Just shy of 10 years ago, a crested macaque monkey in the jungles of Indonesia pressed the shutter button of a photographer’s camera, taking a selfie of himself smiling. The ensuing photo went on to be massively viral and sparked a unique debate: Who owns the picture if a human didn’t take it?
Figure 1. The “monkey selfie” taken by Naruto, a crested macaque, in Indonesia.
The Supreme Court answered this in the landmark case Naruto the Monkey v. Slater (2018). The case asked the question: if copyright laws serve to “promote the progress of science and useful arts” by granting “authors and inventors the exclusive right to their respective writings and discoveries”, as outlined in Article 1, Section 8, Clause 8 of the US Constitution, then how could a non-human (or in this case, a monkey) fulfill those requirements? The argument made on behalf of Naruto was that he had “constitutional standing” to the ownership of his own likeness; however, the counterargument responded that he may not have the ability to file for Copyright under the Copyright Act of 1976. Though it may not come as a surprise, the Supreme Court decided that “this monkey—and all animals, since they are not human—lacks statutory standing under the Copyright Act”.
What makes this significant is how this case shaped future copyright debates, namely, those concerning Artificial Intelligence and artificially generated images. As a result of the previous ruling that copyright can only be held and created by a human, Naruto v. Slater serves as precedent for a much more recent case. In 2024, computer scientist and copyright theorist Dr. Steven Thaler asked the US Copyright Office to register and protect his artwork, “A Recent Entrance to Paradise”. The only problem: Dr. Thaler’s own AI System called “the Creativity Machine” had generated the image entirely on its own, and Dr. Thaler listed it as the sole author in the official registration documents. The Office denied his application for copyright registration, and upon appeal, found itself in court with Thaler.
Figure 2. “A Recent Entrance to Paradise,” generated by Dr. Steven Thaler’s AI system, the Creativity Machine.
It was quickly ruled and upheld that “the Creativity Machine cannot be the recognized author of a copyrighted work because the Copyright Act of 1976 requires all eligible works to be authored in the first instance by a human being,” despite the appellant’s argument that “he is the work’s author by virtue of making and using the Creativity Machine”. Using the same logic used to deny Naruto his copyright, the courts denied the Creativity Machine’s (and therefore Thaler’s) copyright.
Although Thaler’s story ends there, it raises an interesting question. If the law allowed an AI model to hold copyright of the work it creates, who would hold the limited monopoly the copyright provides: the person who built the machine, or the machine itself?
As copyright legislation continues to grow and adapt to the rise of AI and other complicated technologies, it is important to remember the origins of these rights. The Constitution guarantees these protections to promote the progress of science and the useful arts. Therefore, it should be of the highest priority to speak with Congress in constructive and progressive dialogues that meet the demands these technologies create. For instance, one demand of AI technology is on our energy infrastructure and data storage. In turn, it is up to our government to support this growing demand and, more importantly, ensure the proper measures of guidance and regulation are in place for such development to be effective and safe.
We dive more into how U.S. governance frameworks are still evolving to keep pace with these rapidly advancing technologies on our Artificial Intelligence & Quantum page here.
There are many ways that legislators can attempt to reconcile these conflicting issues. They could choose to simply revisit the Copyright Act itself, given how much the digital world has changed in the past 50 years since the act was written, or simply abandon it in favor of a new system. Regardless, lawmakers must balance previous rulings and established judicial precedent with the rapidly changing landscape of technology development. It is fair to say that this problem will not be resolved overnight, and we can expect many more years of litigation before a clear legal picture is painted.
The Digital Chamber applauds the OCC for issuing final rules implementing the GENIUS Act, expected to take effect in January 2027. Through continued stakeholder engagement, the OCC is helping ensure the United States remains the digital finance capital of the world and payment stablecoins reinforce U.S. dollar dominance. We are pleased to see several of TDC’s recommendations reflected in the final rules, including narrowing the “yield prohibition” to align with Congressional intent, giving digital asset service providers the flexibility to offer services needed to compete, and not requiring identification of secondary-market stablecoin holders. TDC and its members look forward to continued engagement with the OCC and fellow regulators as implementation continues.
