation of remotely controlled and autonomous vessels. It applies to large internationally-trading cargo ships and takes effect on 1 July 2026. The code is non-mandatory. A mandatory version is planned for adoption by 2030 at the latest, with entry into force on 1 January 2032.¹

The adoption is a regulatory milestone, but the legal questions it does not answer are more significant than the safety requirements it establishes. Maritime liability law was constructed around the presence of a human master exercising judgment on a physical bridge. The International Regulations for Preventing Collisions at Sea presuppose a lookout maintaining a proper watch by sight and hearing. The Hague-Visby Rules allocate carrier liability on the basis of due diligence by human agents. When the bridge is empty and the navigation is algorithmic, each of these frameworks confronts a gap between its assumptions and the technology it must now govern.

The commercial deployment of autonomous vessels is no longer theoretical. The Yara Birkeland, a 120-TEU electric container vessel, has been in commercial operation between Heroya and Brevik in Norway since 2022 and completed a fully autonomous voyage under human supervision in March 2023, though regulatory constraints mean it continues to carry a small crew.² The River Drone fleet of ten autonomous dry cargo barges is conducting commercial operations on European inland waterways.³ The Netherlands amended its Inland Waterways Police Regulations from 1 January 2025 to remove the mandatory crew requirement, enabling applications for autonomous navigation.³ The MASS Code will accelerate this trajectory. The law is catching up, but it is doing so by adapting existing frameworks rather than building new ones.

The MASS Code and Its Limits

The MASS Code addresses safety requirements across thirteen chapters, covering surveys and certificates, risk assessment, system design, software principles, safe operations management, alert management, manning and training, safety of navigation, connectivity and remote operations.¹ Its central structural feature is the retention of a designated master who holds absolute legal responsibility for the vessel at all times, whether physically stationed on the bridge or monitoring from a remote operations centre ashore.¹

This is a pragmatic regulatory choice, not a legal answer. It preserves the existing chain of liability under The International Convention for the Safety of Life at Sea (SOLAS) and flag-state law by ensuring that someone, somewhere, can be identified as the responsible human. But it does not resolve the harder questions. When an autonomous navigation system makes a routing decision that leads to a collision, the master at the remote operations centre may have had no practical opportunity to intervene. The MASS Code requires human oversight but does not define the standard of care for a remote operator monitoring multiple vessels through a digital interface rather than standing watch on a single bridge.

The experience-building phase from 2026 to 2030 is designed to generate the operational data that will inform the mandatory code. During this period, flag states may authorise trials and commercial operations under their own domestic regulatory frameworks. The regulatory landscape will be jurisdictionally fragmented until at least 2032.

The River Drone 5 and the First Collision Involving an Autonomy-Equipped Barge

On 5 December 2024, the autonomous barge River Drone 5 collided with a container vessel on the Scheur River near Rotterdam.³ At least four containers were spilled into the waterway. The River Drone 5 is one of ten autonomous dry cargo barges, each approximately 100 metres in length with a cargo capacity of 3,850 tonnes, operated by the Dutch maritime technology firm Seafar using remote navigation systems.³

The operator confirmed that the vessel was under the command of a captain on board at the time of the collision and was not operating in autonomous mode.³ The distinction is critical for liability allocation: if the vessel had been navigating autonomously, the liability analysis would have engaged product liability for the navigation system, the classification society’s certification of the autonomous capability and the regulatory framework under which the vessel was authorised to operate. Because a human captain was in command, the incident falls within conventional fault-based collision law.

The incident arrived three weeks before the Netherlands activated its new inland waterway regulations permitting crewless autonomous operation from 1 January 2025.³ The timing illustrates the regulatory challenge: vessels capable of autonomous operation will frequently switch between autonomous and human-commanded modes. The liability framework must accommodate both states and the transitions between them.

Collision Liability and the COLREGs Problem

The 1972 Convention on the International Regulations for Preventing Collisions at Sea was drafted for vessels commanded by human officers making real-time navigational decisions. Rule 5 requires every vessel to maintain a proper lookout “by sight and hearing as well as by all available means appropriate in the prevailing circumstances.” Rule 7 requires the use of “all available means” to determine risk of collision. Rule 8 requires action to avoid collision to be “positive, made in ample time and with due regard to the observance of good seamanship.”⁴

These rules assume human sensory perception and human judgment. An autonomous navigation system does not maintain a lookout “by sight and hearing” in any conventional sense. It processes sensor data, radar returns, AIS signals and camera feeds through algorithmic decision-making. Whether this constitutes compliance with Rule 5 depends on whether “all available means appropriate in the prevailing circumstances” can encompass an autonomous sensor suite. The IMO’s regulatory scoping exercise acknowledged this ambiguity but did not resolve it. The MASS Code’s approach is to require that the autonomous system achieve functional equivalence to human-commanded navigation, without specifying how that equivalence is to be assessed in a collision inquiry.¹

When two autonomous vessels collide, the traditional apportionment of fault under the Brussels Collision Convention of 1910 faces a category problem. Fault in collision law is assessed against the standard of good seamanship. Good seamanship is a human standard, developed through centuries of admiralty case law and predicated on the judgment of a competent mariner. The question of whether an algorithm can exercise or fail to exercise good seamanship has no settled answer. Norwegian maritime law scholars have proposed that collision costs could be shared proportionally based on COLREGs compliance, treating technical failure as the functional equivalent of navigational error, but no court has adopted this analysis.⁵

Classification Society Liability

Classification societies set the standards for ship design, construction and maintenance. Hull, cargo and P&I underwriters require classification as a prerequisite to insurability. For autonomous vessels, classification societies will certify not only the physical structure of the ship but the reliability and safety of the autonomous navigation system, the sensor suite, the communications infrastructure and the software that controls the vessel.⁶

The liability exposure is material. Classification societies have historically disclaimed responsibility for vessel safety, fitness for purpose and seaworthiness, maintaining that their role is verification against published standards rather than warranty of operational capability. This disclaimer has been tested in conventional shipping and has generally held. The question is whether the same disclaimer will survive when the classification society has certified an AI navigation system that subsequently causes a collision. The society’s assessment of autonomous capability is not a routine structural survey. It involves evaluating software reliability, sensor integration, failure-mode analysis and cybersecurity resilience. If the certified system fails and a vessel is lost, claimants will argue that the society’s certification constituted a representation of capability that induced reliance by the shipowner, charterer and cargo interests.⁶

P&I Insurance and the Coverage Gap

Protection and indemnity insurance covers third-party liabilities including crew injury, cargo claims, collision damage and pollution. The P&I clubs that provide this cover operate as mutual associations, pooling risk across their membership. Their rules and coverage are designed around conventional crewed vessels.⁷

Autonomous vessels introduce risks that sit outside the existing P&I framework. Cyber-attack on a navigation system, software failure causing grounding, sensor malfunction leading to collision and communication loss between the vessel and its remote operations centre are all plausible casualty scenarios with no direct precedent in P&I claims history. The International Group has not yet produced a uniform market wording for autonomous vessel risk. Individual clubs have indicated willingness to cover autonomous operations on a case-by-case basis, but the premium methodology, the exclusion architecture and the allocation of liability between shipowner, software provider and remote operations company remain undefined.⁷

The insurance market is likely to follow the pattern seen in other AI-exposed sectors. For a period, autonomous risks will sit in “silent” coverage: neither expressly included nor expressly excluded. The ambiguity will probably be resolved by litigation after a casualty, not by proactive policy drafting.

Electronic Trade Documents and Smart Bills of Lading

The digitisation of shipping documents is proceeding on a separate but convergent track. UNCITRAL’s Model Law on Electronic Transferable Records has been enacted or adopted in legislation in at least eleven jurisdictions.⁸ The United Kingdom’s Electronic Trade Documents Act 2023 gave digital trade documents legal equivalence with paper.⁸ India’s Bills of Lading Act 2025 modernised the statutory framework for bills of lading in a major emerging market, but it should not be treated as equivalent to the United Kingdom’s Electronic Trade Documents Act 2023.⁸

Blockchain-based smart bills of lading promise to automate the transfer of title to goods in transit. If an autonomous vessel carries cargo under a smart bill of lading that releases title on delivery confirmation from the vessel’s own sensors, the entire transaction from loading to title transfer could in principle occur without human intervention. The legal infrastructure is being assembled. The ICC’s 2024 Digital Trade Survey found that 49.2% of respondents were already using electronic bills of lading in some capacity. That is a respondent-usage figure, not a measure of eBLs as a proportion of all bills of lading issued.⁸

The convergence of autonomous vessels and electronic trade documents raises liability questions that neither framework addresses in isolation. If a smart bill of lading releases title based on sensor data from an autonomous vessel and the cargo is subsequently found to be damaged, the allocation of liability between the carrier, the software provider, the sensor manufacturer and the blockchain platform operator has no precedent.

Strategic Outlook

Maritime law absorbs technological change slowly and through accretion. The MASS Code will not create a comprehensive law of autonomous shipping. It will provide a safety framework within which flag states, classification societies, P&I clubs and commercial parties must build their own liability and contractual structures.

Three gaps will define the next decade of autonomous shipping disputes. The COLREGs gap, where the collision avoidance regime has not been rewritten for algorithmic navigation and will be interpreted by admiralty courts applying human-seamanship standards to machine decisions. The insurance gap, where P&I coverage has not been restructured for autonomous risks and where the first major casualty will produce coverage litigation before it produces safety regulation. And the liability gap, where the allocation of responsibility between the remote master, the shipowner, the software provider, the classification society and the communications infrastructure operator remains contractually and doctrinally unresolved.

The shipowners and technology companies that build the first generation of commercial autonomous fleets are constructing the factual matrix from which maritime law will be remade. The direction of travel is not fixed. The law may adapt fault-based principles to autonomous operation. It may move towards strict liability for autonomous systems. Much will depend on where the first catastrophic casualty occurs and which court hears the case.

Notes

1. IMO Maritime Safety Committee, 111th session (MSC 111), 13-22 May 2026; adoption of the non-mandatory Maritime Autonomous Surface Ships (MASS) Code; entry into force 1 July 2026; mandatory code planned for adoption by 2030, entry into force 1 January 2032. DNV reporting on MSC 111 confirms thirteen-chapter structure and retention of designated master with absolute legal responsibility.

2. MV Yara Birkeland, 120-TEU electric autonomous container vessel, operating between Heroya and Brevik, Norway (approximately 7 nautical miles), since 2022; fully autonomous voyage under human supervision completed March 2023; continues to carry small crew due to regulatory constraints; built by Vard, owned by Yara International. Yara International reporting.

3. River Drone 5 collision, Scheur River near Rotterdam, 5 December 2024; approximately 100m autonomous barge, 3,850-tonne capacity, operated by Seafar; four containers spilled; operator confirmed vessel was under human command, not autonomous mode, at time of collision. Maritime Executive, Marine Insight and Breakbulk News reporting. Netherlands amended Inland Waterways Police Regulations (BPR) from 1 January 2025 removing mandatory crew requirement and enabling applications for autonomous navigation exemptions.

