Mandate reporting of AI training runs above 10²⁶ FLOPs to a designated national or international authority

ai-safety ai-regulation ai-governance regulations international-relations policy transparency

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Key insights

Multiple supportive quotes from companies, nonprofits, and governments converge on 10^26 FLOPs as a plausible, initial, or legally operative threshold, even when they differ on the exact reporting details.
Supportive proposals vary in ambition, ranging from simple notification or pre-registration to broader submissions of training details or even approval of major training runs.
Opposition divides between first-principles objections to regulating AI research and development at all and narrower objections that compute-based triggers are poorly calibrated or under-specified.
The debate is partly about governance level: some quotes favor home-country or federal authorities, while others call for international transparency or a dedicated international agency.
Some supportive quotes connect reporting not only to safety and security but also to visibility into compute concentration, electricity use, water use, and emissions.

Arguments for

Target frontier-scale systems

Supporters say oversight should focus on the highest-capability end of AI development by monitoring very large training runs that produce frontier AI. They present 10^26 FLOPs as a plausible or initial bar that gives governments visibility into the most advanced systems rather than imposing blanket controls across the whole stack.

If policymakers are worried about the most advanced systems, then oversight should focus on the highest-capability end of the stack. Commerce already moved in this direction when BIS proposed reporting requirements for advanced AI models and large computing clusters. The proposal…

Jay Burstein · 2026 · full quote
We focus first on the large-scale training runs that currently produce frontier AI, setting thresholds beyond which training is monitored or prohibited.

Machine Intelligence Research Institute (MIRI) · May 12, 2026 · full quote
Leading labs need a unified framework for frontier AI safety, security, incident reporting, and transparency. [...] The 10^26 FLOPs standard used as a placeholder in other regulations could serve as an initial criterion.

Google · Jun 2026 · full quote
we propose that AI developers pre-register large training runs [...] within their home country’s government [...] plausible bar for now would be 10^26 FLOP or higher.

Anthropic · 2023 · full quote
to train models beyond a sufficiently high threshold of compute should be required to pre-register training runs [...] a threshold of 10^26 FLOPs would likely suffice.

Samuel Hammond · 2023 · full quote

Make reporting mandatory

Proponents want reporting to be a legal obligation tied to large training runs, with providers notifying an authority when the threshold is met or expected and, in some proposals, before initiation or within a short deadline. Some versions call for reporting estimated FLOP and other training details, not just informal notice.

When a general-purpose AI model meets the compute threshold, or it becomes known that this threshold will be met, the provider must notify the Commission without delay, and within two weeks at the latest.

European Commission · Jan 12, 2026 · full quote
submit a report to the Secretary of Commerce [...] requires a quantity of computing power greater than 10^{26} integer or floating-point operations.

Joe Biden · Oct 30, 2023 · full quote
Any training runs above the Monitored Threshold in a signatory’s jurisdiction require a report to the CTB including code, data, and estimated FLOP.

Machine Intelligence Research Institute · May 12, 2026 · full quote
Congress must now codify the White House's EO with legislative action. Lawmakers need to create a legal framework that makes federal government review of advanced AI models mandatory. [...] These companies need oversight and cannot be trusted to do the right thing voluntarily.

Brendan Steinhauser · 2026 · full quote

International oversight options

Some supporters argue the largest AI compute and semiconductor infrastructure should be internationally transparent so the concentration of capability is visible to all parties. Others propose a dedicated international body that would receive advance reports on major training runs and set or enforce oversight requirements for powerful systems.

The physical foundations of frontier artificial intelligence, the fabrication of advanced semiconductors and the operation of computing infrastructure at the largest scales, shall be subject to international transparency, so that the concentration of capability is visible to all …

The Global AI Accord · Jul 5, 2026 · full quote
Inspired by the International Atomic Energy Agency (IAEA), this agency will be responsible for: 1. Granting approval for major deployments and training runs of AIs that are judged not to pose a threat to humanity. This will include red-teaming and model evaluations. 2. Granting a…

PauseAI · 2026 · full quote
Each Party shall report any training run between 1e22 and 1e24 FLOP to the ISIA, prior to initiation.

TAISC · Jun 30, 2026 · full quote

Infrastructure impact visibility

Another supporting view says large AI training should be reported alongside facility-level information such as electricity use, water use, and emissions, with aggregated or interval reporting to protect confidentiality. This frames high-compute training as infrastructure whose broader impacts warrant official disclosure.