This summer, a student research team at George Mason University’s Costello College of Business Future of Finance Lab (FFL) tested whether stablecoin growth shows up in the money supply, bond yields, and U.S. Treasury holdings of 25 countries. The paper, “Assessing the Influence of Stablecoin Adoption on Dollar Dominance from Cross-Country and Monetary Data,” comes from Sarah Dalmet, Aiden Lee, Christina Liu, Mishka Kandhari, and FFL’s Derek Horstmeyer, PhD. It draws on new country-level stablecoin data from Allium and monthly figures that run from mid-2021 to mid-2026.
TDC thanks the authors, and Dr. Horstmeyer in particular, who walked TDC through the results. The team assembled the data and ran the regressions in about three months.
What they found
Country by country, stablecoin growth showed no clear relationship with narrow money (M1), and no sign that stablecoins displace direct holdings of U.S. Treasuries. Bond yields produced isolated results at most. A pooled regression across all countries also found no relationship.
Figure 1. Each dot is one country row in the paper’s results table. Source: Table 1, Dalmet et al. (2026).
One result held up. In Indonesia, Romania, Singapore, and Taiwan, stablecoin growth moved with broad money (M2). Dr. Horstmeyer was careful on two points: (1) this is an association, not causation and, (2) the team’s working read runs from money → stablecoins (when a country’s money supply grows, residents convert more of it into dollar stablecoins). The team cannot yet explain why these four countries stood out – a long decline in the local currency was the first idea, but it does not fit Singapore’s economic conditions.
Why it matters
Two findings bear directly on our work. The first is the direction of the M2 result. If new local money is flowing into dollar stablecoins, residents are converting at the margin. They are not draining existing deposits – the bank-funding risk the IMF flagged last December. The second is the null on Treasury holdings. The data show no sign that savers who buy dollar stablecoins buy fewer Treasuries directly, which is consistent with what we noted in 2024: stablecoins add to global demand for the dollar and lower U.S. borrowing costs.
TDC’s view
The study does not test our tariff argument. It has no exchange rate outcome, no tariff episodes, and country-specific data access is still growing. Null results across 25 countries cannot rule out a channel that operates under stress in a few economies.
Our hypothesis that dollar stablecoins act as geoeconomic tools in trade rate disputes stays open, and the study window predates the changes ahead. The GENIUS Act takes full effect by January 2027, and cross-border stablecoin value more than doubled between January 2025 and June 2026. We will not put a date on when the channel shows up in national data, but Dr. Horstmeyer suggested running these tests again in about four years to capture impacts after more significant market penetration.
With roughly 100 country-level tests, a few significant results can appear by chance. We read the M2 pattern as a lead worth chasing, and the team plans to keep chasing it.
What’s next
The Future of Finance Lab plans to keep building on this work with more complete data and a path to causal identification, and Dr. Horstmeyer’s team expects the picture to look different as the series lengthens. We are talking with our global developments team about where to look next.
If your organization has data, questions, or funding that could help, FFL wants to hear from you. TDC also welcomes researchers willing to do the hard work of testing emerging tech hypotheses, so that policy on the Hill rests on facts. FFL’s research collection also covers cross-border payments, how AI agents choose between stablecoins, and perpetual futures trading. The full paper and the collection are available on the Future of Finance Lab website.
The legal footing under U.S. tariffs has shifted twice this year. The Supreme Court struck down IEEPA tariffs on February 20, 2026. A Section 122 stopgap expired on July 24, and the same day USTR imposed Section 301 tariffs on 60 economies, at 10% or 12.5% depending on their forced labor import rules. While rates moved, strategic responses did not. A country hit by U.S. tariffs has always had one reliable answer – devaluing its own currency – and dollar stablecoins now give that country’s savers a way around it.
How the currency defense works
Countries targeted by tariffs usually weaken their currencies, which cheapens exports and offsets the tax. The math is simple (Figure 1). Fully offsetting a tariff of t takes a depreciation of:
d=t1 + t
A 12.5% tariff takes about 11%. A 46% tariff takes about 32%. China’s 145% peak in April 2025 would have taken about 59%. In 2018, Brookings’ Robin Brooks wrote, the average U.S. tariff on China was 12.5% and the yuan fell almost exactly as much, a de facto full offset. (Figure 2)
Figure 1. Depreciation needed for a full offset equals t / (1 + t). Source: author’s calculation.