4. Convention on the International Regulations for Preventing Collisions at Sea 1972 (COLREGs), Rules 5 (lookout), 7 (risk of collision) and 8 (action to avoid collision).

5. Wiersholm, ‘Liability for damage caused by autonomous ships: a Norwegian perspective’, discussing fault-based collision liability under the Norwegian Maritime Code and its application to autonomous vessels; ScienceDirect, ‘Tortious liability for autonomous marine vehicle collisions: A suggestive move from fault-based to strict liability’ (2025).

6. Skuld, ‘Liability of Classification Societies’; IACS classification requirements for hull, cargo and P&I insurability; SAFETY4SEA, ‘Insurers’ Considerations for Autonomous Ships’.

7. International Group of P&I Clubs; SAFETY4SEA, ‘Insurers’ Considerations for Autonomous Ships’; Riviera Maritime Media, ‘Autonomous shipping creates liability waves’.

8. UNCITRAL Model Law on Electronic Transferable Records (MLETR), enacted or adopted in legislation in at least 11 jurisdictions per UNCITRAL status page; UK Electronic Trade Documents Act 2023; India’s Bills of Lading Act 2025 modernised India’s bills of lading framework; it does not provide the same electronic trade document equivalence model as the UK Electronic Trade Documents Act 2023; ICC 2024 Digital Trade Survey reporting 49.2% of respondents using electronic bills of lading, with actual eBL penetration as a proportion of total bills of lading issued remaining in the low single digits.

Nine class actions commenced in the Northern District of Illinois between 11 and 13 May 2026 allege that Alphabet, Amazon, Apple, Meta, Microsoft, NVIDIA, Samsung, Adobe and ElevenLabs extracted voiceprints from recordings of Illinois broadcasters, podcasters and audiobook narrators in the course of training AI foundation voice models. The plaintiffs say none of this was preceded by notice, written consent or a publicly available retention policy. The statute they sue under is the Illinois Biometric Information Privacy Act 2008.

The plaintiffs and the defendants

The named plaintiffs include Carol Marin (CBS News, 60 Minutes, Chicago Tonight), Phil Rogers (NBC Chicago, WBBM), Robin Amer (The Washington Post and The City podcast), Yohance Lacour (You Didn’t See Nothin’), Lindsey Dorcus (audiobook narrator for Penguin Random House, Hachette and Disney), Victoria Nassif (Chicago PD; audiobooks for Penguin, Hachette and Audible) and Alison Flowers (Invisible Institute; Somebody podcast). Flowers is named in every action except those against Amazon and Apple. The plaintiffs are represented by Loevy + Loevy with Ross Kimbarovsky as lead counsel.

The statute

Section 10 of BIPA defines biometric identifier as a retina or iris scan, fingerprint, voiceprint, or scan of hand or face geometry. Voiceprint has sat in the statutory definition since the Act was passed in 2008. Section 15 sets the obligations that BIPA actions are typically built on. Section 15(a) requires a publicly available retention and destruction schedule. Section 15(b) requires written notice and written consent before collecting a biometric identifier. Section 15(c) prohibits selling, leasing or otherwise profiting from biometric data. Section 15(d) restricts disclosure without consent or statutory exception, and Section 15(e) requires reasonable care in storage and protection. Section 20 fixes statutory damages at $1,000 for negligent violations and $5,000 for intentional or reckless violations, plus reasonable attorneys’ fees, costs and injunctive relief.

Damages after Clay

After Cothron v. White Castle, Section 15(b) and Section 15(d) claims could accrue with each scan or transmission. That was the source of the per-scan exposure problem in workplace fingerprint litigation. A biometric timeclock used repeatedly over several years could produce thousands of alleged statutory violations for the same employee.

The Illinois General Assembly passed Public Act 103-0769 in August 2024 to narrow that exposure. For repeated Section 15(b) collection and Section 15(d) disclosure, the statute now limits recovery to one award per person, per method, for the same biometric identifier or information. On 1 April 2026, the Seventh Circuit in Clay v. Union Pacific Railroad Company, No. 25-2185, held that the amendment applies retroactively to cases pending at the time of enactment.

The result is a narrower damages framework. The question is no longer whether repeated ingestion events can multiply exposure in the same way as repeated fingerprint scans. It is whether the alleged extraction of a voiceprint from training recordings falls within BIPA at all.

The Section 10 question

The decisive question across the nine complaints is whether training a foundation voice model on a recording extracts a voiceprint within the meaning of Section 10. The plaintiffs say it does. A foundation voice model is trained on pitch, cadence, tone and vocal-tract characteristics. On that case, the mathematical representation those features produce inside the model is a voiceprint by another name. The defendants are likely to say it does not. On the defence view, a recording is audio data and a voiceprint is a stored mathematical identifier used to authenticate a speaker. If the data is generalised across many speakers and is not retained as a speaker-authentication template, the defence will say it is neither. Microsoft has already advanced that position in separate Teams litigation: Basich v. Microsoft Corp., W.D. Wash.

What to watch

Two thresholds will shape the litigation.

First, whether the Northern District of Illinois accepts the plaintiffs’ theory that training a foundation voice model on a recording is collection of a voiceprint within Section 15(b).

Second, whether the post-Clay damages framework leaves enough exposure and settlement leverage to support coordinated nine-defendant litigation. The Section 10 voiceprint question will decide the first point. Early consolidation decisions, dismissal motions and any first settlement structure will show the second.

Sources

Loevy + Loevy press release, 14 May 2026.

Chicago Sun-Times, 19 May 2026.

Capitol News Illinois, 15 May 2026.

740 ILCS 14/1 et seq. (Illinois Biometric Information Privacy Act).

Public Act 103-0769 (eff. 2 August 2024).

Cothron v. White Castle System, Inc., 2023 IL 128004.

Clay v. Union Pacific Railroad Company, No. 25-2185 (7th Cir. 1 April 2026).

Basich v. Microsoft Corp., W.D. Wash. (pending).

CourtListener, Basich v. Microsoft Corporation, No. 2:26-cv-00422, W.D. Wash.

UC Today, Microsoft Teams Lawsuit: BIPA Class Action Targets AI Voice Data, 17 February 2026.

Law360, Microsoft Says Teams Info Not ‘Voiceprint’ Under BIPA.

The pleading

In Pikabea v. Walters (S.D.N.Y. 1:26-cv-03238, commenced 20 April 2026), a putative class of AI16Z / ELIZAOS purchasers sued Shaw Walters, Eliza Labs, AI16Z DAO and others. The complaint alleges that the project’s central claim, that an autonomous AI agent named “Marc AIndreessen” was managing on-chain investment decisions, was false. It alleges the agent was operated manually.¹ Secondary reporting refers to a class said to cover approximately 3,945 affected wallet addresses across a class period running from 24 October 2024 to 20 April 2026.²

The pleading further alleges that the project mimicked the “a16z” abbreviation of Andreessen Horowitz without authorisation. It alleges that a late-2025 token migration from $AI16Z to $ELIZAOS expanded supply tenfold from 1.1 billion to 11 billion. Of the expanded supply, 40 per cent is alleged to have been allocated to insiders and undisclosed private investors against a 60 per cent allocation to public holders.³ The token had reportedly reached a peak market capitalisation above USD 2.6 billion in early January 2025.

The complaint asserts six counts: sections 349 and 350 of the New York General Business Law (deceptive acts or practices and false advertising), the California Unfair Competition Law and False Advertising Law, negligent misrepresentation and unjust enrichment. There is no Securities Act or Exchange Act claim.

The action has been assigned to Hon. Jed S. Rakoff. At a status conference on 19 May 2026, the court declined to advance the matter because plaintiff had made only one service attempt on Walters in San Francisco; a written report on service is due by 10am on 5 June 2026. Sebastian Quinn-Watson is resident in Australia and will require Hague Convention service. AI16Z DAO’s amenability to suit is contested; DLA Piper, appearing for Walters and Eliza Labs, took the position at the same conference that “the DAO can’t be sued”.⁷ The forum is the same district and before the same judge as United States v. Heppner (S.D.N.Y., 17 February 2026), the AI-privilege ruling involving a criminal defendant’s self-directed use of Claude.

The state-law turn

The choice is consistent with two converging pressures. The first is the SEC’s reorientation. The joint SEC/CFTC interpretive release of 17 March 2026 sets out a taxonomy of crypto-assets and signals an approach centred on classification rather than the front-foot enforcement of the Gensler era. Chairman Atkins has signalled that AI-related misstatements will be addressed under existing anti-fraud frameworks rather than through a bespoke AI-washing rule.⁴

The second is the prior enforcement record. The SEC’s March 2024 settlements against Delphia (USA) Inc. and Global Predictions Inc. produced civil penalties of $225,000 and $175,000 respectively for misstatements about the use of AI in investment processes. The enforcement template exists. What is uncertain is whether it will be used in crypto-token AI-agent cases where classification remains contested.⁵

The Pikabea complaint shows how that gap may be filled. State consumer protection statutes do not depend on a “security” classification of the token. Section 349 of the New York General Business Law requires a deceptive act in the conduct of any business directed at consumers in New York. Section 350 requires false advertising. The California UCL and FAL provide analogous coverage. The pleader does not need to win the Howey debate to advance the state-law theory.

The English comparator

A claim of this character would be addressed differently in the United Kingdom. Under the Digital Markets, Competition and Consumers Act 2024, in force from 6 April 2025, the CMA may issue direct infringement decisions for misleading commercial practices and impose civil penalties of up to 10 per cent of a trader’s global turnover without first obtaining a court order.⁶ A token promotion that misrepresented the existence of an autonomous AI agent would be capable of attracting that jurisdiction. It would also fall within the FCA’s financial promotions regime for qualifying cryptoassets, in force since 8 October 2023, where breach of the section 21 financial-promotion restriction can amount to a criminal offence under section 25 of the Financial Services and Markets Act 2000.

The English direction is administrative and centralised. The American direction, at least for the moment, is private and state-based. The same misrepresented AI capability is policed through two different enforcement architectures.

Implications for practitioners

Three points follow. First, crypto issuers should treat any “autonomous AI agent” claim as capable of being characterised as a material representation under state consumer protection law and, in UK-facing promotion, as part of a financial promotion under FCA rules. Second, pleading strategy in US AI-washing cases may migrate towards state consumer statutes in federal forums where securities status is contested. Third, English counsel advising crypto issuers must stress-test promotional copy not against the Misrepresentation Act 1967 alone but against the DMCC Act enforcement regime, which imposes a far heavier penalty exposure.

Pikabea is the early-stage pleading. Whether the court certifies a class and whether the state-law theory survives a motion to dismiss will set the template for the next generation of AI-washing claims.