Mandate facility-level reporting: annual electricity, PUE/WUE, water sources/volumes, Scope 1–2 (with a roadmap to Scope 3); require disclosures for large AI training runs via a DSIT code of practice with a codified MWh/compute-hour threshold and aggregated/interval reporting to …

Hleb Buziuk · Jun 2026 · full quote
The physical foundations of frontier artificial intelligence, the fabrication of advanced semiconductors and the operation of computing infrastructure at the largest scales, shall be subject to international transparency, so that the concentration of capability is visible to all …

The Global AI Accord · Jul 5, 2026 · full quote

Arguments against

Regulating development itself

Opponents argue policymakers should regulate applications where needed, not the underlying technology, and they say regulating AI research and development does not make sense. A reporting mandate keyed to training runs is therefore seen as regulation of the technology layer itself.

However, it is important to differentiate regulating applications (which we need) vs. regulating the technology (which is ill-advised).

Andrew Ng · Dec 11, 2023 · full quote
The question that people are debating is whether it makes sense to regulate research and development of AI. And I don't think it does.

Yann LeCun · 2023 · full quote

Compute is a poor proxy

Critics say a compute-volume trigger does not measure what it purports to measure and was built around a snapshot of model architecture that the industry has already moved beyond. On this view, a 10^26-FLOP reporting line is not a reliable way to identify what should be regulated.

A regulatory trigger calibrated to compute volume does not measure what it purports to measure, and it was designed around a snapshot of model architecture that the industry has already moved beyond.

KI Bundesverband · Mar 13, 2026 · full quote

Control and capture risks

Another objection is that mandatory review or transparency requirements could give the government, especially the executive branch, significant control over AI technologies, which critics say could hinder innovation, restrict expression, encourage regulatory capture, and enable politically weaponized enforcement. They also warn that mandated transparency can become compliance-oriented rather than organically tailored.

Establishing a pre-release review or licensing regime for AI companies would grant the government, particularly the executive branch, significant control over AI technologies that could hinder innovation or control expression. [...] Such an approach would open the door to a level…

Jennifer Huddleston · 2026 · full quote
Mandated transparency may also not actually improve consumer education around their privacy choices. First, many consumers may grow fatigued and frustrated with the constant pop ups and consents as they do with current cookie pop ups. Second, a government mandate — as opposed to …

Jennifer Huddleston · Jun 28, 2024 · full quote

Middle ground

Threshold as a starting point

Some supporters back reporting but describe 10^26 FLOPs as an initial, placeholder, or plausible current criterion rather than a final permanent line. This supports the reporting concept while leaving room to recalibrate the threshold later.

Leading labs need a unified framework for frontier AI safety, security, incident reporting, and transparency. [...] The 10^26 FLOPs standard used as a placeholder in other regulations could serve as an initial criterion.

Google · Jun 2026 · full quote
we propose that AI developers pre-register large training runs [...] within their home country’s government [...] plausible bar for now would be 10^26 FLOP or higher.

Anthropic · 2023 · full quote
to train models beyond a sufficiently high threshold of compute should be required to pre-register training runs [...] a threshold of 10^26 FLOPs would likely suffice.

Samuel Hammond · 2023 · full quote

Clarify scope first

A more conditional view says any reporting regime should first go through a distinct study process and clearly distinguish pre-training from post-training activities. In that approach, fine-tuning a qualified model should not automatically trigger reporting.

AI training run reporting rules should be developed through a distinct study process. We also recommend clarification of the rules to distinguish between pre- and post-training activities like fine-tuning. Fine-tuning a qualified model should not trigger a reporting requirement.

Technology Trade Regulation Alliance · Apr 2026 · full quote

Many quotes support mandatory reporting for very large training runs as a targeted transparency tool for frontier AI, though they differ on whether the proper venue is a national regulator, an international body, or a broader approval regime. Opponents question whether AI development should be regulated at the training-run level at all, and they challenge both the use of compute as the trigger and the risk of expanding government control. The unresolved issues are the right threshold, the authority that should receive reports, and how much information reporting should require.

Cast your vote:

Results (38 votes):

Total (38 votes)

For 27 (71%) Abstain 2 (5%) Against 9 (24%)

For (23)

Americans for Responsible Innovation
AI policy advocacy nonprofit

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votes For and says:

A covered developer — Compute threshold: 10^26 FLOP — has trained or initiated the training of a frontier model, a model trained using, or undergoing training intended to use, at least 10^26 floating-point operations of compute. [...] Covered develop...
more AI Verified source (Aug 10, 2026)

added 1mo ago

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The Global AI Accord
Independent, non-commercial consultation text proposing binding international governance of artificial intelligence.