Figure 2 (Left). China’s official $/CNY fix, 2012 to 2024. Source: Bloomberg, via Robin Brooks, Brookings, January 16, 2025. Figure 3 (Right). Drivers of China’s official FX reserve accumulation, 2010 to 2024. Source: Haver Analytics, via Robin Brooks, Brookings, January 16, 2025.
Depreciation has a price the exporter does not pay. It cuts the value of local savings, and savers who expect depreciation move their money into other assets as avenues become available. China saw this in 2015 and 2016, when depreciation fears drove capital flight through bank-intermediated channels despite strict controls. (Figure 3)
While foreign leaders may seek to diversify their economies and reduce reliance on the U.S. dollar to retain policy independence and shield against U.S. geopolitical maneuvering… those countries’ citizens are increasingly prioritizing acceptance, stability, liquidity, and the backing of a strong referent currency. Stablecoins allow users to realize this preference for U.S. dollars.
A government that weakens its currency is betting that savers stay put. Dollar stablecoins give them an exit pathway. About 97% of stablecoin issuance is dollar-pegged. A July BIS working paper found that stablecoin flows appear largely unaffected by capital controls, and that they share their drivers with old-style dollar deposits: exchange rate dynamics and financial crises. In Vietnam, USDT averaged a 3.35% premium in 2024, and about 7.8% of remittances arrive as stablecoins.
How big is the leak?
Right now? Small, and growing from almost nothing. The IMF reports that stablecoin holdings rose from virtually zero in 2020 to 1.5% of total deposits in Africa and the Middle East and 2.7% in Latin America and the Caribbean by 2024. Chainalysis finds that monthly cross-border stablecoin value more than doubled, from $11 billion in January 2025 to $24 billion in June 2026. The IMF treats lost control of domestic liquidity as a risk that arrives once a significant share of activity shifts. We are not there yet, but there is a trend.
Figure 4. Stablecoin holdings as a share of total deposits, 2024. Source: IMF, Understanding Stablecoins, December 2025 (Figure 8).
States already see the threat
Governments increasingly are identifying dollar stablecoins as a sovereignty problem. China bans crypto outright. Vietnam’s licensed pilot requires settlement solely in dong and limits tokenized assets to those backed by real assets, which keeps dollar stablecoins out of the legal market. Permissionless architectures complicate this, though – licensing governs exchanges, but can’t govern wallets directly.
TDC’s view
This is a statecraft question, rather than a market question. Dollar stablecoins extend U.S. monetary reach into economies at odds with Washington, regardless of the tech’s intended design. Tariff targets assume they can answer with currency devaluation, and that assumption now may no longer be safe.
We cannot yet point to a tariff that stablecoins neutralized, and the first systematic data, from George Mason University, shows no aggregate effect so far. But the exit exists and volumes are rising. Policymakers weighing tariffs should treat the currency defense as weaker than it was in 2018.
The cost of this capability lands unevenly, and this tradeoff is often not made explicit. Washington gains deeper demand for dollars. Savers in tariffed economies gain protection from depreciation their governments engineered. Those governments lose room to direct their own monetary policy, and the savers adopt issuer and platform risk that deposit insurance does not cover. Dollar stablecoins are not neutral infrastructure – they are inseparable from U.S. geoeconomic statecraft.
What’s next
GENIUS is law. President Trump signed it on July 18, 2025, its core provisions take effect by January 18, 2027, and Treasury’s August proposal on unlawful issuance is open for comment until October 19. Supply sits near $300 billion, off a May peak near $310 billion.
Treasury’s final GENIUS rules will decide how foreign users and foreign issuers fit into the system. States in trade disputes with Washington will answer with their own licensing rules, capital controls, and state digital currencies, and each will struggle to overcome the capabilities introduced by digital assets and sovereign wallets. In a follow-up post, we look at new country-level research from George Mason University’s Future of Finance Lab that tests whether stablecoin growth shows up in money supply, bond yields, and Treasury holdings.
Sources
U.S. Trade Representative, “USTR Takes Action in Forced Labor Section 301 Investigations,” July 23, 2026. Effective date (12:01 a.m. ET, July 24, 2026): RV Industry Association, https://www.rvia.org/news-insights/section-122-tariffs-replaced-new-section-301-forced-labor-tariffs. IEEPA ruling date: USA-ASEAN Business Council (link in text). April 2025 46% rate for Vietnam: Executive Order 14257, April 2, 2025.
The Digital Chamber, “How Stablecoins Are Extending U.S. Dollar Dominance,” November 17, 2024.