Footnotes

¹ Pikabea v. Walters et al, No. 1:26-cv-03238 (S.D.N.Y. commenced 20 April 2026; Rakoff, J.). Justia docket; Inner City Press court report of the 19 May 2026 status conference; Burwick Law statement of 20 April 2026. Operative complaint not independently reviewed at time of writing.

² Wallet-address figure (approximately 3,945) drawn from secondary reporting (Binance Square, claimdepot.com, 21 April 2026).

³ Allocation, supply and migration figures, market-capitalisation figure and chronology drawn from secondary reporting (claimdepot.com, 21 April 2026, citing the complaint paragraph-by-paragraph; crypto-economy.com; cryptorank.io). Independent verification against the operative complaint and the token contract remains recommended.

⁴ SEC interpretive release issued with CFTC interpretive guidance, 17 March 2026 (SEC Press Release 2026-30; SEC Release No. 33-11412).

⁵ In re Delphia (USA) Inc. and In re Global Predictions Inc., SEC settled orders of 18 March 2024 (penalties of $225,000 and $175,000). Wider AI-washing template includes the June 2024 charges against the founder of Joonko Diversity Inc. (SEC Press Release 2024-70).

⁶ Digital Markets, Competition and Consumers Act 2024, Parts 3 and 4 (consumer protection), in force 6 April 2025 (Commencement No. 2 Regulations 2025).

⁷ Inner City Press court report, “Crypto Lawsuit Over AI16Z Is Delayed By Lax Service of Process in SF As DAO Unrepresented” (19 May 2026), reporting the live SDNY status conference before Judge Rakoff on service of process and the DAO’s amenability to suit.

In United States v. Heppner, Judge Rakoff (S.D.N.Y., 17 February 2026) ruled that a criminal defendant’s exchanges with Anthropic’s Claude were not protected by attorney-client privilege.¹ That opening understates the split that followed within six weeks.

The American split

Three federal opinions issued between February and March now sit in tension. Heppner refuses both attorney-client privilege and work product protection to AI-generated material a client produced on his own initiative. Warner v. Gilbarco, Inc. (E.D. Mich., 10 February 2026), decided the same day as Heppner‘s ruling, protects pro se work product on the reasoning that generative AI tools “are tools, not persons” and that treating each prompt as a waiver would “nullify work-product protection in nearly every modern drafting environment”.² Morgan v. V2X, Inc. (D. Colo., 30 March 2026) sits between the two. Magistrate Judge Dominguez Braswell upheld work product protection but compelled the litigant to disclose the AI tool used and prohibited uploads of confidential discovery into any platform not contractually barred from training on, retaining or sharing the inputs.³

All three opinions are first-instance. None has yet been tested on appeal. The pressure point is not the privilege analysis itself, which is conventional. It is the framing of the AI tool. Heppner treats Claude as a non-lawyer interlocutor whose receipt of the communication destroys it. Warner treats the tool as an instrument of the litigant, no different from a word processor with autocomplete. Morgan accepts the Warner framing but imposes a vendor-contract overlay borrowed from data protection practice.

The Kovel line

The unresolved question for practitioners is whether United States v. Kovel, 296 F.2d 918 (2d Cir. 1961), reaches AI. Kovel extended privilege to communications routed through an accountant engaged by counsel to assist in providing legal advice. Subsequent authority confirmed the principle that translators, investigators and experts hired by counsel can sit inside the privileged circle as the lawyer’s agent.

Heppner gestures at this without resolving it. Judge Rakoff observed that the outcome could have been different had counsel directed the use of Claude and the document fed back into the lawyer’s advice loop. The immediate route is therefore not an AI privilege, but the older agency question: whether the tool was deployed at counsel’s direction for the purpose of enabling legal advice. That test will probably arrive in a civil case in which counsel has directed the use of an enterprise AI tool with contractual restrictions on training, retention and disclosure. The question will be whether the Kovel principle survives the translation from a human professional to a model.

The English position and the practitioner read

The Upper Tribunal decision in Munir v Secretary of State for the Home Department [2026] UKUT 81 (IAC) takes the harder position. Uploading confidential client material into a public or open AI tool (what the Tribunal called an “open-source AI tool, such as ChatGPT”) is publication to the public domain that waives legal professional privilege. Closed-source enterprise systems sit outside that holding, but the Tribunal did not extend privilege to them affirmatively. The Three Rivers (No 6) [2004] UKHL 48 architecture, which already constrains legal advice privilege to lawyer-client communications and litigation privilege to anticipated proceedings, leaves little room for a Kovel-style agency extension to consumer AI in England and Wales.

For in-house counsel the strategic position is now clear in outline. Three propositions hold across both jurisdictions. The lawyer’s direction must be documented at the point the AI tool is deployed, not reconstructed later. The vendor contract must restrict training, retention and third-party disclosure. Consumer AI tools should be excluded from any workflow that touches privileged or work product material until the appellate position is settled.

The first federal case to test Kovel against an enterprise AI deployment is the one to watch. Until then, Heppner, Warner and Morgan identify the question without resolving it.

¹ United States v. Heppner, S.D.N.Y., Memorandum Opinion of 17 February 2026 (Rakoff, J.).

² Warner v. Gilbarco, Inc., No. 2:24-cv-12333, E.D. Mich., Order of 10 February 2026 (Patti, M.J.).

³ Morgan v. V2X, Inc., D. Colo., Order of 30 March 2026 (Dominguez Braswell, M.J.).

Introduction

On 23 May 2025, Judge Arun Subramanian of the Southern District of New York granted Avraham Eisenberg’s Rule 29 motion following his Mango Markets conviction.¹ Eisenberg had been convicted by a jury in April 2024 on commodities fraud, commodities manipulation and wire fraud. The trial judge set aside all three counts. The ruling was not a general endorsement of “code is law”. It was narrower and more legally important. The commodities convictions failed because the government had not proved venue in the Southern District of New York. The wire-fraud count failed both because the government had not proved venue and because it had not proved a false statement or deceptive representation to the protocol. The decision exposes the difficulty of translating protocol-permitted conduct into conventional fraud doctrine, but it does not immunise manipulation as such. Federal prosecutors have appealed.¹

The decision crystallises a problem that courts across multiple jurisdictions are now confronting. Smart contracts execute autonomously. They do not interpret. They do not exercise discretion. They do what their code permits. When a participant exploits that code in ways the developers did not anticipate, the question of whether that conduct constitutes fraud depends on whether the protocol itself established the norms being violated. If the protocol is silent, the traditional elements of fraud may not be present. The implications for decentralised finance are practical, not theoretical and the case law emerging in 2024 and 2025 suggests that legal systems are absorbing programmable transactions into existing doctrinal frameworks rather than constructing new ones.

The emerging case law is not asking whether code is law. It is asking which layer of a decentralised system the law should treat as legally operative: the autonomous code, the governance machinery around it, or the human actors who designed, controlled and profited from both. The cases reviewed in this article divide across those three layers. Eisenberg concerns the autonomous code layer. Tornado Cash concerns the boundary between code and its human operators. Ooki DAO and the governance-exploit cases concern the human control layer. The Property (Digital Assets etc) Act 2025 provides the English-law proprietary framework within which disputes at all three layers may be resolved.

The Mango Markets Problem

The mechanics of Eisenberg’s scheme were straightforward in execution if complex in structure. In October 2022, he created two accounts on the Mango Markets platform. One account took a large long position in MNGO Perpetuals; the other took a corresponding short position. He then purchased MNGO tokens across multiple exchanges in sufficient volume to inflate the spot price. Because Mango Markets used an oracle feed drawing on those external exchange prices, the inflated spot price fed through to his perpetual position’s unrealised profit. That unrealised profit constituted collateral within the protocol. Against that inflated collateral, Eisenberg borrowed approximately $110 million in other tokens from the Mango Markets lending pools and withdrew them from the platform.¹

The scheme was public. Eisenberg identified himself on social media within days and described his actions as a “highly profitable trading strategy.” He offered to return a portion of the funds in exchange for an agreement not to pursue criminal prosecution, a negotiation conducted openly through on-chain governance proposals.

The legal question was precise: if a decentralised protocol has no rules prohibiting the conduct, is exploiting it fraud? The government’s theory had two strands. The commodities counts treated the oracle-driven price inflation as market manipulation. The wire-fraud count treated the borrowing as obtaining property by false pretences. The court separated them. It held that the commodities counts failed for want of venue. The wire-fraud count failed for want of venue and, independently, because the protocol had made no representation capable of being falsified and had no rule prohibiting the borrowing.¹

The appeal is pending before the Second Circuit. It will test how far venue doctrine and wire-fraud falsity constrain crypto-exploit prosecutions. It will not settle all DeFi liability. It will matter most where prosecutors seek to characterise protocol-permitted execution as deceit, rather than as manipulation, theft or unauthorised access.

Tornado Cash and the Limits of Sanctioning Code

On 26 November 2024, the Fifth Circuit held that the Office of Foreign Assets Control exceeded its statutory authority when it designated the immutable smart contracts of Tornado Cash under the International Emergency Economic Powers Act.² The court’s reasoning was direct. IEEPA permits the blocking of “property” in which a foreign national has an “interest”. Immutable smart contracts do not constitute property within that statutory meaning where no person owns, controls or can modify them. No person holds an ownership interest. No person exercises exclusion rights. The contracts exist on the Ethereum blockchain, executing their mixing function for anyone who interacts with them.

The Treasury Department declined to seek certiorari. On 21 March 2025, OFAC formally delisted the Tornado Cash smart contract addresses.³ The sanctions designation that had been in force since August 2022 was withdrawn.

Roman Storm’s prosecution shows the same distinction operating from the other direction. In August 2025, a jury convicted him of conspiracy to operate an unlicensed money-transmitting business but deadlocked on the money-laundering and sanctions-conspiracy counts.² The result sharpens the distinction drawn in Van Loon: immutable contracts may fall outside OFAC’s property-blocking power under IEEPA, but developers and operators remain exposed to conventional criminal theories where prosecutors can prove the necessary human conduct and mens rea. As of May 2026, prosecutors were seeking an October 2026 retrial on the two deadlocked counts, while Storm continued to pursue post-trial relief from the money-transmitting conviction.

Immutable smart contracts may fall outside a particular statutory mechanism, as Van Loon held under IEEPA. That does not make the wider protocol ecosystem legally neutral. Interfaces, developers, governance participants, relayers and entities that profit from or maintain the system remain capable of regulation or prosecution where ordinary statutory elements are satisfied. The separation of code from coder is becoming a central organising principle in this area.