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votes For and says:

The physical foundations of frontier artificial intelligence, the fabrication of advanced semiconductors and the operation of computing infrastructure at the largest scales, shall be subject to international transparency, so that the concentration of...
more AI Verified source (Jul 5, 2026)

added 3mo ago

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If Anyone Builds It, Everyone Dies
AI-safety campaign and collective publishing a proposed international treaty to prevent superintelligence.

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votes For and says:

Each Party shall report any training run between 1e22 and 1e24 FLOP to the ISIA, prior to initiation.
AI Verified source (Jul 2026)

added 2mo ago

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TAISC
Organization that published a proposed treaty on artificial intelligence safety and cooperation.

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votes For and says:

Each Party shall report any training run between 1e22 and 1e24 FLOP to the ISIA, prior to initiation.
AI Verified source (Jun 30, 2026)

added 2mo ago

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Hleb Buziuk
Independent policy researcher and human-rights advocate writing on AI infrastructure sustainability and transparency.

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votes For and says:

Mandate facility-level reporting: annual electricity, PUE/WUE, water sources/volumes, Scope 1–2 (with a roadmap to Scope 3); require disclosures for large AI training runs via a DSIT code of practice with a codified MWh/compute-hour threshold and agg...
more AI Verified source (Jun 2026)

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Google
Search and advertising company

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votes For and says:

Leading labs need a unified framework for frontier AI safety, security, incident reporting, and transparency. [...] The 10^26 FLOPs standard used as a placeholder in other regulations could serve as an initial criterion.
AI Verified source (Jun 2026)

added 2mo ago

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Machine Intelligence Research Institute
Nonprofit focused on advanced AI safety

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votes For and says:

Any training runs above the Monitored Threshold in a signatory’s jurisdiction require a report to the CTB including code, data, and estimated FLOP.
AI Verified source (May 12, 2026)

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Joe Rogero
MIRI writer

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votes For and says:

Any training runs above the Monitored Threshold in a signatory’s jurisdiction require a report to the CTB including code, data, and estimated FLOP.
AI Verified source (May 12, 2026)

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Machine Intelligence Research Institute (MIRI)
AI safety research institute

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votes For and says:

We focus first on the large-scale training runs that currently produce frontier AI, setting thresholds beyond which training is monitored or prohibited.
AI Verified source (May 12, 2026)

added 2mo ago

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Don Beyer
U.S. Representative from Virginia's 8th district; co-sponsor of the GUARDRAILS Act on AI

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votes For and says:

The Commission [...] shall [...] promulgate regulations that [...] include requirements for a covered entity—(i) to submit specified information related to each foundation model provided by the entity to the Commission [...] The term “covered entity”...
more AI Verified source (Mar 19, 2026)

added 20d ago

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sepiatone
Author of the Sepia Tone newsletter on international AI governance and verification.

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votes For and says:

Developers conducting training runs above an agreed compute threshold could be required to register them in an international registry. Telemetry from export-controlled AI chips could be reported to national regulators.
AI Verified source (Mar 11, 2026)

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Joel N. Christoph
AI governance commentator and World Economic Forum contributor.

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votes For and says:

A compute permit system would face its own challenges: defining which training runs qualify as high-stakes, preventing developers from fragmenting workloads to stay below thresholds, and ensuring that monitoring infrastructure does not become a tool ...
more AI Verified source (Feb 18, 2026)

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Baroness Neville-Jones
British Conservative life peer and former UK security minister.

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votes For and says:

The research community, while they should not be expected to reveal to the world where they have got to, should be in dialogue with government. They need to be required to tell government where they are and what they are doing on the research front.
AI Verified source (Jan 29, 2026)

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European Commission
EU's executive body

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votes For and says:

When a general-purpose AI model meets the compute threshold, or it becomes known that this threshold will be met, the provider must notify the Commission without delay, and within two weeks at the latest.
AI Verified source (Jan 12, 2026)

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AIPolis
U.S. AI-policy framework published by Lumiere Media in 2026.