Robin Brooks, “China, tariffs, and Trump 2.0,” Brookings Institution, January 16, 2025 (Figures 5 and 8 reproduced from this article).
Reuters, “Yuan Falls to 2007 Lows as US Tariffs on China Kick In,” April 9, 2025. The Investor (Vietnam), “Challenges for Vietnam’s monetary policy in 2026.”
Tiger Research, “Stablecoins as Vietnam’s Parallel Financial System,” May 20, 2025.
International Monetary Fund, “Understanding Stablecoins,” Departmental Paper, December 2, 2025.
Boris Hofmann, Aaron Mehrotra, and Jan Paulick, “Dollarisation and monetary control: what lessons for the rise of stablecoins?,” BIS Working Papers No. 1370, July 21, 2026.
Chainalysis, “2026 Global Crypto Adoption Index,” September 23, 2026.
Resolution 05/2025/NQ-CP (Vietnam), as summarized by ASL Gate and Russin & Vecchi.
Sullivan & Cromwell, “Treasury Department Proposes Rules Clarifying GENIUS Act Prohibitions,” August 2026.
Forbes, “The Stablecoin Market Shrank For The First Time In Four Years,” July 27, 2026; CryptoTimes, “Stablecoin Market Reaches $300.9B as Tron Gains $5B in Q3,” October 2, 2026 (RWA Foundation and Token Terminal data as of October 1, 2026).
On September 18, The Digital Chamber filed comments on NERC Project 2026-02, the first reliability standards written for computational loads: AI data centers, high-performance computing, and Bitcoin mining. The three standards cover interconnection, real-time operations, and disturbance monitoring. They apply to sites of 50 MW or more connected at 100 kV or above.
TDC supports the goal. Grid planners need to know how these loads behave, and developers need to know what the grid expects of them. But campuses with multiple connections, operating out of one site, need additional clarity to ensure that reporting obligations aren’t inappropriately levied on smaller partners at a site – a key point of our response. Additionally, TDC introduced the concept of decentralized distributed computational loads, and offered recommendations for new classifications in preparation for future mesh network buildouts.
Rules That Work for Every Operator
Compute is among the fastest-growing sources of demand on the grid, and projects need clear, workable rules. We see two areas where added clarity would help NERC reach its reliability goals:
Right-size costs across facility sizes. Modeling, monitoring, communications, and training cost about the same at a 50 MW site as at a 500 MW site, or roughly ten times more per megawatt. Costs that scale with system impact would keep small and mid-size facilities in the market.
Give distributed compute a clear home. Independently owned installations increasingly sell capacity into shared networks. Clarifying whether joining one makes a 2 MW owner a NERC-registered entity, and who is responsible when coordinated load moves across regions, would give owners and planners the same certainty.
NERC is finalizing Phase 1 while its roadmap sets Phase 2 priorities through 2027. TDC will stay engaged so NERC has the best information on current and future deployments.
TDC’s view
Reliability obligations should attach to the controller of electrical demand. Our comments ask NERC to:
Treat location as decisive. Common ownership, a shared scheduler, or a shared workload arrangement should not merge separate installations into one regulated site.
Define the aggregator by what it does. We proposed three new terms – Distributed Compute Resource, Dispatch Coordination, and DCR Aggregator – that place obligations on the entity coordinating load, modeled on FERC Order No. 2222 for distributed energy.
Separate control from commerce. Selling capacity a site can decline is not a change in grid behavior and shouldn’t trigger a restudy. Transfer of control, however, should.
Protect exit rights. Small operators must be able to leave an arrangement freely. Otherwise, the registered entity gains commercial control over its participants’ access to the grid.
Keep compliance proportionate. That means 18 to 24 months for first-time registrants, a materiality test for model re-verification, triggered data retention, and data limits tied to electrical behavior.
We also encouraged NERC to approach fast workload transfers between regions as a capability planners can see and coordinate, rather than only a risk to manage.
Getting these details right will help smaller operators, who carry the heaviest per-megawatt burden, build and compete alongside the largest campuses.
What’s next
NERC’s drafting team will review comments before revising the standards, and we look forward to working with the team and other stakeholders through that process. TDC has also recommended a dedicated Phase 2 workstream on coordinated distributed compute through the Large Loads Working Group. Our filing includes a supporting annex, “Distributed Compute Resources and the Aggregation Gap,” which details that framework.