DAO Liability and the Ooki DAO Precedent

In June 2023, the Commodity Futures Trading Commission obtained a default judgment against Ooki DAO in the Northern District of California.⁴ The court imposed a civil monetary penalty of $643,542 and a permanent trading and registration ban. The significance of the judgment lies not in the quantum but in the jurisdictional finding: the court held that Ooki DAO was an unincorporated association under California law and therefore was a “person” under the Commodity Exchange Act and could be sued, served and bound by judgment.⁴

The CFTC had served the DAO through a “help chat” box on its website and by posting the complaint in a governance forum, methods the court accepted as adequate under the circumstances. The DAO’s failure to appear (no individual took responsibility for instructing lawyers) produced the default. But the legal architecture the court constructed survives independently of the procedural posture. If a DAO is an unincorporated association, its members may bear joint and several liability for its obligations. The harder question is whether governance participation can translate into personal exposure. Ooki establishes that the DAO itself may be treated as an unincorporated association. It does not yet establish that every voting token holder is personally liable for the DAO’s regulatory violations.

Token-holder liability therefore remains the unresolved issue. No court has yet determined whether holding a governance token and voting on a proposal creates the degree of participation necessary to establish personal liability under unincorporated association principles. The question is live. Multiple enforcement actions against DAOs are proceeding in various jurisdictions and the Ooki DAO framework provides the template that regulators are deploying. For DeFi governance participants, the practical consequence is that voting on protocol proposals may create legal exposure that a purely passive token holding would not.

The Property (Digital Assets etc) Act 2025

The Property (Digital Assets etc) Act 2025 received Royal Assent on 2 December 2025.⁵ It contains one operative section, but the legal effect is material. The Act confirms that digital assets are capable of being personal property under English law notwithstanding that they are neither things in possession nor things in action. It gives statutory footing to the third-category analysis that the common law had been developing since 2019, while leaving the boundaries of that category to be worked out by the courts.

The Act’s intellectual foundations lie in the UK Law Commission’s 2023 final report on digital assets, which recommended statutory confirmation that digital assets could attract proprietary rights without fitting into either traditional category.⁶ The Law Commission’s draft bill followed in July 2024. Parliament enacted it with minimal amendment. The Law Commission’s ongoing project on digital assets and electronic trade documents in private international law, with a consultation paper published on 5 June 2025, extends this work into cross-border questions of applicable law and jurisdiction.⁷

The Act does not create an all-purpose law of digital assets. Its move is narrower and more useful. It confirms that an asset is not excluded from personal property rights merely because it is neither a thing in possession nor a thing in action. That statutory negative removes the threshold objection. The content of the rights, and the availability of proprietary remedies, remain questions for ordinary legal and equitable doctrine. Trust structures over crypto-assets rest on firmer ground. Security interests can in principle be structured over digital tokens, although the form and effectiveness of that security will still depend on the asset, the control arrangements and the applicable collateral regime. Interim injunctions freezing specific on-chain assets are available on conventional proprietary principles. The pre-existing case law (AA v Persons Unknown, Fetch.AI v Persons Unknown and others) had already extended these remedies, but the statutory foundation removes the doctrinal uncertainty that defendants were beginning to exploit in contested hearings.

The UK Jurisdiction Taskforce Legal Statement on cryptoassets and smart contracts, published in November 2019, was the catalyst for this legislative programme.⁸ Its conclusion, that cryptoassets were capable of being owned and that smart contracts were capable of giving rise to binding legal obligations, was influential precisely because it was produced by senior practitioners and academics rather than by government. The Law Commission work and the 2025 Act represent the legislative endorsement of those conclusions.

Smart contract enforceability under English law now rests on the interaction between the 2025 Act (confirming proprietary status), the existing law of contract (offer, acceptance, consideration, certainty of terms) and equitable principles (constructive trust, unjust enrichment, knowing receipt). English law has not created a new body of “smart contract law.” It has confirmed that existing principles apply to this new form of transaction.

Governance Exploits vs Code Exploits

An analytical distinction is emerging in the case law and enforcement practice between exploits that target smart contract code and exploits that target governance infrastructure surrounding that code. The distinction carries legal significance because the two categories engage different duties and different liability frameworks.

On 21 February 2025, the Bybit exchange lost approximately USD 1.4 billion in digital assets.⁹ The attack did not exploit any smart contract vulnerability. The attackers, subsequently attributed to the Lazarus Group (North Korea’s state-sponsored hacking operation), compromised the development machine of a Safe{Wallet} front-end developer. They altered the user interface that Bybit’s signatories used to approve transactions, causing the display to show a legitimate transaction while the underlying payload transferred assets to attacker-controlled addresses.⁹ This was an attack on human trust in a user interface, not on the mathematical guarantees of smart contract code.

In April 2026, two further large-scale exploits demonstrated the same pattern. On 1 April, the Drift Protocol lost approximately $285 million through a governance mechanism exploit.¹⁰ On 18 April, Kelp DAO lost approximately $292 million through a cross-chain verification and control-layer failure.¹¹ Neither attack required breaking cryptographic assumptions or exploiting coding errors in the core smart contracts. Both targeted the governance and administrative layers that sit above the autonomous code.

Public blockchain-incident datasets converge on the same direction of travel, even if their classifications differ. TRM reported USD 2.87 billion stolen across nearly 150 hacks and exploits in 2025. SlowMist-linked reporting put the figure at roughly 200 incidents and more than USD 2.9 billion.¹² The exact count matters less than the pattern: the largest losses are increasingly associated with compromised keys, wallets, interfaces and control planes, not only with defective smart contract code.

The legal significance is direct. A pure code exploit, where a trader uses a protocol exactly as its code permits in the manner Eisenberg did at Mango Markets, may not engage traditional fraud or theft doctrines if the protocol’s rules do not prohibit the conduct. A governance exploit is different. Where attackers compromise administrative keys, corrupt oracle feeds through infrastructure attacks or manipulate front-end interfaces, the conduct engages fiduciary duties owed by key holders, tortious liability for negligent security practices and potentially criminal liability for unauthorised computer access. Protocol developers who hold administrative keys may owe duties to depositors, depending on the control retained, the representations made and the structure through which user assets are held. Multisig signatories who fail to implement adequate operational security may face claims in negligence.¹² The emerging analytical framework treats the smart contract itself as neutral infrastructure and locates legal liability in the human decisions surrounding its deployment and governance.

Strategic Outlook

The “code is law” thesis occupies a difficult position in 2026. As a defence for individuals who exploit protocols according to their rules, it has had its most significant judicial outing in Eisenberg, though the acquittal rested on venue and sufficiency rather than a broad endorsement of the principle. As a shield against sanctions, it succeeded in Van Loon: immutable contracts cannot be treated as blockable property under IEEPA. But as a comprehensive theory of how decentralised systems interact with legal order, it is failing. Courts are not creating a new body of autonomous digital law. They are absorbing smart contracts into existing categories: property (the 2025 Act), unincorporated associations (Ooki DAO), fraud (Eisenberg, albeit unsuccessfully) and sanctions (Tornado Cash).

The United Kingdom is building statutory infrastructure systematically. The Property (Digital Assets etc) Act 2025 addresses the proprietary question.⁵ The Law Commission’s private international law project, with its June 2025 consultation paper, addresses the cross-border question.⁷ The Law Commission has already published its DAO scoping paper. That paper did not recommend a DAO-specific legal entity, but it identified areas where further work may be needed if Government wants to clarify DAO status and regulatory reach. The approach is incremental, doctrinally conservative and designed to slot digital assets into existing legal architecture rather than construct parallel systems.

The United States remains a jurisdictional patchwork. The Eisenberg acquittal turned on venue, a question specific to the Southern District of New York.¹ The Fifth Circuit’s Tornado Cash holding binds courts within that circuit and will be persuasive, but not controlling, elsewhere.² The CFTC’s theory in Ooki DAO that DAOs are unincorporated associations has not been tested in contested litigation.⁴ No federal legislation specifically addresses smart contract liability. Venue selection and circuit-specific precedent may determine outcomes more than any unified theory of smart contract law.

For practitioners advising participants in decentralised finance, whether protocol developers, governance token holders, institutional depositors or claimants pursuing stolen funds, the current landscape requires jurisdiction-specific analysis. The question is no longer whether smart contracts create legal obligations (they do, on conventional contractual principles where the elements are satisfied) but rather where liability attaches when autonomous systems produce outcomes their participants did not intend. The answer, increasingly, is that liability attaches not to the code but to the humans who designed it, deployed it, governed it and profited from it.

Notes

1. United States v Eisenberg, No. 23-cr-10 (SDNY), opinion of Judge Arun Subramanian, 23 May 2025; commodities counts (counts one and two) vacated for want of venue; judgment of acquittal on wire-fraud count (count three) for insufficient evidence of venue and failure to prove falsity or material misrepresentation; jury had convicted on commodities fraud, commodities manipulation and wire fraud in April 2024; appeal pending, Second Circuit No. 25-1782.

2. Van Loon v Department of the Treasury, No. 23-50669, United States Court of Appeals for the Fifth Circuit, 26 November 2024; Roman Storm, SDNY: convicted August 2025 of conspiracy to operate an unlicensed money-transmitting business; jury deadlocked on money-laundering and sanctions-conspiracy counts. Prosecutors sought an October 2026 retrial on the deadlocked counts; Storm’s post-trial Rule 29 motion remained part of the procedural posture in 2026.

3. Office of Foreign Assets Control, delisting of Tornado Cash smart contract addresses, 21 March 2025.

4. CFTC v Ooki DAO, No. 3:22-cv-05416, Northern District of California, default judgment June 2023, civil monetary penalty of $643,542 and permanent trading and registration ban.

5. Property (Digital Assets etc) Act 2025, c. 29, Royal Assent 2 December 2025.

6. UK Law Commission, ‘Digital Assets: Final Report’ (Law Com No. 412, 2023).

7. UK Law Commission, ‘Digital assets and electronic trade documents in private international law’ consultation paper, 5 June 2025.

8. UK Jurisdiction Taskforce, ‘Legal Statement on cryptoassets and smart contracts’, November 2019.

9. Bybit exchange exploit, 21 February 2025, approximately USD 1.4-1.5 billion depending on valuation date, stolen via compromised Safe{Wallet} developer machine; attributed to Lazarus Group (DPRK).

10. Drift Protocol governance exploit, approximately $285 million, 1 April 2026.

11. Kelp DAO governance exploit, approximately $292 million, 18 April 2026.

12. TRM Labs, 2026 Crypto Crime Report, reporting USD 2.87 billion stolen across nearly 150 hacks and exploits in 2025. SlowMist, 2025 Blockchain Security and AML Annual Report, reporting 200 security incidents and approximately USD 2.935 billion in losses. Travers Smith, ‘DeFi exploits, on-chain interventions, and the private key: recent developments in crypto asset recovery’, 30 April 2026.

On 19 May 2026 the federal regime against non-consensual intimate imagery in the United States stopped being a piece of legislation and started being a compliance statute. The Federal Trade Commission published guidance and opened the enforcement window, after Chairman Ferguson had sent compliance letters to fifteen named platforms on 11 May.¹ A second round of warning letters followed on 20 May, addressed to twelve operators of generative ‘nudify’ tools.² The criminal limb of the TAKE IT DOWN Act has been in force since the President signed Public Law 119-12 on 19 May 2025. The new development is that the civil and platform-side regime now bites.

