AI Token Securities Analysis: When Does AI Utility Create an Investment Contract? (2026)

Bottom line up front: There is no SEC rule, no Commission-level interpretive release, and no reported federal decision that addresses “AI tokens” as a category. Classification is therefore resolved the same way it was in 2017 — under SEC v. W.J. Howey Co., 328 U.S. 293 (1946) — with one twist that matters enormously in practice: AI utility is unusually good at satisfying Howey’s fourth prong, not defeating it.

That runs against founder intuition. The prevailing assumption is that shipping genuine functionality — metered GPU rental, model inference, agent payments — converts a token from a security into a consumptive good. The Supreme Court’s decision in United Housing Foundation, Inc. v. Forman, 421 U.S. 837, 852–53 (1975) does support that logic: where a purchaser is “motivated by a desire to use or consume the item purchased,” the securities laws do not apply. But most AI token architectures are deliberately built so that the network’s technical output — inference quality, model benchmark performance, GPU supply growth — is the same thing purchasers are told will drive token price. Under Howey, that is a promise of profit derived from the managerial and technical efforts of others. The better the AI story, the stronger the SEC’s fourth-prong argument.

Prong Four Is the Whole Case

Howey’s first three elements — investment of money, common enterprise, expectation of profit — are rarely seriously contested in token litigation. Money is paid; horizontal commonality is usually conceded because purchaser fortunes are tied to a shared pool of protocol assets; and SEC v. Edwards, 540 U.S. 389, 394–95 (2004) forecloses the argument that fixed or predictable returns fall outside the definition. Litigation lives and dies on the fourth prong: whether profits are expected “solely” — read as “predominantly,” per the SEC’s Framework for ‘Investment Contract’ Analysis of Digital Assets (FinHub, Apr. 3, 2019) — from the efforts of a promoter or third party.

Three AI token archetypes sit in very different places on that spectrum. (Projects are named below only to identify architectural patterns; nothing here is a conclusion about any specific token’s status.)

1. Compute and Resource Marketplaces

Networks in the Render or Akash mold price the token as payment for a metered, real-world resource: GPU-seconds, CPU, storage. This is the strongest Forman consumption posture available in the sector. A buyer who acquires tokens to rent rendering capacity looks like the co-op resident buying shares to occupy an apartment, not to profit.

Three design choices routinely destroy that posture: (a) burn-and-mint or supply-cap mechanics marketed as deflationary and value-accretive; (b) staking rewards paid to holders who never supply or consume compute; and (c) a foundation that unilaterally sets pricing oracles, emission rates, or fee burns. The third is the most damaging — it is precisely the “essential managerial efforts which affect the failure or success of the enterprise” that Howey targets. Compare the SEC’s Division of Corporation Finance no-action letters in Turnkey Jet, Inc. (Apr. 3, 2019) and Pocketful of Quarters, Inc. (July 25, 2019): both required immediate full functionality, a fixed price untethered to speculation, and marketing limited to use rather than appreciation. Almost no AI compute token meets all three conditions at launch.

2. Model and Inference Incentive Networks

Bittensor-style architectures — where holders stake to validators, and emissions flow to third-party miners whose models are scored by the network — present the hardest facts. Here the “efforts of others” are literally the efforts of others: independent operators produce the models that generate the returns. Passive stakers contribute capital and receive a yield determined by the entrepreneurial performance of participants they do not control.

The Division of Corporation Finance’s May 29, 2025 statement on protocol staking offers less comfort than commonly assumed. It addressed self-custodial and delegated staking on proof-of-stake networks where the staker’s activities are “administrative or ministerial” in nature. Staking that funds, directs, and is rewarded by third-party model production is not obviously administrative. Critically, that statement — like the Feb. 27, 2025 memecoin statement, the Mar. 20, 2025 mining statement, and the Aug. 5, 2025 liquid-staking statement — is staff-level, expressly non-binding on the Commission, and revocable by a future administration. It is not a safe harbor.

3. Data and Agent-Service Tokens

Data marketplaces and autonomous-agent payment rails (the Fetch.ai / Ocean / SingularityNET lineage, now consolidated under the Artificial Superintelligence Alliance) are the most fact-dependent category. Where the token is a bare settlement medium between an agent and a data provider, the consumption argument is credible. Where the sponsor markets a roadmap in which future agent adoption drives token demand, the analysis collapses back into Howey.

The Marketing Record Usually Decides the Case

Every significant token loss for issuers has turned on promotional statements, not code. In In re Munchee Inc., Securities Act Release No. 10445 (Dec. 11, 2017), the SEC found an app-utility token to be a security based almost entirely on the issuer’s pitch about ecosystem growth and secondary-market appreciation. In SEC v. LBRY, Inc., No. 21-cv-260-PB (D.N.H. Nov. 7, 2022), the court held that LBRY’s own statements about token appreciation created a reasonable expectation of profit notwithstanding genuine functional use on the network. SEC v. Telegram Group Inc., 448 F. Supp. 3d 352 (S.D.N.Y. 2020) and SEC v. Kik Interactive Inc., 492 F. Supp. 3d 169 (S.D.N.Y. 2020) reached the same place through pre-functionality distribution.

