Require frontier AI labs to conduct nearly all R&D publicly but keep the weights closed

ai-regulation open-science transparency research-policy open-source ai-policy ai-safety ai-governance

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Arguments for

Scrutiny and safety benefits

Supporters say opening the full pipeline—data, code, weights, methods, and documentation—makes AI accountable and scalable by enabling scrutiny, validation, reproducibility, independent assessment, and collective improvement. Fully transparent models are also described as supporting the most extensive safety certifications and better risk mitigation.

For AI to be both accountable and scalable, every part of the pipeline—from the initial dataset to the final set of parameters—needs to be open to scrutiny, validation, and collective improvement.

Open Source Initiative · Jan 29, 2025 · full quote
Fully transparent models providing access to their training data and procedures can support the most extensive safety certifications.

Hugging Face · Mar 19, 2025 · full quote
Our open source approach also helps us to better anticipate and mitigate risk because it enables us to learn from the broader community’s independent assessments of our models’ capabilities.

Meta · Feb 3, 2025 · full quote
All mixtures, filtering rules, and data sources are fully released for reproducibility.

Institute of Foundation Models (IFM) · Dec 1, 2025 · full quote
Fully open, end-to-end recipe for language model training: [...] all ingredients openly available: data, training recipe, methodology, and software.

Ai2 (Allen Institute for AI) · Mar 13, 2025 · full quote

Broader innovation and participation

Proponents argue that closed practice concentrates progress in a few labs and narrows the literature that could exist. Open research, recipes, code, and data are presented as ways for academia, nonprofits, and the wider community to build similar or better models and participate in frontier progress.

The biggest threat to AI innovation is the closed nature of the practice. We have been pushing very, very strongly towards openness.

Ali Farhadi · Feb 19, 2025 · full quote
These days you might argue that progress in AI is getting limited into a few closed labs, but it’s actually very important to the vast majority of academia and researchers, or nonprofit and other places who want to also be part of this progress. And we’ve seen that all this progr…

Hanna Hajishirzi · Mar 25, 2026 · full quote
The goal of Open-R1 is to build these last missing pieces so that the whole research and industry community can build similar or better models using these recipes and datasets.

Open-R1 · Jan 28, 2025 · full quote
We open-source INTELLECT-2 along with our code and data, hoping to enable more open research in the field of globally-distributed training

Prime Intellect · May 11, 2025 · full quote

Public-interest AI infrastructure

Another line of support treats models and training data as public-interest infrastructure. On this view, open and trustworthy AI foundations can serve the public good, broaden fair access, and let communities shape the building blocks of AI.

For that to happen, the building blocks of AI—the data that powers these models and the models themselves—must serve and be shaped by the public interest. [...] a rare and exciting opportunity for open, transparent, and accountable AI infrastructure to gain ground.

Martin Tisné · May 16, 2025 · full quote
We believe that open-wide models should be the basis for building all the AI software in the world. By having an open ecosystem of people that have aligned incentives to create assets that are going to be great for humanity, we can accelerate progress and make sure that everybody…

Arthur Mensch · Mar 25, 2026 · full quote
Apertus is built for the public good. It stands among the few fully open LLMs at this scale and is the first of its kind to embody multilingualism, transparency, and compliance as foundational design principles.

Imanol Schlag · Sep 2, 2025 · full quote
Apertus demonstrates that generative AI can be both powerful and open. [...] The release of Apertus is not a final step, rather it’s the beginning of a journey, a long-term commitment to open, trustworthy, and sovereign AI foundations, for the public good worldwide.

Antoine Bosselut · Sep 2, 2025 · full quote
Private actors must be encouraged or required to make commitments around openness, safety, and community control.

Public AI Network · Jul 12, 2025 · full quote

Openness as regulatory default

Some advocates want openness to be the regulatory baseline, with policymakers assuming openness and requiring large labs to make concrete commitments. In this view, open-source AI means releasing core artifacts such as weights, code, and, when legally possible, datasets, with a goal of making almost all AI research publicly visible.