The Statutory Architecture in Two Parts

Public Law 119-12 splits cleanly. The criminal limb amends 47 U.S.C. section 223 to make the knowing publication of a non-consensual intimate visual depiction a federal offence punishable by up to two years’ imprisonment for adult publication offences and up to three years where the depicted individual is a minor.³ The platform limb sits at a new section, 47 U.S.C. section 223a, which creates the notice-and-removal duty and the procedural skeleton around it.⁴ Both limbs use the same definitional spine. ‘Intimate visual depiction’ is borrowed from 15 U.S.C. section 6851, the civil cause of action created by the 2022 reauthorisation of the Violence Against Women Act. The TAKE IT DOWN Act then adds the separate concept of a ‘digital forgery’, covering intimate depictions created or altered using software, machine learning, artificial intelligence or other computer-generated or technological means.⁵

That structure is important. The Act does not police platform conduct through the criminal limb. It polices it through the civil notice-and-removal duty enforced by the FTC. A platform is not a perpetrator. A platform is a recipient of valid removal requests with a 48-hour clock attached.

The FTC’s enforcement guidance reads the platform duty as applying to both authentic intimate depictions and AI-generated digital forgeries. CRS has identified a possible interpretive point in the statutory drafting, because section 223a’s notice-and-removal language is framed around ‘intimate visual depictions’ rather than separately repeating ‘digital forgeries’. That issue has not yet been litigated.

What the FTC Actually Did on 19 May

On the day enforcement opened, the Commission issued a press release confirming that Section 3 of the Act was now enforceable and published its own compliance guidance for businesses.⁶ Chairman Andrew Ferguson framed the regime in personal terms: ‘We stand ready to monitor compliance, investigate violations and enforce the Take It Down Act.’ He added that ‘in the age of AI, anyone can be targeted, and that becomes even more appalling if children are involved.’⁷

Before the enforcement date, Chairman Ferguson had sent compliance letters to fifteen major US consumer-facing platforms: Alphabet, Amazon, Apple, Automattic, Bumble, Discord, Match Group, Meta, Microsoft, Pinterest, Reddit, SmugMug, Snapchat, TikTok and X.⁸ A second round of letters on 20 May went to twelve operators of generative tools that produce non-consensual intimate imagery from clothed photographs. The Commission did not name those twelve in its release.⁹

The civil penalty mechanism follows the FTC Act. A violation of the platform duty is treated as an unfair or deceptive act or practice. The current per-violation civil penalty maximum, after the FTC’s annual inflation adjustment under 15 U.S.C. section 45(m), is USD 53,088. The Commission has confirmed that figure in its own enforcement guidance.

What the Removal Duty Looks Like in Practice

Section 223a sets out what makes a request valid and what the platform must then do. A valid request is in writing. It carries a physical or electronic signature of the depicted individual. It identifies the depiction and supplies sufficient information for the platform to locate it. It states a good-faith belief that the depiction is not consensual. It provides the platform with sufficient information to contact the requester.

From receipt of a valid request, the platform has 48 hours to remove the depiction. Within the same 48-hour window, the platform must also make reasonable efforts to identify and remove known identical copies. The statute does not impose a general duty to monitor proactively. It imposes a duty to act on what it has been told and what it can readily find.

The statute also confers a substantial safe harbour. A platform that disables or removes material in good faith based on facts from which non-consensual publication is apparent is not liable, regardless of whether the depiction is ultimately found to be unlawful.¹⁰ The architecture rewards conservative compliance. Faced with a request that looks credible, the rational economic choice is removal first and adjudication later.

Why Section 230 Is Not the Shield Here

TAKE IT DOWN does not amend 47 U.S.C. section 230. It does not need to. The platform exposure created by section 223a does not rely on treating the platform as the publisher or speaker of user content. It treats the platform as the operator of a regulated takedown system. Whether a platform owes a private-law duty to an aggrieved third party still runs through Section 230 in the usual way. Whether a platform has discharged its federal notice-and-removal duty under section 223a is now a separate question. It is enforceable by the federal regulator and not insulated by Section 230.

That is the practical engineering of the regime. Section 230 still does what it has always done in this area, which is to bar a great many private-law claims arising out of platform hosting decisions. The TAKE IT DOWN Act runs alongside it on a different track, with a different enforcer, a different standard and a 48-hour clock.

The State Overlay

The federal regime is a floor. As at early 2026, state-law trackers put the number of US states addressing sexually explicit deepfakes at roughly 45 to 46, depending on classification. All 50 states and the District of Columbia had some form of non-consensual intimate imagery protection in place.¹¹ Several of the state regimes reach further than TAKE IT DOWN and several attach sharper remedies.

Texas is illustrative. The first iteration of section 21.165 of the Texas Penal Code reached only deepfake videos. 2025 legislation, enacted through SB 441, closed that loophole and extended the prohibition to sexually explicit deepfake media, including still images as well as video.¹²

California is the other obvious comparator. California’s civil route began with AB 602, which created Civil Code section 1708.86, and has since been expanded, most recently by AB 621, effective 1 January 2026. SB 926, in force from 1 January 2025, criminalises the intentional creation and distribution of AI-generated sexually explicit deepfakes where the distributor knows or should know the content will cause serious emotional distress. SB 981 imposes a parallel platform mechanism, requiring social media platforms to provide a reporting route, temporarily block reported content while it is assessed and remove it from public view if it qualifies as sexually explicit digital identity theft.¹³

The federal-state interaction will matter for compliance design. A platform that builds a TIDA-compliant intake will satisfy the federal duty but may not, on its own, satisfy the additional state duties on response time, private rights of action or scope. The forward planning question for general counsel is no longer whether to build a takedown system. It is whether to build one system that satisfies the strictest applicable state regime or to layer the federal floor with jurisdiction-specific overlays.

The First Amendment Question

Whether the Act survives first contact with the First Amendment is the most consequential open question hanging over the regime. The Congressional Research Service noted, before the Act was even passed, that because it regulates speech on the basis of its content, it could attract strict scrutiny if challenged. That formulation is conditional for a reason. A court may instead conclude that some or all of the regulated material sits inside an unprotected category, or sufficiently close to one, that a less demanding standard applies.

The opposition is well-marshalled. The Electronic Frontier Foundation argues that the notice-and-removal regime is overbroad. Its definitions reach beyond the clearest category of non-consensual intimate imagery. The 48-hour clock incentivises platforms to remove first and assess later. The absence of a counter-notice mechanism or meaningful anti-abuse process will, on the EFF’s reading, produce over-removal in practice.¹⁴ The Center for Democracy and Technology, the Authors Guild, Freedom of the Press Foundation and others have aligned with the EFF on the censorship and due-process concerns.

The counter-argument has force. Non-consensual intimate imagery has been treated for two decades by US criminal and civil law as material in which the depicted individual retains a recognisable privacy and dignitary interest sufficient to defeat a general free-speech objection. The notice-and-removal regime also tracks the architecture of existing takedown systems. But the safe harbour reduces the platform’s legal risk from good-faith removal, which is exactly why critics say the statute will favour over-removal.

The realistic prediction is not that strict scrutiny will or will not apply. It is that the first facial challenge is a matter of when, not whether. The standard a court applies will be the heart of the case.

What to Watch in the Next Ninety Days

Four developments will shape the regime through the second half of 2026.

First, the FTC’s first enforcement action. The Commission has chosen warning letters in the first instance rather than an immediate sweep. The platform that becomes the test case for a 48-hour-clock missed deadline will set the early posture of the regime.

Second, the first facial challenge. The civil liberties coalition has been organised on this point since before the Act was passed. A likely doctrinal challenge would be brought by a platform with standing, on overbreadth and prior-restraint grounds. It would put the strict-scrutiny question to a federal court squarely.

Third, state attorneys general. The state-law regimes are now layered over a federal floor. State AGs will continue to bring criminal and civil actions against perpetrators and, in California, to test SB 981’s platform reporting duties. The resulting body of decisional law will give platforms more concrete operational guidance than the FTC’s compliance blog.

Fourth, the smaller-platform problem. The fifteen named platforms can build dedicated trust-and-safety operations. The longer tail of forums, image-sharing services, messaging apps and adult-content platforms cannot. The TAKE IT DOWN Act applies to them on the same terms. The realistic compliance burden will fall disproportionately on the operators least equipped to bear it.

TAKE IT DOWN does not amend Section 230. It creates a parallel FTC-enforced notice-and-removal duty at 47 U.S.C. section 223a, backed by civil penalties and reinforced by a widening state-law deepfake overlay. The duty has moved upstream from litigation defence to compliance operations. The 48-hour clock is now a feature of every covered platform’s intake. The question is whether, when the first First Amendment challenge arrives, that 48-hour clock survives in its current form.

Notes

1 Federal Trade Commission, ‘FTC Chairman Ferguson Advises Companies to Comply with the Take It Down Act’, press release, 11 May 2026; Federal Trade Commission, ‘FTC Begins Enforcing the TAKE IT DOWN Act’, press release, 19 May 2026; Federal Trade Commission, ‘Take It Down Act enforcement starts now: What to know about the FTC and TIDA’, business guidance blog, 19 May 2026.

2 Federal Trade Commission, ‘FTC Sends Warning Letters to Companies About Compliance with the TAKE IT DOWN Act’, press release, 20 May 2026.

3 TAKE IT DOWN Act, Public Law 119-12 (signed 19 May 2025), section 2, amending 47 U.S.C. section 223.

4 TAKE IT DOWN Act, section 3, adding 47 U.S.C. section 223a ‘Notice and removal of nonconsensual intimate visual depictions’.

5 15 U.S.C. section 6851 (Violence Against Women Act Reauthorization Act of 2022, definition of ‘intimate visual depiction’), as incorporated by reference in 47 U.S.C. section 223a. ‘Digital forgery’ is separately defined in the TAKE IT DOWN Act and codified in 47 U.S.C. section 223(h).

6 Federal Trade Commission, ‘FTC Begins Enforcing the TAKE IT DOWN Act’, press release, 19 May 2026.

7 Federal Trade Commission, ‘FTC Chairman Ferguson Advises Companies to Comply with the Take It Down Act’, press release, 11 May 2026; Federal Trade Commission, ‘FTC Begins Enforcing the TAKE IT DOWN Act’, press release, 19 May 2026.

8 Federal Trade Commission, ‘FTC Chairman Ferguson Advises Companies to Comply with the Take It Down Act’, press release, 11 May 2026; addressees listed in alphabetical order by corporate name.

9 Federal Trade Commission, press release of 20 May 2026. The Commission referred to the addressees only as ‘twelve operators of online services that offer artificial-intelligence-powered nudification tools’.

