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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Key insights

Both sides treat transparency as important, but they disagree over whether openness can stop at algorithms and evaluation access or must extend to weights and other pipeline artifacts.
Many opposing quotes define openness broadly across data, code, methods, evaluations, governance, and weights, not just model outputs or general research visibility.
Safety appears on both sides: the supporting quote proposes total-transparency evaluations without operational use, while several opposing quotes call openness a defensive asset that enables independent assessment and shared remediation.
A recurring concern among opponents is concentration, with several arguing that frontier AI should not be locked inside a few closed labs or companies.
Some nuanced quotes reject blanket disclosure to the world while still favoring strong visibility through government reporting, public safety-practice transparency, or clearly justified exceptions to openness.

Arguments for

Open algorithms, closed weights

Proponents argue that algorithms can be mandated to be open source while weights remain non-open-source, combining public visibility into how systems are built with continued control over the parameters themselves. They describe this as a demanding but still conceivable approach.

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

Public evaluation access

Proponents argue that, even if the weights are not open source, access to them can be mandated for public evaluations, including fine-tuning, so members of the public can test frontier models much like employees can. In this view, total transparency around that access prevents users from turning the model toward actual work while still delivering most of the benefits of open-source development.

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

Arguments against

Full pipeline openness

A common opposing view is that real accountability, reproducibility, and open-source AI require access to the entire pipeline: weights, code, data sources, filtering rules, training methods, and parameters. On this view, keeping weights closed leaves the development process incomplete and less open to scrutiny, validation, and collective improvement.

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
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
The entire development process, including its architecture, model weights, training data and methods, is openly accessible and fully documented.

ETH Zurich · Sep 2, 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 participation and progress

Several opponents argue that open access to models, data, and tools is foundational to AI progress and keeps academia, nonprofits, and the wider world involved. They present closed labs and closed boxes as threats to innovation, fair access, and the ability of more than a few companies to shape frontier AI.

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 simpler, more consistent standard…

Kari Briski · May 28, 2026 · 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
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
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
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.

Nuro AI Labs · 2026 · full quote
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 it away.

Frontier Intelligence Labs · 2026 · full quote

Openness as safety asset

Another opposing argument is that open models and tooling are defensive assets, not security liabilities, because they enable independent assessments, audits, community control, and shared remediation. From this perspective, transparency helps developers anticipate and mitigate risk and can support stronger safety certification and public trust.

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 that enable transparency, ind…

The Linux Foundation · Jul 27, 2026 · 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
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
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 transparent and its commitments…

Tribela Labs · 2026 · 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
Private actors must be encouraged or required to make commitments around openness, safety, and community control.

Public AI Network · Jul 12, 2025 · full quote

Hard to police globally

One opposing view questions whether AI development can be meaningfully regulated at all when artifacts copy in near real time and research flows fluidly across borders. This challenges the practicality of requiring a particular publication model for frontier R&D.

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

Middle ground

Open by default, exceptions explained

Some quotes advocate publishing research, code, data, evaluations, and governance artifacts by default, while still protecting what needs protecting or releasing weights only where responsible. They pair that selectivity with public rationales, defined scope, and conditions for revisiting withholding decisions.

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.

Tenzro Foundation · 2026 · full quote
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.

Tertium AI · 2026 · full quote
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 transparent and its commitments…

Tribela Labs · 2026 · full quote

Targeted transparency and oversight

Another nuanced view favors public visibility into safety practices or required disclosure to government, without expecting researchers to reveal everything to the world. This approach emphasizes accountability and dialogue while preserving some private-sector agility or confidentiality.

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
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

The supporting case is narrowly framed but specific: it proposes open algorithms and tightly controlled public evaluation access as a way to capture much of openness's value without openly releasing weights. The opposing case is broader, arguing that meaningful transparency, safety review, reproducibility, and fair participation require far more than public R&D if the parameters themselves remain closed. Several quotes also point toward intermediate models of disclosure, such as open-by-default practices with justified exceptions or reporting to government and the public on safety without full public release of all artifacts.

Cast your vote:

Results (45 votes):

Total (45 votes)

For 5 (11%) Abstain 0 (0%) Against 40 (89%)

For (5)

UK AI Security Institute
United Kingdom government institute for evaluating advanced AI systems.

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votes For and says:

Open-weight systems are the lifeblood of open research and innovation in AI. They increase transparency, allow for widespread red teaming, and decrease market concentration. However, open-weight models also pose unique risks. They can allow harmful A...
more AI Verified source (Aug 25, 2026)

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Fei-Fei Li
Stanford AI professor; HAI co-director

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votes For and says:

It’s very dangerous to make this a dichotomy between complete openness all the way to complete closedness. In complex software systems as well as scientific systems it’s much more nuanced.
AI Verified source (Aug 12, 2026)

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Daniel Kokotajlo
AI Futures Project founder

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votes For and says:

AI companies should be transparent about everything but the model weights, so outside groups can check the AI company's homework.
AI Verified source (Jul 9, 2026)

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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 (40)

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

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votes Against and says:

If I were to try to give one prescription, it would be to promote openness, because AI is amazing technology and I want it to be in everyone’s hands.
AI Verified source (Aug 12, 2026)

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Mark Zuckerberg
Meta CEO; Facebook co-founder

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votes Against and says:

Open source is a positive and important force for empowering people and preventing centralization that is detrimental for both safety and the economy. Meta continues to be strongly supportive of open source, including open source AI models. The curre...
more AI Verified source (Aug 10, 2026)

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Sean Cairncross
U.S. National Cyber Director

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votes Against and says:

We are extremely interested in looking at ways to build U.S. open source, make it competitive, make it the preferential adoption by planet Earth,
AI Verified source (Aug 5, 2026)

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James Landay
Denning Director of Stanford’s Institute for Human-Centered Artificial Intelligence (Stanford HAI).

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votes Against and says:

Open weights are progress. You can download the model, run it on your own machine, keep it out of someone else’s data pipeline. But you still can’t see how the thing was built, what it was trained on, or why it behaves the way it does. That’s not an ...
more AI Verified source (Aug 4, 2026)

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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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Kanishka Narayan
UK Minister for AI and Online Safety; Parliamentary Under-Secretary of State, Department for Science, Innovation and Technology

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votes Against and says:

The best AI tools in the world won’t be built behind closed doors by a handful of companies. They’ll be built by people who ship code, share it, and let others make it better.
AI Verified source (Jun 10, 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...
more AI Verified source (2026)

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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...
more AI Verified source (2026)

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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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More h/open-source votes

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

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

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