TDC shares NERC’s objective: computational growth that proceeds alongside a reliable and resilient electric grid. Members running or planning computational load should contact the TDC policy team to share how these standards affect them.
As AI is increasingly integrated into our lives and national infrastructure, The Digital Chamber (TDC) has built policy principles to ensure that these vital systems preserves innovation, resiliency, safety, and a responsible federal-state regulatory harmonization that provides clear direction and protections for relevant stakeholders in the AI ecosystem. Specifically, the United States must refine regulations to leverage decentralized AI and AI development that supports open and democratic governance – technological remedies to many legacy AI roadblocks and concerns.
TDC supports the following principles that we intend to use to guide our AI policy engagement:
1. Mitigating Bias: Bias Audits Should Be Effective, Outcomes-Based, Transparent, and Public
AI systems should be tested for fair and non-discriminatory outcomes across their full lifecycle, not certified only once at launch.
TDC supports making bias-audit results available for regulator and public review, with the FTC and state attorneys general able to act where a consistent pattern of failed audits reveals discriminatory outputs. Bias audits should be tied to reasonable mitigation requirements, and at no point should mitigating bias be treated as the introduction thereof. Responsibility for biased outputs should generally rest with the deployer, the entity closest to the person affected, rather than falling equally on every upstream developer. The goal is a standard that measures results, rather than intent, as AI is definitionally incapable of producing ‘intent’ necessary for disparate-treatment claims.
2. Model Safety: Safety Disclosure Should Scale with Use Case, Not Model Size
Safety testing should be continuous and include:
pre-deployment evaluation
active monitoring,
an established process for addressing failing systems in real time
TDC supports disclosure requirements for safety testing results, calibrated to expected use case, user base, and associated risks, rather than model size alone. Systems deployed in healthcare, hiring, housing, or against a vulnerable population should meet a higher bar than a narrow tool with limited exposure, regardless of scale. Risk thresholds should reflect input from the people who bear the consequences of failure, and the workforce that identifies harm(s)deserves protections of its own.
Further, catastrophic risks have received outsized attention compared to more common violations of user rights and well-being. Both catastrophic and quotidian risks must be addressed.
3. Liability and Accountability: Accountability Should Follow Supply-Chain Role
Liability should track function: developers and deployers should each be accountable for harms tied to their specific role, and content-neutral infrastructure providers in a decentralized AI operating system should not be shoehorned into one of these existing categories.
Developers supply capability and are typically liable for defects in the underlying model (e.g., Meta is the developer of the Llama series). A party that modifies an existing model also takes on developer obligations where that modification expands the capability surface, alters the action space, or degrades safeguards;
Deployers refine capability and put it into use. This covers parties that fine-tune a model to narrow it within the intended purposes and documented limitations, and the entity that chooses to place a system into service. Deployers sit closest to the affected person and carry the greatest share of responsibility for outcomes in use. A deployer that narrows a model should publish its intended-use documentation and test for drift within that window;
End Users query or operate an AI system. Where the user and the affected person are the same, accuracy limits are disclosed, and a free and prompt remedy is available, liability should not run upstream for use outside the developer’s/deployer’s stated parameters. This limit does not apply where the affected person is not the operator, as in lending, hiring, housing, healthcare, and criminal justice.
These categories describe function, not identity, and are not mutually exclusive. A power user self-hosting a model may be developer, deployer, and end user at once. Contractual allocation should not displace the factual distribution of control.
Obligations also presuppose a party able to discharge them. Where a layer is permissionless and no entity controls access or output, assigning developer or deployer duties there accomplishes nothing or makes recentralization the only compliance path. Accountability should attach to parties exercising control, applied equally to concentrated and distributed infrastructure.
This chain should also make room for a distinct category of content-neutral infrastructure providers, including decentralized compute, storage, data-availability, and validation networks that do not select or control the models running on them, who should not inherit developer or deployer liability absent knowing participation in a specific harm (see Principle 9).
Each party should be able to explain the portion of the process it controls, and affected individuals should receive that explanation without requesting it or litigating for it.
4. Governance and Oversight: Human Oversight Is the Floor; Direct Governance Can Exceed It
Human oversight of consequential AI decisions – in lending, hiring, healthcare, housing, and criminal justice, among other high-risk domains – remains essential, as is government accountability.