10 47 U.S.C. section 223a (safe harbour for good-faith disabling or removal).

11 StackCyber, ‘Deepfake Legislation Tracker: Federal and State Laws’, updated spring 2026; supplementary review of state legislative trackers as at March 2026.

12 Texas Penal Code section 21.165, as amended by SB 441, 89th Texas Legislature, signed 20 June 2025 and effective 1 September 2025.

13 California Civil Code section 1708.86 (AB 602, 2019), as expanded by AB 621 (effective 1 January 2026); California Penal Code section 647(j) as amended by SB 926 (2024, effective 1 January 2025); California Business and Professions Code as amended by SB 981 (2024).

14 Electronic Frontier Foundation, ‘The TAKE IT DOWN Act: A Flawed Attempt to Protect Victims That Will Lead to Censorship’, 5 February 2025; Center for Democracy and Technology and others, joint letter to the United States Senate, February 2025.

A paper to be presented at ICAIL 2026 in Singapore (Xu et al., University of Copenhagen and Umeå, posted to arXiv on 19 May 2026) sets out the first systematic evaluation of large language models on a task that the legal information industry has spent more than a century industrialising: generating the propositional statements that summarise what a case stands for. In American legal practice these are called headnotes. In doctrinal scholarship they are called legal propositions. They are the working unit of legal reasoning, the building blocks of textbooks and the index entries that allow lawyers to find authority on a point.

The study tested three open-source LLMs (GPT-OSS 120B, OLMo-3-7B-Instruct and the legal-specialised Saul-7B-Instruct) on ten decisions of the Court of Justice of the European Union. Two legally trained annotators (a final-year law student and a research assistant with a PhD in law) scored the hundred propositions generated by the two top-performing models (GPT-OSS and OLMo-3) against a five-dimension rubric the authors call LP-Eval.

The rubric does three things in sequence. It first checks that each proposition contains three required components: Stance (a normative position), Object (the legal rule itself, not a factual summary of the case) and Specification (the conditions or scope under which the rule operates). It then scores quality on a 1-3 scale across five dimensions (Source Independence, Fact Independence, Conciseness, Generality and Fidelity). Finally it asks for an overall quality score on the same 1-3 scale. Decomposing the assessment this way improves reliability and gives the rubric a second life later in the paper as a prompt template for LLM judges.

The headline numbers

Ninety-five of the hundred propositions were rated formally valid. The five that failed all failed for the same reason: the model summarised the facts of the case but did not articulate the legal rule (what the rubric calls the Object component). Mean overall quality was 2.5 out of 3, with fact independence at 2.96 and fidelity at 2.95.

On the surface this is a positive result for legal tech. Off-the-shelf models can generate competent doctrinal summaries of European jurisprudence with only an expert-crafted prompt, at least on the scored outputs from GPT-OSS and OLMo-3.

The recency gap

The recency gap is what sits beneath the headline numbers: the measured difference in LLM proposition quality between well-established CJEU authorities and recent decisions. The authors deliberately divided their sample into well-established cases (highly cited, spread across time) and recent decisions. Propositions drawn from the well-established cases scored a mean of 2.66. Those drawn from recent cases scored 2.35. The gap is significant at p<0.001 and is driven principally by source independence, the dimension that captures whether a proposition can stand alone as a statement of law rather than as a paraphrase of the underlying paragraph. On that dimension, established cases score 2.52 and recent cases 2.11.

The explanation flagged by the authors is a combination of case-prominence effects and possibly training-data memorisation; the paper expressly notes the limits of causal analysis on this point. The well-established cases have been discussed in textbooks, commentary, case notes and other model outputs that LLMs may have ingested. The model behaves as though it is reproducing settled doctrine rather than reasoning to it independently. On recent, less-commented cases the model produces close paraphrase and direct citation rather than a standalone legal proposition.

What this means for the headnote industry

For the legal information industry this is a more interesting finding than the headline 95 per cent validity figure suggests. Westlaw and LexisNexis built their commercial value on the editorial work of human lawyers writing headnotes. Modern LLM output can replicate that work on cases that are already heavily edited and commented on. It is materially worse on the cases of highest editorial value to the publisher, those decided last week.

Two consequences follow. Automated headnote pipelines will be most reliable on the material that already has good human headnotes and least reliable on material where the publisher would otherwise add the most value. Legacy citation networks then take on a self-reinforcing quality: the more a case is written about, the better the model performs on it; the better it performs, the more text it generates about that case; that text becomes training data for the next generation.

This is structural concentration. A handful of well-established authorities will be progressively easier for machines to summarise. Newly decided cases will sit in a persistent quality gap until enough commentary accretes to bring them into the training set of the next model.

The judge that cannot judge novelty

The second half of the paper tests whether LLMs can grade the work of other LLMs. With rubric guidance, GPT-OSS reaches Gwet’s AC1 of 0.91 to 0.93 with the two human annotators; inter-expert agreement is 0.94. Without the rubric, GPT-OSS agreement falls to 0.85.

The critical limitation is that LLM judges do not detect the gap between well-established and recent cases. Human annotators caught it at p<0.001. The model judges did not register a statistically significant difference. An evaluation pipeline built on LLM-as-judge would risk certifying recent-case outputs as equivalent to established-case outputs, which is the precise opposite of the correction a quality-assurance system would need to make.

For any organisation considering an automated legal research stack, that finding is the one to internalise. The model that drafts the headnote and the model that audits the headnote share the same blind spot. Detecting where the system fails requires the very expertise the system is meant to replace.

The LP-Eval paper links to a public companion GitHub repository for the appendix, dataset and code. The arXiv paper itself is licensed under CC BY 4.0.

The FTC began enforcing the TAKE IT DOWN Act on 19 May 2026, one year to the day after President Trump signed it.¹

The compliance posture that platforms now occupy is the product of three separate regulatory theories, each treating the same underlying harm. The three are not fully aligned.

A takedown duty in parallel with Section 230

The Act criminalises the publication of non-consensual intimate images, including computer-generated images depicting identifiable individuals, and requires covered platforms to remove such content within 48 hours of a valid notice.² The civil penalty is $53,088 per violation under FTC Act enforcement principles.³

The Act does not amend Section 230 of the Communications Decency Act. It runs parallel to it. The Act includes a safe harbour for good-faith disabling or removal but offers no equivalent protection for under-removal. Section 230 does not provide a defence to an independent federal statutory notice-and-removal duty enforced by the FTC. The structural effect, if not the formal one, is that Section 230 is displaced for this category of content.

The architecture invites comparison with the DMCA notice-and-takedown framework from 1998. The DMCA created a conditional safe harbour: failure to comply risks losing that safe harbour and facing ordinary copyright liability. TIDA adds an affirmative FTC-enforced removal duty backed by civil penalties.

Criminalising creation, the UK approach

The UK’s response operates at the source rather than the platform layer. Section 138 of the Data (Use and Access) Act 2025 amended the Sexual Offences Act 2003 to create a criminal offence of intentionally creating a “purported sexual image” of an identifiable person without consent.⁴ The offence took effect on 6 February 2026 and covers both AI-generated images and conventionally manipulated ones.

Platforms operate within a separate regime. The Online Safety Act 2023 treats non-consensual intimate images as a priority offence within the illegal-content duties. Failure to operate proportionate removal systems can attract Ofcom penalties of up to 10% of worldwide turnover and, in extreme cases, service-blocking orders.⁵ The UK regulator has the larger stick. The US regulator has the lower threshold of intervention.

Transparency at the model layer, the EU approach

The EU does not yet treat non-consensual intimate deepfakes through a dedicated removal duty. From 2 August 2026, Article 50 of the AI Act addresses the upstream problem by requiring providers of generative systems to mark synthetic outputs in a machine-readable format. The Digital Services Act handles downstream notice-and-action for illegal content through hosting-service mechanisms, with additional risk-assessment obligations for VLOPs (Very Large Online Platforms) and VLOSEs (Very Large Online Search Engines).

The EU theory is that the harm is mitigated by detectability and procedural diligence. The US theory is that the harm requires a hard removal deadline. The UK theory is that the harm requires a criminal prohibition on the upstream act. The three are not contradictory but they impose different compliance designs on the same platform.

Practitioner implication

A platform with users in all three jurisdictions cannot run one workflow. The trigger for action differs (a US notice, a UK priority-offence designation, an EU complaint or risk-assessment finding), the actor differs (the FTC, Ofcom, the European Commission and national DSA coordinators) and the penalty calculus differs by orders of magnitude. The rational design is to operate to the lowest common denominator on response time and to the highest common denominator on transparency. That points toward 48-hour removal as the operational floor and machine-readable provenance as the technical ceiling once Article 50 applies.

The Section 230 question worth watching is whether TIDA’s structure becomes a model for other categories. If notice-and-removal with civil penalties survives constitutional challenge in this domain, the case for extending it to adjacent ones, including AI-generated defamation and impersonation, will be made.

Code on Trial will track that case as it develops.

References

¹ Federal Trade Commission, “FTC Begins Enforcing the TAKE IT DOWN Act,” press release, 19 May 2026.

² Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks Act (TAKE IT DOWN Act), Public Law 119-12 (2025), section 3 (notice-and-removal duty).

³ Civil penalty figure adjusted under the Federal Civil Penalties Inflation Adjustment Act; FTC business guidance, May 2026.

⁴ Data (Use and Access) Act 2025, section 138, amending the Sexual Offences Act 2003.

⁵ Online Safety Act 2023, illegal-content duties under Part 3 and priority offence schedules; Ofcom enforcement powers under Part 7.

The Bartz v Anthropic fairness hearing on 14 May 2026 saw little resistance to the headline number. But the class definition reaches the registered US works problem. It does not reach the global corpus problem.

The Hearing

On 14 May 2026 the Northern District of California held the final fairness hearing in Bartz et al v Anthropic PBC, Case No. 3:24-cv-05417, the USD 1.5 billion proposed class settlement covering eligible registered works Anthropic allegedly downloaded from pirate libraries. Judge Araceli Martínez-Olguín took the matter under submission. She inherited the docket from Judge William Alsup, who retired at the end of 2025, after the June 2025 summary judgment that held training on lawfully acquired books to be fair use while leaving the pirated central-library theory exposed for trial.¹

By the date of the hearing 447,576 of approximately 482,460 eligible works had been claimed, a rate of 92.77 percent. Class counsel from Lieff Cabraser characterised the opt-out and objection rates as minimal. The judge’s questioning concentrated on attorneys’ fees and the settlement’s cost structure rather than the substantive deal terms.²

The Registration Trap

The most consequential objection came from author George Tombs, whose works were excluded because they lacked a US copyright registration. The class definition restricts membership to works that satisfy the settlement’s eligibility criteria, including qualifying US registration. The predictable consequence is that many foreign-authored books, and many unregistered US works, fall outside the settlement entirely.