Plan A is not that crazy, but it’s close: the algorithms are mandated to be open source, and while the weights aren’t (in fact the weights are banned from being OS), access to the weights is mandated to be open; members of the public can do evaluations (including fine-tuning!) on…

AI Futures Project · Jul 9, 2026 · full quote
Policymakers should regulate on the assumption of openness, build the capacity to respond to emerging risks, and avoid restrictions based on low-confidence projections.

Ben Brooks · Aug 31, 2025 · full quote
Mozilla has long advocated for such rules to be included in the AI Act to ensure that large AI labs must play their part in making the technology they develop safer and more transparent.

Maximilian Gahntz · Aug 11, 2025 · full quote
Open source AI requires: unrestricted access to trained parameters/weights; complete code used to train the system and prepare training data; full dataset (unless legally impossible).

Stefano Maffulli · Oct 6, 2025 · full quote
We encourage model developers including our AI Alliance research projects to release as much as possible. [...] For our own AI Alliance model development projects we strive to release as much as possible.

AI Alliance · Apr 24, 2025 · full quote

Arguments against

Withhold dangerous methods

Opponents say some high-risk methods should not be publicly disclosed at all. Withholding methodology for dangerous evaluation tasks is presented as necessary to prevent misuse.

Legal and enforcement limits

Critics also argue that complete openness is not always feasible. One quote says full openness of training datasets may be legally impossible, and another says AI development cannot be meaningfully regulated in a world where artifacts copy in near real time and research flows across borders.

We cannot meaningfully regulate the development of AI in a world where artifacts copy in near real time and research flows fluidly across borders.

John deVadoss · Feb 2, 2026 · full quote

Targeted transparency instead

A narrower-transparency position says labs should not be expected to reveal to the world where they have got to. Instead, they should be in dialogue with government and provide public visibility into safety practices while preserving private-sector agility.

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.

Baroness Neville-Jones · Jan 29, 2026 · full quote
Our proposed transparency framework offers a practical first step: public visibility into safety practices while preserving private sector agility to deliver AI's transformative potential.

Anthropic · Jul 7, 2025 · full quote

Middle ground

Hybrid open-closed ecosystem

Several speakers reject a strict open-versus-closed framing, arguing that proprietary and open approaches can coexist. They describe a landscape with powerful closed models, emerging open models, and at least some open component across different AI frontiers, rather than a requirement that nearly all R&D be public.

Models are fundamental knowledge infrastructure, and fundamental knowledge infrastructure yearns for openness. There’s a flourishing ecosystem of powerful, closed models but equally capable open models that are going to be coming over the next couple years.

Misha Laskin · Mar 25, 2026 · full quote

Insights

Both sides connect openness to safety, but in opposite ways: supporters stress scrutiny and independent assessment, while critics stress misuse prevention and selective withholding.
The sharpest disagreement is often about how much to disclose publicly, not whether any transparency is desirable.
A number of pro-openness quotes describe full releases of data, code, weights, methods, or documentation, using concrete projects as examples of end-to-end transparency.
Dataset openness is a specific fault line, with supporters tying it to reproducibility and accountability and critics saying complete openness may be legally impossible.
The quoted support for openness spans universities, nonprofits, open-source groups, and companies, while reservations also come from government, corporate, and safety-focused institutions.

Supporters present public R&D as a path to wider participation, stronger scrutiny, reproducibility, and AI infrastructure shaped by the public interest. Opponents and mixed-position speakers do not frame the issue as openness versus no openness so much as a dispute over scope, arguing that nearly all research should not be public because of misuse risks, legal and cross-border practicalities, and the value of narrower or hybrid disclosure models.

Cast your vote:

Results (37 votes):

Total (37 votes)

For 2 (5%) Abstain 0 (0%) Against 35 (95%)

For (1)

AI Futures Project
Nonprofit AI forecasting research group behind AI 2027 and Plan A; led by Daniel Kokotajlo.