Where a community (like a blockchain-based AI DAO) holds governance rights, that direct governance can fulfill accountability requirements.
Worker organizations and unions should be included as participants in setting oversight standards for AI directly impacting workers in a stakeholder model. Agentic AI should inherently carry identification metadata.
Further, that identification should extend to verifiable tamper-evident action logs, so an autonomous agent’s decisions can be attributed to a responsible party after the fact rather than reconstructed through litigation (see Principle 13). Mechanisms that build oversight directly into execution, such as network-level alignment nodes, illustrate how decentralized systems can make governance a property of the infrastructure rather than an afterthought.
5. Data Privacy and Sovereignty: Individuals Should Hold a Strong Claim to Their Own Data
Meaningful, rather than purely symbolic or perfunctory, control over personal and session data is essential.
Principled data sovereignty requires that people natively own their data. Privacy-preserving infrastructure, like decentralized identity and zero-knowledge methods, can help realize these protections.
Regulators should treat these privacy-enhancing technologies, including trusted execution environments and confidential computing, secure multi-party computation, and federated approaches, as affirmative compliance pathways deserving of safe-harbor treatment, rather than viewing their use with suspicion.
Reasonable limits on data brokers and concentrated data control can help enhance privacy where individual-level tools fall short. TDC also supports updated de-identification standards that account for AI-enabled re-identification risk.
6. Redefining “Democratic AI”: Legitimacy Comes from Governance, Not Governments
Democratic legitimacy is measured by how directly an AI stack’s power and governance is checked and how widely it is distributed, rather than by which government’s flag sits on a developer’s S1.
TDC believes AI development should build toward open, auditable, and decentralized AI, governed by meaningful direct input from the people it affects. Where that level of direct governance isn’t yet possible, legislative direction and generic guardrails can be a reasonable alternative.
Widespread use of a technology is not the same as widespread control over it. A system available to everyone and governed by one company is not democratic. Democratic AI should embody the following qualities:
Both control and usage are distributed. Many parties can run a system, but that is not the test. What matters is whether any single entity can unilaterally change the model, revoke access, or set the terms everyone else operates under.
The stack is open and forkable. Weights, training data, and code are published under terms that permit modification and redistribution. ‘Open source’ is a necessary but insufficient quality for Democratic AI.
Governance rights are enforceable. People affected by a system can change how it operates.
Decentralization also carries a resilience and security dividend that policymakers should weigh directly: infrastructure with no single point of control or failure reduces systemic risk and concentrated dependency. Advancing this standard should include interoperability, data and model portability, and open protocols, so that no single incumbent can capture the AI stack through lock-in.
7. Shared Societal Benefit: Democratizing AI Should Extend to Democratizing Its Gains
AI-driven labor disruption calls for near-term investment in reskilling and a modernized safety net, tracking impact as it happens rather than after the fact.
TDC believes that economic gains of AI should be widely distributed through consultation with civil society and affected populations. Workers and communities displaced by AI infrastructure should have a meaningful path to share the benefits of that infrastructure, rather than simply support for the disruption it causes.
Democratizing AI should extend beyond its oversight to who benefits from the tech, which should itself be decided democratically. Concretely, this can be delivered through verifiable on-chain attribution and micropayments to the people who contribute data, compute, and storage, and through participation in the networks those contributions help run. Such mechanisms distribute gains by design rather than only redistributing them after the fact (see Principle 12).
8. Verifiable AI:Cryptographic Proof Should Be a Recognized Path to Compliance
Compliance should be something a regulator can check, rather than something a developer asserts.
Audits, disclosures, and self-certification all ultimately ask the public to trust the party being regulated. TDC supports recognizing cryptographic verification, meaning proof of what model ran, on what data, and with what result, established through methods such as verifiable inference, provenance attestation, trusted execution environments, and zero-knowledge proofs, as a valid and, where available, preferred means of demonstrating compliance. A standard built on “verify, don’t just trust” lets oversight keep pace with systems too numerous and fast-moving to certify one at a time.
9. Neutral Infrastructure:Content-Neutral Providers Should Have a Clear Safe Harbor
Providing the pipes should not mean answering for the water.
The compute, storage, data-availability, and validation networks that AI runs on are typically content-neutral: they do not choose, train, or control the models they carry. TDC supports a clear safe harbor establishing that operating such infrastructure does not, by itself, make a party a developer or deployer, absent known participation in a specific harm. Without this, the decentralized and distributed infrastructure that makes open AI possible cannot operate at scale in the United States.