The exclusion is statutory. Section 411(a) requires registration as a precondition for bringing a US infringement action in respect of a United States work. Section 412 restricts statutory damages and attorneys’ fees where registration was not timely made, and that remedy restriction applies to foreign and domestic works alike.³ Neither provision extinguishes the underlying copyright, which subsists automatically in any work qualifying under Article 5(2) of the Berne Convention.⁴ Foreign authors whose works lack qualifying US registration, and other unregistered rightsholders, retain whatever underlying copyright they have but sit outside the USD 1.5 billion settlement structure.

The Comparative Position

The German position has begun to be clarified by the OLG Hamburg judgment in Kneschke v LAION, 5 U 104/24, 10 December 2025.⁵ The court held that section 44b UrhG, which implements Article 4 of Directive (EU) 2019/790, can permit text-and-data mining for AI-training datasets where no valid machine-readable opt-out has been declared. It also held that LAION, as a non-commercial research organisation, could rely separately on section 60d UrhG.⁶ The judgment matters for foreign authors with respect to Anthropic because Anthropic is a commercial enterprise. Section 60d is unlikely to assist it, and section 44b is available only where rightsholders have failed to opt out in a machine-readable form. If the relevant copying occurred before machine-readable opt-out mechanisms were standardised or widely implemented, the German exposure will turn on how courts treat that timing problem.

In the United Kingdom, the Government’s March 2026 copyright and AI report confirmed that a broad text-and-data-mining exception with opt-out is no longer its preferred way forward.⁷ The section 29A research exception remains narrow. Where the relevant copying occurs within the UK, unauthorised commercial training may be actionable as primary infringement under section 16 of the Copyright, Designs and Patents Act 1988, subject to proof of copying, territorial nexus and any applicable exception.⁸ Getty Images v Stability AI is the principal English authority to date, but it left the training-stage question only partly answered because the primary copyright claim was narrowed by jurisdictional and evidential issues. UK collective management organisations and author groups are obvious potential claim-coordination vehicles, but the procedural route remains unsettled.

In France, the infringement baseline rests on article L122-4 of the Code de la propriété intellectuelle. The text-and-data-mining exceptions sit in articles L122-5 and L122-5-3, following implementation of the CDSM Directive by Ordonnance n° 2021-1518.⁹ The opt-out architecture is materially similar to Germany’s. France is an obvious forum for coordinated rights-holder action, but the procedural route remains to be seen.

The Strategic Implication

The unresolved question is whether the US per-work figure of approximately USD 3,000 sets a reference point for parallel European proceedings. There is no doctrinal reason it should. Damages in European jurisdictions are typically calculated by reference to a notional licence fee or to an account of profits, not to settlement values reached in unrelated US class actions. The reference may still operate informally. Anthropic’s commercial incentive will be to characterise the US settlement as substantial compensation already provided. The counter is that excluded European authors and unregistered rightsholders received nothing under that settlement and their underlying rights remain unimpaired.

If approved, Bartz will close the US registered-works class. It will not close the international copyright map. European proceedings may determine whether the global cost of the allegedly pirated training corpus remains a US-class-settlement number or becomes a materially larger cross-border exposure.

Footnotes

¹ Bartz et al v Anthropic PBC, Case No. 3:24-cv-05417 (ND Cal); Order on Motion for Summary Judgment, 23 June 2025 (Alsup, J.).

² Authors Alliance, “Bartz v. Anthropic Fairness Hearing: Observations and Takeaways”, 14 May 2026; Publishing Perspectives, “Anthropic Settlement Appears to Cruise Through Its Final Fairness Hearing”, 15 May 2026.

³ 17 U.S.C. sections 411(a), 412, 504(c).

⁴ Berne Convention for the Protection of Literary and Artistic Works, Article 5(2).

⁵ OLG Hamburg, Kneschke v LAION, Case No 5 U 104/24, 10 December 2025.

⁶ Directive (EU) 2019/790, Article 4; Urheberrechtsgesetz, sections 44b and 60d.

⁷ UK Government, Report and Impact Assessment on Copyright and Artificial Intelligence, March 2026.

⁸ Copyright, Designs and Patents Act 1988, sections 16 and 29A.

⁹ Code de la propriété intellectuelle, articles L122-4, L122-5 and L122-5-3; Ordonnance n° 2021-1518.

Introduction

On 18 April 2026, Sullivan & Cromwell LLP wrote to Chief Judge Martin Glenn of the United States Bankruptcy Court for the Southern District of New York after Boies Schiller Flexner identified AI-generated errors in a Chapter 15 filing in the Prince Global Holdings proceedings. The reported errors included inaccurate citations, misstatements or misquotations of bankruptcy law and other drafting defects that should have been caught before filing.¹ The incident matters not because elite firms are uniquely vulnerable, but because it shows that AI risk has moved from the margins of legal practice into the core workflow of sophisticated firms.

The professional liability question is becoming unavoidable: when does reliance on AI constitute negligence, and when might failure to use AI become negligent? Those questions appear to point in opposite directions. In fact, they are converging. The emerging standard is not anti-AI and it is not pro-AI. It is a standard of competent, supervised and verified use.

The Scale of the Problem

The Sullivan & Cromwell incident is not isolated. It is the most prominent recent example of a wider failure pattern. Damien Charlotin’s AI Hallucination Cases database tracks legal decisions in which generative AI has produced hallucinated content, or where AI use has been addressed by a court or tribunal in more than a passing reference. The database is important, but its methodology matters: it does not purport to capture every false citation in every filing. It captures the court-facing subset that has reached judicial or tribunal attention.²

As at 15 May 2026, Charlotin’s tracker identified 1,450 matters worldwide, including 1,003 in the United States.³ The precise number changes quickly. The direction of travel does not. What began as a few notorious filing failures has become a recurring operational risk in litigation practice.

The origin point in public consciousness remains Mata v Avianca. In June 2023, Judge P. Kevin Castel of the Southern District of New York imposed a $5,000 Rule 11 sanction after lawyers submitted an affirmation containing six non-existent judicial opinions generated by ChatGPT.⁴ At the time, much of the profession treated Mata as a cautionary novelty. By 2026, that reading is no longer sustainable.

The sanctions environment has hardened. In Couvrette v Wisnovsky, the District of Oregon found that summary judgment briefing contained 15 non-existent cases and eight fabricated quotations. The financial outcome was not a single fine, but a combination of a $15,500 sanctions order and later fee and cost orders that together exceeded $110,000.⁵ The distinction matters: the case should not be described simply as a ‘$110,000 fine’. Its real significance is broader. Courts are increasingly willing to make the cost of verification failure fall on the lawyers and parties responsible for putting false material before the court.

Court-level requirements are also multiplying. As at May 2026, Legal AI Governance identified 113 active orders and rules binding attorney filings.⁶ The requirements vary. Some require disclosure of AI use. Some require certification that AI-assisted work has been checked. Others simply restate that Rule 11, candour and professional responsibility duties apply irrespective of the tool used. The common message is that ‘AI did it’ is not a defence.

The Regulatory Response

The American Bar Association responded with Formal Opinion 512 on 29 July 2024, its first comprehensive ethics opinion on generative AI in legal practice. The opinion addresses competence, confidentiality, communication with clients, billing, candour to the tribunal and supervisory responsibilities under Model Rules 1.1, 1.4, 1.5, 1.6, 3.3, 5.1 and 5.3.⁷ The central proposition is simple: a lawyer may use generative AI, but the lawyer remains responsible for the work product.

Formal Opinion 512 does not require lawyers to understand the model architecture of every system they use. It does require them to understand the capabilities and limitations of the tools sufficiently to use them competently. It also requires lawyers to protect confidential information, communicate with clients where AI use materially affects the representation, verify AI-generated legal analysis and citations before submission, supervise junior lawyers and non-lawyers using AI and avoid billing clients for time spent correcting avoidable AI errors.

California has moved towards a more prescriptive model. In 2026, the State Bar of California sought public comment on proposed amendments to six Rules of Professional Conduct addressing AI: competence, communication, confidentiality, candour to the tribunal, managerial responsibility and supervision of non-lawyer assistants.⁸ If adopted, the proposals would make verification explicit. A proposed comment to the competence rule states that, when using technology including AI, a lawyer must independently review, verify and exercise professional judgment over the output. A proposed comment to the candour rule specifically requires verification of the accuracy and existence of cited authorities before submission to a tribunal.

That is not yet the same as an enacted rule. The California material should therefore be described as a proposed amendment, not as binding black-letter law. Its significance lies in the direction of regulatory movement: duties that were previously implicit in competence and candour rules are being translated into AI-specific drafting.

Colorado is useful for a different reason. It has both a disciplinary example and a rule-based development. In People v Crabill, a Colorado lawyer received a suspension of one year and one day, with 90 days actively served, after using ChatGPT-generated case law without reading or verifying it, failing to alert the court to sham cases and then falsely attributing the errors to a legal intern.⁹ Separately, the Colorado Supreme Court approved Rule Change 2026(02), which adds commentary making clear that technology, including AI, does not diminish a lawyer’s professional responsibilities and that a lawyer who uses technology in delivering legal services may be subject to discipline for a resulting rule violation.¹⁰

These regulatory materials share a common architecture. They treat AI as a tool, not as an independent professional actor. They impose verification duties. They reject any presumption that AI-generated legal output is reliable. They locate the obligation within existing competence, candour, confidentiality and supervision frameworks rather than creating a wholly new category of professional regulation.

The English Position

The leading English authority is Ayinde v London Borough of Haringey and Al-Haroun v Qatar National Bank, heard together and decided by the Divisional Court in June 2025.¹¹ The judgment is often described as an AI case, but that shorthand needs care. The court was dealing with the actual or suspected use of generative AI by lawyers to produce written legal arguments or witness statements that were not checked, resulting in false information being put before the court.

In the Ayinde matter, the underlying problem was the inclusion of five fake cases in judicial review materials. The provenance of the false authorities was contested; the barrister denied using AI. Ritchie J nevertheless made wasted costs orders of £2,000 each against Ms Forey and Haringey Law Centre and required referral to the Bar Standards Board and the Solicitors Regulation Authority.¹² The Divisional Court later held that the threshold for initiating contempt proceedings was met in relation to Ms Forey, but decided not to initiate contempt proceedings or refer the case to the Law Officers.¹³

The practical guidance from the judgment is more important than the disputed factual mechanics. The court stated that freely available generative AI tools such as ChatGPT are not capable of conducting reliable legal research. They may cite sources that do not exist, quote passages that do not appear in genuine sources and produce confident assertions that are simply untrue. Lawyers who use AI for legal research, or rely on others who have done so, have a professional duty to check the output against authoritative sources before using it in advice or before a court.