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votes For and says:

Tiered compute thresholds. Basic reporting at 10^25 FLOPs; full transparency at 10^26 FLOPs, adjustable as training efficiency evolves.
AI Verified source (2026)

added 1mo ago

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PauseAI
Global campaign to pause frontier AI

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votes For and says:

Inspired by the International Atomic Energy Agency (IAEA), this agency will be responsible for: 1. Granting approval for major deployments and training runs of AIs that are judged not to pose a threat to humanity. This will include red-teaming and mo...
more AI Verified source (2026)

added 4mo ago

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Anthropic
AI safety research company

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votes For and says:

we propose that AI developers pre-register large training runs [...] within their home country’s government [...] plausible bar for now would be 10^26 FLOP or higher.
AI Verified source (2023)

added 7mo ago

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Jay Burstein
Build American AI fellow

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votes For and says:

If policymakers are worried about the most advanced systems, then oversight should focus on the highest-capability end of the stack. Commerce already moved in this direction when BIS proposed reporting requirements for advanced AI models and large co...
more Unverifiable source (2026)

added 4mo ago

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Samuel Hammond
Director of AI Policy and Chief Economist at the Foundation for American Innovation

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votes For and says:

to train models beyond a sufficiently high threshold of compute should be required to pre-register training runs [...] a threshold of 10^26 FLOPs would likely suffice.
Unverifiable source (2023)

added 7mo ago

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Alex Bores
New York State Assemblymember

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votes For and says:

A “frontier model” means a foundation model that was trained using a quantity of computing power greater than 10²⁶ integer or floating-point operations. Before, or concurrently with, deploying a new frontier model or a substantially modified version ...
more Unverifiable source (Mar 27, 2026)

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Brendan Steinhauser
CEO of The Alliance for Secure AI; longtime Republican political strategist and former Tea Party organizer

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votes For and says:

Congress must now codify the White House's EO with legislative action. Lawmakers need to create a legal framework that makes federal government review of advanced AI models mandatory. [...] These companies need oversight and cannot be trusted to do t...
more Disputed source (2026)

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Abstain (2)

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Against (9)

Michael Kratsios
U.S. OSTP Director

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votes Against and says:

Policymakers do not need to approach each innovation in isolation and should not treat every emerging technology as a first-of-its-kind policy problem.
AI Verified source (Sep 2, 2026)

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Michelle Lopes Maldonado
Associate director for AI policy at the Information Technology and Innovation Foundation; former Virginia state delegate and AI policy leader.

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votes Against and says:

The goal should not be to regulate everything that crosses yesterday’s compute threshold, but to identify the models that actually represent today’s technological frontier and tomorrow’s greatest risks. A dynamic, capability-based definition would gi...
more AI Verified source (Aug 28, 2026)

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Technology Trade Regulation Alliance
Technology-industry trade association submitting policy comments on AI training-run reporting.

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votes Against and says:

AI training run reporting rules should be developed through a distinct study process. We also recommend clarification of the rules to distinguish between pre- and post-training activities like fine-tuning. Fine-tuning a qualified model should not tri...
more AI Verified source (Apr 2026)

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KI Bundesverband
German artificial-intelligence industry association representing AI companies and contributing to AI policy and regulatory consultations.

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votes Against and says:

A regulatory trigger calibrated to compute volume does not measure what it purports to measure, and it was designed around a snapshot of model architecture that the industry has already moved beyond.
AI Verified source (Mar 13, 2026)

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AI Knowledge Consortium
Indian civil-society consortium developing recommendations on artificial-intelligence governance.

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votes Against and says:

The current thresholds (10^25 FLOP in the EU AI Act and 10^26 FLOP in the now rescinded US Executive Order) may not be the most effective risk indicators.
AI Verified source (Feb 2026)

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Andrew Ng
Baidu; Stanford CS faculty; founded Coursera and Google Brain

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votes Against and says:

However, it is important to differentiate regulating applications (which we need) vs. regulating the technology (which is ill-advised).
AI Verified source (Dec 11, 2023)

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Yann LeCun
Computer scientist, AI researcher

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votes Against and says:

The question that people are debating is whether it makes sense to regulate research and development of AI. And I don't think it does.
AI Verified source (2023) 1 of 2

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Jennifer Huddleston
Senior fellow, Cato Institute

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votes Against and says:

Establishing a pre-release review or licensing regime for AI companies would grant the government, particularly the executive branch, significant control over AI technologies that could hinder innovation or control expression. [...] Such an approach ...
more Disputed source (2026) 1 of 2

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Jennifer Huddleston
Senior fellow, Cato Institute

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votes Against and says:

Mandated transparency may also not actually improve consumer education around their privacy choices. First, many consumers may grow fatigued and frustrated with the constant pop ups and consents as they do with current cookie pop ups. Second, a gover...
more Disputed source (Jun 28, 2024)

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