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votes For and says:

Plan A is not that crazy, but it’s close: the algorithms are mandated to be open source, and while the weights aren’t (in fact the weights are banned from being OS), access to the weights is mandated to be open; members of the public can do evaluatio...
more AI Verified source (Jul 9, 2026)

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Abstain (0)

Against (35)

The Linux Foundation
Nonprofit consortium supporting open-source technology ecosystems

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votes Against and says:

As policymakers and regulators work through questions of AI safety, the distinction that matters is not open versus closed, but transparent and secure versus opaque and unexamined. Open models and open tooling should be recognized as defensive assets...
more AI Verified source (Jul 27, 2026)

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Jakob Foerster
Associate professor at the University of Oxford and co-director of the British Open-ended Learning and Discovery Lab

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votes Against and says:

BOLD is about a different route: discovering fundamentally new ways to build AI that are more efficient, more open and better aligned with human needs.
AI Verified source (Jun 23, 2026)

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Kari Briski
Vice president of generative AI software at NVIDIA

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votes Against and says:

Open innovation is foundational to AI progress, and broad access to models, data, and tools is essential to accelerating breakthroughs. By adopting the Linux Foundation’s OpenMDW framework for NVIDIA open model families, we’re helping establish a sim...
more AI Verified source (May 28, 2026)

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Hanna Hajishirzi
Professor at the University of Washington and senior director of NLP at Ai2

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votes Against and says:

These days you might argue that progress in AI is getting limited into a few closed labs, but it’s actually very important to the vast majority of academia and researchers, or nonprofit and other places who want to also be part of this progress. And ...
more AI Verified source (Mar 25, 2026)

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Arthur Mensch
Mistral AI co-founder and CEO

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votes Against and says:

We believe that open-wide models should be the basis for building all the AI software in the world. By having an open ecosystem of people that have aligned incentives to create assets that are going to be great for humanity, we can accelerate progres...
more AI Verified source (Mar 25, 2026)

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Misha Laskin
Co-founder and CEO of Reflection AI; artificial intelligence researcher

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votes Against and says:

Models are fundamental knowledge infrastructure, and fundamental knowledge infrastructure yearns for openness. There’s a flourishing ecosystem of powerful, closed models but equally capable open models that are going to be coming over the next couple...
more AI Verified source (Mar 25, 2026)

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John deVadoss
AI technologist; co-founder of NeuralFabric and former Microsoft general manager

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votes Against and says:

We cannot meaningfully regulate the development of AI in a world where artifacts copy in near real time and research flows fluidly across borders.
AI Verified source (Feb 2, 2026)

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

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votes Against 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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Nuro AI Labs
London-based AI research lab developing open models and general-intelligence research

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votes Against and says:

We publish weights, training code and synthetic-data pipelines wherever the research permits. Apache 2.0 is the default. We don't believe the systems on the path to general intelligence should be closed boxes held by three companies.
AI Verified source (2026)

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Tribela Labs
European AI research lab focused on open, transparent, privacy-preserving systems

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votes Against and says:

We believe transparency and open research are not optional — they are the only way to build AI that people can trust, audit, and improve upon. Tribela Labs exists to prove that a sustainable company can advance AI research while keeping its methods t...
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Frontier Intelligence Labs
Frontier AI research group founded in 2026 to develop broadly accessible systems

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votes Against and says:

We’re a research group pushing the frontier of artificial intelligence — and making it useful, affordable, and open to the world. Not the few. Everyone. We do original research at the capability frontier — and publish what we learn instead of locking...
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Tertium AI
Nonprofit building and distributing frontier AI with open-by-default governance

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votes Against and says:

We’re open by default: open code; open data, subject to privacy and copyright; and open weights wherever responsible. We release as widely as we can demonstrate to be safe.
AI Verified source (2026)

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Tenzro Foundation
Public-interest foundation developing open and accountable AI infrastructure

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votes Against and says:

Research, code, weights, datasets, evaluations, and governance artifacts default to open. Any decision to withhold is published with its rationale, its scope, and the conditions under which it will be revisited.
AI Verified source (2026)

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Institute of Foundation Models (IFM)
AI research institute behind the fully open LLM360 model program

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votes Against and says:

All mixtures, filtering rules, and data sources are fully released for reproducibility.
AI Verified source (Dec 1, 2025)

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Stella Biderman
EleutherAI executive director and scientist

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votes Against and says:

At Eleuther AI, we’ve spent five years working to make large-scale AI research more transparent, accessible, and ethical, with a strong focus on data work.
AI Verified source (Oct 7, 2025)