The Solicitors Regulation Authority has not adopted an ABA-style AI ethics opinion. Its approach remains principles-based. On 22 April 2026, however, the SRA opened its consultation, ‘Strengthening our continuing competence approach’, running until 15 July 2026.¹⁴ The consultation proposes stronger requirements for solicitors to record how they identify and address learning and development needs and to participate in annual ethics discussions. It is not an AI-specific rulebook, but it sits alongside the SRA’s broader AI risk materials and compliance guidance on AI and technology.¹⁵

The Law Society’s guidance on generative AI likewise has persuasive rather than binding force. Its September 2025 update expressly added references to Ayinde and Al-Haroun, and it now stresses verification against reliable and authoritative sources, supervision and risk management.¹⁶ The English position is therefore less prescriptive than the emerging American model, but not less serious. The courts have made clear that false authorities in court documents may trigger wasted costs orders, regulatory referral and, in an appropriate case, contempt proceedings.

The Emerging Dual Standard

Professional liability in the age of generative AI is developing along two vectors at the same time. The first is negligent reliance. A lawyer who submits AI-generated legal material without proper verification may breach duties of competence, candour, supervision and care to the client. That proposition is now strongly supported by the cases, ethics opinions and regulatory materials.

The second vector is less developed but potentially more disruptive: the possibility that failure to use AI may, in some circumstances, fall below the standard of care. This proposition should not be overstated. No court has yet held a lawyer negligent simply for failing to use AI. Nor is there a general duty to use every available technology. The better formulation is narrower: where a particular AI tool has become a reasonably standard, reliable and proportionate means of improving the relevant task, a professional who fails to consider or deploy it may struggle to justify the omission if the client suffers avoidable loss.

That argument is consistent with orthodox professional negligence principles. The standard is not perfection. It is the standard of a reasonably competent professional in the relevant circumstances. In English law, Bolam and Bolitho provide useful analogies, particularly the idea that a practice accepted by a responsible body of professionals must also withstand logical scrutiny. In the solicitor context, the same essential question appears in a different form: what would a reasonably competent practitioner have done, judged by the standards of the profession at the time?

Anurag Bana’s SSRN paper on artificial intelligence, legal professional negligence and AI-covered indemnity risk articulates the point clearly: as AI becomes prevalent in legal practice, liability may arise both from using AI incorrectly and from failing to use it where its use would have been reasonably expected.¹⁷ Recent professional negligence commentary takes a similar position: AI is not generally mandatory, but the real question is whether a reasonably competent professional would have used it in the client’s interests in the particular circumstances.¹⁸

The point is easiest to see in verification. A lawyer who asks a public chatbot to produce case law and files the result without checking it is plainly exposed. But consider the inverse case: a firm has access to a reliable citation-checking, document-comparison or disclosure-analysis tool; the tool is widely used for the task; the cost of using it is proportionate; and the error that later causes loss is precisely the kind of error the tool would probably have caught. In that scenario, failure to use AI is not negligence because AI exists. It is negligence, if at all, because the professional failed to use an available and reasonably standard quality-control method.

The practical tension is acute. A lawyer who uses AI without verification may be negligent. A lawyer who refuses to use AI where it has become an ordinary part of competent practice may also become exposed. The safe ground is not abstention. It is disciplined adoption: use AI where it adds value, understand its limits, verify its output, supervise its use and preserve human professional judgment as the final decision-making layer.

Indemnity and Insurance Implications

Professional indemnity insurers now face the dual standard as both a coverage and pricing problem. The negligent reliance vector produces familiar claims in new clothing: failed applications, adverse costs orders, lost procedural opportunities, confidentiality breaches and client losses caused by unverified work product. The failure-to-use vector is more novel: claims alleging that a lawyer failed to use an available technology that would have prevented the loss.

Coverage questions remain unresolved. United States commentary on lawyers’ professional liability policies notes that many policies do not expressly exclude AI use, but coverage may depend on whether the conduct falls within the policy definition of professional services and whether exclusions for intentional acts, fraud, fee disputes or technology failures are engaged.¹⁹ Some professional liability insurers have also begun experimenting with AI-specific exclusions or endorsements.²⁰

The hardest cases will sit between negligence and recklessness. An isolated failure to check an AI-assisted draft may be characterised as negligence. Repeated submission of fabricated authorities after warning signs have been raised, or a deliberate refusal to verify citations known to have come from a generative tool, may be characterised very differently. Coverage will turn on policy wording, governing law and the factual findings in the underlying claim.

The NAIC Model Bulletin on the use of AI systems by insurers, adopted in December 2023, is relevant but only indirectly.²¹ It addresses insurer use of AI in insurance operations, including governance, risk management and compliance with insurance law. It does not solve the professional indemnity question of how insurers should underwrite or respond to claims arising from insured lawyers’ use, misuse or non-use of AI.

For underwriters, the risk is now two-sided. Traditionally, technology risk in professional indemnity was framed as a risk of using defective systems. AI introduces the additional possibility of technology abstention risk: the allegation that competent practice required the use of a tool and the insured failed to use it. That does not mean underwriters should require blanket AI adoption. It means proposal forms, renewal questions and risk engineering will need to move beyond asking whether a firm uses AI and start asking how AI is governed, supervised, verified and documented.

Strategic Outlook

The standard of care is moving. Expected work product quality will rise as reliable AI-assisted methods become normalised. Firms that ban AI entirely face one liability vector. Firms that permit uncontrolled AI use face another. Both approaches are inferior to governed use.

The first strategic imperative is verification. Every firm should have a clear rule that AI-generated legal authorities, quotations, factual propositions and analytical conclusions must be checked against authoritative sources before submission to a court, delivery to a client or use in advice. The rule should apply not only to partners and associates, but also to trainees, paralegals, knowledge teams, external consultants and anyone else contributing to legal work product.

The second imperative is supervision. AI use should be treated like any other delegated work stream. A partner is not excused because the first draft came from an AI tool rather than a junior lawyer. A recent US sanction against a managing partner for a junior lawyer’s AI-related citation error illustrates the same point in operational terms.²² The supervision question is the same: who checked it, against what source, using what process and where is the evidence that the check occurred?

The third imperative is training. The profession has spent too much time debating whether AI should be used and too little time teaching practitioners how to use it responsibly. Competence training needs to cover prompt design, tool selection, confidentiality, privilege, hallucination risk, citation verification, document comparison, disclosure workflows, billing treatment and escalation protocols. The SRA’s continuing competence consultation points in this direction even if it does not prescribe AI-specific rules.

The fourth imperative is insurance engagement. Firms should not wait for a claim before asking how their professional indemnity policy treats AI-assisted legal work. They should understand whether AI use is within the scope of covered professional services, whether any AI-specific exclusions apply, whether sanctions and fee-shifting orders are covered and what notification obligations arise when an AI-related error is discovered.

The dual standard is likely to sharpen over the next three to five years. Courts will continue to sanction negligent reliance. At some point, a failure-to-use allegation is likely to be tested in a professional negligence claim, most probably where a client can show that a readily available verification, disclosure or drafting tool would have caught the error that caused the loss. The claim may or may not succeed. Its arrival should surprise no one.

The profession cannot resolve this tension by choosing one vector over the other. It can resolve it only by occupying the disciplined middle ground: deploying AI tools competently, verifying their output rigorously, supervising their use systematically and maintaining human judgment as the irreducible core of legal practice. Firms that master that discipline will reduce cost, improve quality and protect themselves. Firms that do not will be exposed in both directions: for unverified AI reliance and, in time, potentially for failing to adopt standard AI-assisted checks.

Notes

1. Reuters, ’Sullivan & Cromwell law firm apologizes for AI hallucinations in court filing’, 21 April 2026; Legal Cheek, ’Sullivan & Cromwell apologises after AI hallucinations appear in court document’, 22 April 2026.

2. Damien Charlotin, AI Hallucination Cases Database, methodology note: database tracks legal decisions where the use of AI, whether established or merely alleged, is addressed in more than a passing reference by a court or tribunal; it does not track the wider universe of all false citations or uses of AI in court filings.

3. Damien Charlotin, AI Hallucination Cases Database, last updated 15 May 2026, identifying 1,450 cases worldwide and 1,003 USA matters. The database is live and should be rechecked immediately before publication.

4. Mata v Avianca, Inc., No. 22-cv-1461, 678 F Supp 3d 443 (SDNY 2023), sanctions order dated 22 June 2023.

5. Couvrette v Wisnovsky, No. 1:21-cv-00157-CL (D Or), Opinion and Order on sanctions, 12 December 2025, 2025 WL 4109655; Opinion and Order on fee/cost allocation, 23 March 2026, ECF No. 225; subsequent merits order, 30 March 2026, ECF No. 227.

6. Legal AI Governance, Federal and State Court Orders on AI tracker, identifying 113 active orders and rules binding attorney filings.

7. American Bar Association, Formal Opinion 512, Generative Artificial Intelligence Tools, 29 July 2024.

8. State Bar of California, Proposed Amendments to the Rules of Professional Conduct Related to Artificial Intelligence, public comment material, 2026; public comment deadline 4 May 2026.

9. People v Zachariah C. Crabill, 23PDJ067, Colorado Office of the Presiding Disciplinary Judge, 22 November 2023.

10. Colorado Supreme Court, Rule Change 2026(02), approved 8 January 2026.

11. Ayinde v London Borough of Haringey and Al-Haroun v Qatar National Bank QPSC [2025] EWHC 1383 (Admin), Divisional Court, 6 June 2025.

12. R (Ayinde) v London Borough of Haringey [2025] EWHC 1040 (Admin), Ritchie J; summarised in the Divisional Court judgment at [2025] EWHC 1383 (Admin).

13. Ayinde and Al-Haroun [2025] EWHC 1383 (Admin), discussion of contempt threshold and decision not to initiate contempt proceedings.

14. Solicitors Regulation Authority, Strengthening our continuing competence approach, consultation opened 22 April 2026 and closing 15 July 2026.

15. Solicitors Regulation Authority, Risk Outlook report on the use of artificial intelligence in the legal services sector, updated 23 April 2026; SRA compliance tips on AI and technology, updated 9 February 2026.

16. The Law Society of England and Wales, Generative AI: the essentials, 1 October 2025, Updates: September 2025.

17. Anurag Bana, ’Artificial Intelligence, Legal Professional Negligence and the Rise of AI-Covered Indemnity Risk’ (SSRN, abstract dated 2025; PDF posted 9 April 2026).

18. Cripps, ’When is it negligent for a professional to use or ignore AI?’, 7 May 2026.

19. Reuters Legal News, ’From innovation to exposure: artificial intelligence risks for legal professionals’, 14 July 2025.

20. Reuters Legal News / Westlaw Today, ’Insuring against productive laziness: attorney use of artificial intelligence’, 22 December 2025.

21. National Association of Insurance Commissioners, Model Bulletin: Use of Artificial Intelligence Systems by Insurers, adopted 4 December 2023.

22. Reuters Legal News, ‘US judge says senior lawyers must pay for mistakes by subordinates using AI tools’, 1 May 2026.