10. Digital Assets: Network-Incentive Tokens Deserve Regulatory Clarity
Decentralized AI cannot run on infrastructure that has no lawful way to reward its participants.
Decentralized networks coordinate and compensate their compute providers, storage providers, validators, and other contributors through functional tokens. TDC supports clear federal treatment confirming that tokens used to coordinate infrastructure and reward genuine network participation are not, by default, treated as securities, along with workable clarity for staking and other consensus mechanisms. Resolving this question, now under active debate in digital-asset market-structure legislation, is a precondition for a domestic decentralized-AI industry rather than a matter separate from AI policy.
11. Open Models:Releasing Open-Weight AI Should Be Presumptively Protected
Openness is both a safe and competitive strategy.
Open-weight and open-source models let independent researchers inspect, test, and improve systems that would otherwise be opaque. TDC supports treating the release of open-weight models as presumptively protected, so that a developer is not automatically liable for every downstream use of an openly released model and we are cautious about blanket reporting or restriction regimes aimed at open release. Where safety concerns arise, verifiability (Principle 8) is the better answer than closure: open and verifiable is more accountable than closed and unexamined.
12. Data Provenance:Contributors Should Be Traceable and Compensable
If data is the raw material of AI, its providers should be visible in the record and able to share in the value.
Verifiable data pipelines make it possible to record what data trained a model and to trace contributions back to their source. TDC supports on-chain, privacy-respecting data provenance together with mechanisms for attribution and compensation, including micropayments, to the creators and communities whose data trains AI systems. This advances content authenticity and consent while giving the shared-benefit commitment in Principle 7 a concrete mechanism.
13. Agentic AI:Autonomous Agents Need Identity, Accountability, and Payment Rails by Design
As agents begin to act and transact on their own, the accountability layer should be built in up front.
Autonomous agents increasingly initiate actions and transactions with limited real-time human involvement. TDC supports requiring that agents carry verifiable, portable identity, maintain tamper-evident logs of their actions, and remain attributable to a responsible human or entity, with any agent-to-agent transaction rails designed so that accountability is verifiable rather than reconstructed after harm. Building these guarantees into the infrastructure is the most reliable way to keep an emerging agentic economy answerable to the people it affects.
Today’s action from Chairman Selig and the CFTC is exactly the kind of proactive, rules-based approach our industry has been asking for. For too long, American innovators have been forced to navigate a patchwork of state regimes and after-the-fact enforcement. A purpose-built federal framework, developed through open notice-and-comment, gives builders the certainty they need and consumers the protections they deserve. The Chairman is right that the goal should be to prevent the next FTX, not just prosecute it.
We look forward to working with the Commission to finalize these rules and input our member feedback.
The SEC continues to deliver on its promise to innovate the regulatory landscape. This proposed rulemaking represents a landmark step forward, bringing the custody rules under the Investment Company Act and the Investment Advisers Act into alignment with today’s digital asset markets.
We are pleased to see many of the recommendations made in our Crypto Task Force RFI responses reflected in the proposing release. Chief among them: (1) permitting state-chartered trusts to serve as qualified custodians, (2) allowing investment advisers and investment companies to self-custody digital assets under certain circumstances, and (3) update the recordkeeping and disclosure obligations for advisers and regulated funds.
Yesterday’s proposing release joins a growing body of recent SEC rulemakings reshaping the U.S. digital asset ecosystem to position the United States as the world’s leading hub for crypto innovation. We’re proud to stand alongside the SEC as it charts this new regulatory path forward.
First,existing law already covers most of what worries people about AI in finance. The Equal Credit Opportunity Act, the Fair Housing Act, the Fair Credit Reporting Act, and Dodd-Frank’s UDAAP provisions don’t carve out an exception for a decision a model made instead of a person. Congress’s first job is reviving and enforcing the rules already on the books, rather than building a parallel AI-specific regime.
Second, compliance claims should be provable, rather than asserted by private companies. Tamper-evident action logs, cryptographic attestations, and verifiable model and data provenance records let a regulator or a court confirm which system acted, on what data, and whether the record was altered afterward. None of that replaces governance, testing, or supervision, but it strengthens self-certification with an authoritative audit trail.