Counsel should be cautious about over-relying on the programmatic-sales holding in SEC v. Ripple Labs, Inc., 682 F. Supp. 3d 308 (S.D.N.Y. 2023). It is a single district court ruling; its reasoning was pointedly declined weeks later by Judge Rakoff in SEC v. Terraform Labs Pte Ltd., 684 F. Supp. 3d 170 (S.D.N.Y. 2023); and because both parties abandoned their appeals in 2025, no circuit-level guidance exists. Building a distribution strategy on Ripple is building on an unaffirmed district opinion.

AI-Washing: A Second, Independent Exposure

Even if a token survives Howey, misrepresenting the AI creates liability on its own track. The SEC’s March 18, 2024 settled actions against Delphia (USA) Inc. and Global Predictions Inc. established the template: overstating machine-learning capabilities is a garden-variety misrepresentation case. Securities Act § 17(a) reaches offers and sales; Exchange Act § 10(b) and Rule 10b-5 reach purchases and sales. Where the asset is a commodity rather than a security, CEA § 6(c)(1) and CFTC Rule 180.1 reach fraud in connection with a commodity in interstate commerce — and the FTC Act § 5 plus state UDAP statutes reach the rest. There is no configuration in which inflated AI claims are unregulated.

Where AI Rules and Crypto Rules Collide

For US issuers with EU users, two regimes stack. Under MiCA, Regulation (EU) 2023/1114, a token that is neither an ART nor an EMT still requires a crypto-asset white paper under Articles 6–9, and Article 15 imposes civil liability on the offeror for information that is “not complete, fair, or clear, or is misleading.” A white paper describing AI capability is a liability-bearing disclosure document — functionally the same exposure as a § 17(a) claim in the United States, litigated in a different forum.

Layered on top, the EU AI Act, Regulation (EU) 2024/1689, imposes Article 50 transparency duties on systems that interact with natural persons or generate synthetic content, and Article 53 documentation and copyright-policy duties on general-purpose AI model providers (applicable since Aug. 2, 2025). Article 6 and Annex III high-risk classification — including Annex III point 5(b) on creditworthiness evaluation, which reaches on-chain AI credit scoring — carries an August 2026 application date that has been the subject of Commission delay proposals. Verify the current status before relying on any date.

Domestically, Texas’s TRAIGA (H.B. 149), Colorado’s S.B. 24-205, and California’s S.B. 53 and A.B. 2013 are not securities statutes. But each generates mandatory AI disclosures that plaintiffs’ counsel will read against the token’s offering materials. Inconsistency between an AI Act technical file, a state AI disclosure, and a token white paper is a scienter exhibit.

Acknowledged Regulatory Uncertainty

• No Commission-level guidance addresses AI tokens; all current comfort is staff-level and non-binding.
• The CLARITY Act (H.R. 3633), passed by the House on July 17, 2025, would route “digital commodities” to the CFTC via a control-and-decentralization test. Its Senate status must be confirmed as of publication date.
• Commissioner Peirce’s proposed token safe harbor was never adopted.
• Whether the token itself, or only the contract surrounding its sale, is the security remains unresolved above the district level.

Key Takeaways

• Functional AI utility does not defeat Howey. Forman protects consumptive purchases; it does not protect tokens whose consumptive use is marketed as the engine of price appreciation.
• Prong four is the battlefield. The question is whether an identifiable group’s ongoing entrepreneurial effort — foundation, core devs, subnet miners — determines returns.
• Marketing beats architecture. Munchee and LBRY both found securities despite real utility, based on the issuer’s own public statements.
• AI-washing liability is independent of classification. § 17(a), Rule 10b-5, CEA § 6(c)(1), and FTC Act § 5 collectively leave no gap.
• 2025 staff statements are not safe harbors. They bind no one and can be withdrawn without notice.

What to Do Now

1. Run a marketing audit before a code audit. Pull every X post, Discord announcement, podcast appearance, and deck from the last 24 months. Score each against the 2019 Framework’s “reasonable expectation of profits” factors. This is where the SEC’s case gets built.
2. Separate consumption demand from speculative demand in the token design. Price compute in stable units, cap or eliminate yield to non-participants, and remove supply-scarcity language from all public materials.
3. Document the decentralization record contemporaneously. Track founder token concentration, commit distribution across independent contributors, validator concentration, and governance-proposal authorship — dated and preserved. A control test written into any future market-structure statute will be applied retroactively to these facts.
4. Substantiate every AI claim to an evidentiary standard. For each performance representation, maintain the benchmark, the date, the methodology, and the person who verified it. Treat this as SOX-grade documentation.
5. Reconcile disclosure documents across regimes. Align the MiCA white paper, any EU AI Act Article 53 documentation, state AI disclosures, and US offering materials. Assign one owner to that reconciliation.
6. Assume no exemption without one. If the token may be a security, structure the raise under Reg D, Reg S, or Reg A+ rather than relying on a utility characterization that has never won at the appellate level.

This content is for informational purposes only and does not constitute legal advice. Consult with a qualified US attorney for guidance specific to your situation.