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Stefano Maffulli
Executive director of the Open Source Initiative

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votes Against and says:

Open source AI requires: unrestricted access to trained parameters/weights; complete code used to train the system and prepare training data; full dataset (unless legally impossible).
AI Verified source (Oct 6, 2025)

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Ana Klimovic
Assistant professor of computer science at ETH Zurich and Swiss AI Initiative steering-committee member

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votes Against and says:

I’m really excited about the release of the fully open Apertus model and the follow-up work to come.
AI Verified source (Sep 17, 2025)

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Antoine Bosselut
Professor at EPFL and co-lead of the Swiss AI Initiative

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votes Against and says:

Apertus demonstrates that generative AI can be both powerful and open. [...] The release of Apertus is not a final step, rather it’s the beginning of a journey, a long-term commitment to open, trustworthy, and sovereign AI foundations, for the public...
more AI Verified source (Sep 2, 2025)

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Imanol Schlag
AI researcher at ETH Zurich and technical lead of the Apertus language-model project

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votes Against and says:

Apertus is built for the public good. It stands among the few fully open LLMs at this scale and is the first of its kind to embody multilingualism, transparency, and compliance as foundational design principles.
AI Verified source (Sep 2, 2025)

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ETH Zurich
Swiss public research university and co-developer of the fully open Apertus language model

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votes Against and says:

The entire development process, including its architecture, model weights, training data and methods, is openly accessible and fully documented.
AI Verified source (Sep 2, 2025)

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Ben Brooks
Head of public policy, Stability AI

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votes Against and says:

Policymakers should regulate on the assumption of openness, build the capacity to respond to emerging risks, and avoid restrictions based on low-confidence projections.
AI Verified source (Aug 31, 2025)

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Maximilian Gahntz
Mozilla senior policy manager working on AI governance and open-source policy

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votes Against and says:

Mozilla has long advocated for such rules to be included in the AI Act to ensure that large AI labs must play their part in making the technology they develop safer and more transparent.
AI Verified source (Aug 11, 2025)

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Public AI Network
International initiative building open, publicly governed AI infrastructure

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votes Against and says:

Private actors must be encouraged or required to make commitments around openness, safety, and community control.
AI Verified source (Jul 12, 2025)

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Anthropic
AI safety research company

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votes Against and says:

Our proposed transparency framework offers a practical first step: public visibility into safety practices while preserving private sector agility to deliver AI's transformative potential.
AI Verified source (Jul 7, 2025)

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Baidu
Chinese technology company and developer of the ERNIE family of AI foundation models

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votes Against and says:

Additionally, we open source the development toolkits for ERNIE 4.5, featuring industrial-grade capabilities, resource-efficient training and inference workflows, and multi-hardware compatibility.
AI Verified source (Jun 30, 2025)

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Mistral AI

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votes Against and says:

Frontier AI. In Your Hands. This was the founding north star of Mistral AI and continues to guide our mission two years on. It has helped us evolve from a research lab to the world’s leading independent AI company, rooted in open science.
AI Verified source (Jun 11, 2025)

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Martin Tisné
Founder and chair of Current AI; public-interest technology leader

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votes Against and says:

For that to happen, the building blocks of AI—the data that powers these models and the models themselves—must serve and be shaped by the public interest. [...] a rare and exciting opportunity for open, transparent, and accountable AI infrastructure ...
more AI Verified source (May 16, 2025)

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Prime Intellect
AI research company developing open, distributed infrastructure for training frontier models

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votes Against and says:

We open-source INTELLECT-2 along with our code and data, hoping to enable more open research in the field of globally-distributed training
AI Verified source (May 11, 2025)

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AI Alliance
Global coalition advancing open, safe, and responsible artificial intelligence

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votes Against and says:

We encourage model developers including our AI Alliance research projects to release as much as possible. [...] For our own AI Alliance model development projects we strive to release as much as possible.
AI Verified source (Apr 24, 2025)

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More h/open-source votes

Open-source AI is more dangerous than closed-source AI

Democratic middle powers should jointly develop frontier AI and share its benefits globally

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