Third, liability should follow function and control, rather than a party’s position in the supply chain. A developer that sets a model’s behavior, and that a deployer can neither inspect nor alter, should answer to the consumer. We also asked Congress for the reverse protection: a safe harbor for content-neutral infrastructure. Compute, storage, and validation networks that carry AI without selecting or training the models running on them shouldn’t be treated as developers or deployers simply for existing, and that protection should apply whether the infrastructure is centralized or distributed.
Data Centers, Front and Center
We also weighed in on AI data centers. We asked the Federal Energy Regulatory Commission to assign the transmission and generation costs a large load causes to that load, instead of spreading them across every customer in the region. We asked Congress to give the Energy Information Administration authority to collect mandatory facility-level reporting on power and water use, because fragmentary data reporting obligations leave communities without the information they need to make informed decisions. And we pointed the Committee to decentralized AI architectures, workloads distributed across many smaller machines instead of concentrated in one location, as a way to ease grid pressure and democratize the AI stack.
Committee Democrats will fold this response into future hearings and legislation on AI in financial services and housing. TDC’s position gives them a path that doesn’t force a choice between protecting consumers and letting the industry keep building: rules that already exist, evidence a regulator can check, and liability that lands on whoever actually controls the outcome.
New Digital Power Network initiative will bring industry expertise to federal policy discussions on compute markets and infrastructure.
WASHINGTON, D.C. — September 24, 2026 — The Digital Chamber (TDC), through its Digital Power Network (DPN), today announced the launch of a ComputeWorking Group to help shape policies affecting the supply, financing, and use of computing capacity.
The group will bring together companies that build, power, finance, buy, sell, and operate compute capacity, connecting industry expertise directly with federal policy discussions around artificial intelligence, high-performance computing, energy, and data center infrastructure.
The initiative builds on DPN’s leadership in Bitcoin mining and the deep operational experience its members have developed across energy-intensive digital infrastructure. As demand for compute accelerates, many of the same challenges around power, infrastructure, capital, and market design are increasingly converging.
“Compute is quickly becoming one of the most valuable resources in the American economy, and the markets that finance and allocate it are still being built,” said Cody Carbone, CEO of The Digital Chamber. “Our members have spent years navigating complex challenges around power, capital, and infrastructure at scale. Our Compute Working Group brings that experience to Washington, so policymakers can get these markets right from the start and keep America at the forefront of the compute economy.”
The group’s first project will inform TDC’s response to the Commodity Futures Trading Commission’s request for comment on compute derivatives, including computefutures. Participants will help explain how compute markets operate and assess how emerging financial products could support price discovery, manage risk, and facilitateinvestment.
“Compute policy is already taking shape across financial markets, energy, and infrastructure,” said Zunera Mazhar, VP of Policy and Government Affairs at The Digital Chamber. “We’re focused on bringing the industry’s operational experience to these conversations, starting with derivatives and extending to the energy, infrastructure, procurement, and market standards that will shape how compute markets develop. This work gives policymakers practical insight into how these markets function and what is needed to support investment, innovation, and growth.”
The working group will also explore:
Compute markets and financial innovation: Policies that support transparent, liquid compute markets, including emerging derivatives, financing tools, and risk management products.
Energy and infrastructure: Power availability, grid interconnection, data center development, and other infrastructure constraints affecting compute growth.
Procurement and market access: Opportunities and barriers for companies seeking to provide compute capacity and services to the federal government.
Standards and market integrity: Frameworks for measuring, verifying, and delivering compute services, including interoperability, performance standards, and emerging distributed compute models.
Through this work, participating companies will help translate operational experience into recommendations for policymakers, bringing the perspectives of infrastructure developers, suppliers, and customers into decisions affecting the compute economy.
About The Digital Chamber
The Digital Chamber is a non-profit organization committed to promoting global blockchain adoption. We envision a fair and inclusive digital and financial ecosystem where everyone has the opportunity to participate. Access to digital assets is not merely a technological advancement but a fundamental human right, crucial for economic and social empowerment. Through targeted education, advocacy, and strategic collaborations with government and industry stakeholders, we drive innovation and shape policies that create a favorable environment for the blockchain technology ecosystem.
The Digital Chamber’s umbrella includes: CryptoUK, Digital Power Network (DPN), TDC’s Digital State Network, and Treasury Council.
