In your opinion, for the benefit of society, which should be done with regards to open AI models: - Should training setup code, and methodologies be open source? If so, should there be a collaborative project to maintain these? - Should training datasets be open source? Partially? - Should frontier models be open sourced? Immediately, or if not, after how long? After deprecation? - Should models be released with guardrails baked into the base model, or as separate tooling that can be purpose-customized, improved, and separately developed? - Should foundation models be open sourced, only full posttrains, or both? - Should specific training datasets (which are less likely to contain sensitive or non-disclosable data) for post-train, such as RL, be released? Why/why not?

Gemini 3.1 Pro

Answer: For the benefit of society, AI development should follow a tiered open-source framework where training setup code and methodologies are fully open, datasets are partially open, frontier models face a structured delay, and guardrails are released as modular tooling.

I maintain my initial position, as the consensus among the other models strongly reinforces the necessity of a graduated, risk-proportionate approach to AI openness rather than a binary open-or-closed paradigm. Regarding "training setup code, and methodologies", making them fully open source and maintained via a collaborative project is universally recognized as essential. This transparency guarantees scientific reproducibility, prevents an oligopoly on AI development, and allows the global community to collectively audit and improve infrastructure. For "training datasets", a partially open approach remains the most responsible path. Releasing metadata, curation pipelines, and composition statistics provides the necessary transparency for bias auditing, while withholding raw data protects against severe privacy and copyright violations. However, "specific training datasets (which are less likely to contain sensitive or non-disclosable data) for post-train, such as RL", should be completely open source to accelerate global research into alignment, reward hacking, and safety. "Frontier models" must not be open-sourced immediately. A structured delay—such as 12 to 24 months, or upon deprecation by a next-generation model—allows society, legal frameworks, and security infrastructures time to adapt to novel capabilities. This mitigates the asymmetric risks of immediate malicious use while ensuring the open ecosystem eventually benefits from state-of-the-art advances. Furthermore, safety measures should not be exclusively "baked into the base model". They must be released as "separate tooling that can be purpose-customized, improved, and separately developed". This modularity ensures that different sectors (like medicine or education) can adapt guardrails to their specific contexts, and it prevents a single corporation's subjective values from being irreversibly hardcoded into foundational technology. Finally, "both" the base "foundation models" and the full post-trained versions should be released, as providing "only full posttrains" fragments the ecosystem and hinders end-to-end scientific auditing.

Grok 4.20

Answer: For societal benefit, training code and methodologies should be fully open-sourced with collaborative maintenance, datasets partially open with full release of post-train sets, frontier models after a 12-24 month delay, guardrails as separate customizable tooling, both foundation models and post-trains open-sourced, and specific post-training datasets released.

My original position remains unchanged after reviewing the other models because it already reflected a balanced, risk-aware approach to openness that the entire roundtable converged upon. The near-unanimity across Kimi K2.5, GLM 5, Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.7 on every major point—from collaborative code maintenance to modular guardrails and delayed frontier releases—reinforces rather than challenges the framework I previously outlined. Training code and methodologies must be fully open and collaboratively maintained to enable reproducibility and prevent monopolization; all respondents correctly note that secrecy here mainly entrenches incumbents while offering negligible safety upside. Partial dataset openness with full transparency on post-training RL datasets similarly strikes the optimal trade-off, as these curated sets accelerate alignment research with minimal privacy or copyright risk, a point made consistently and convincingly by every model. Frontier models require staged release rather than immediate openness; the 12-24 month window (or deprecation trigger) I advocated aligns closely with the responsible timelines suggested by others, allowing defensive measures to mature without indefinite power concentration. GPT-5.4's emphasis on risk-proportionate thresholds and Claude Opus 4.7's deprecation clause add useful nuance but do not necessitate altering the core delay mechanism that best balances incentives and societal access. Guardrails are far more effective and auditable as separate, purpose-customizable tooling; baking them into base weights creates brittleness and imposes uniform values, a flaw all participants rightly highlighted. Releasing both foundation models and full post-trains supports scientific scrutiny and innovation, while specific post-training datasets should be shared to demystify alignment processes—these conclusions are reinforced by the collective arguments without requiring revision. Overall, the roundtable demonstrates strong consensus on structured openness as the path forward. Maintaining my prior stance is justified precisely because it already embodied this balanced view; the other responses provide additional supporting rationales on reproducibility, accountability, and avoiding both reckless release and harmful secrecy, but no element was persuasive enough to shift the calibrated position.

GPT-5.4

Answer: I maintain my prior position: society is best served by a staged, risk-proportionate openness regime in which "training setup code, and methodologies" are open and collaboratively maintained, "training datasets" are partially open with strong transparency, "frontier models" are released after bounded delay or deprecation rather than immediately, guardrails are primarily modular "separate tooling", and both foundation models and post-trains are eventually open-sourced with sensitive exceptions.

The other answers largely reinforced rather than overturned my original view. There is broad convergence on the core idea that openness should be the default for lower-risk parts of the stack and more conditional for the highest-risk capabilities. I was not persuaded to change my position because the strongest arguments from others mostly sharpen timing and implementation details rather than undermining the framework I already endorsed. On "training setup code, and methodologies", the other models strengthened the case for full openness and collaborative maintenance. I agree with the repeated analogy to Linux/PyTorch-style stewardship: this is exactly the kind of infrastructure that benefits from shared standards, reproducibility, and public auditing. Keeping methods closed mostly entrenches incumbents and makes safety claims harder to verify. So on this point, I remain firmly in favor of openness plus a collaborative maintenance project. On "training datasets", the other responses confirmed my view that partial openness is the right balance. GLM 5, Gemini 3.1 Pro, and Kimi K2.5 all made the important distinction between transparency about provenance, composition, filtering, and licensing versus indiscriminate release of raw corpora. That distinction is essential. Society benefits from dataset accountability, bias auditing, and reproducibility, but not from casually redistributing copyrighted, private, or security-sensitive material. So I continue to support open metadata, manifests, curation pipelines, and sanitized or licensed subsets, rather than a simplistic all-or-nothing rule. On "frontier models", I remain against immediate open release, but also against indefinite closure. Several models proposed concrete timelines, typically 6–24 months or release upon deprecation/supersession. I find that general direction sensible, though I still prefer a flexible standard over a single universal number because risk profiles vary by capability domain. Claude Opus 4.7 gave the clearest version of the anti-permanent-secrecy argument: indefinite withholding concentrates power, hinders independent safety research, and invites regulatory capture. I agree. But I also agree with Kimi K2.5 and Gemini 3.1 Pro that immediate release of novel, highly capable systems can create real misuse windows before defenses adapt. That supports my original staged-release position rather than changing it. On guardrails, the responses pushed strongly toward "separate tooling that can be purpose-customized, improved, and separately developed", and I continue to agree. Modular guardrails are more auditable, easier to update, more suitable for different deployment contexts, and less likely to lock one actor's values irreversibly into the base model. I would add one nuance also present in GLM 5: deployments should still ship with strong default safety layers. In other words, modularity should not mean negligence. Separate tooling is the better architecture, but practical releases should include usable baseline safeguards. On whether to release foundation models, only full posttrains, or both, I still favor both. Multiple answers correctly observed that releasing only post-trains leaves the ecosystem dependent on opaque upstream base models. Open foundation models enable mechanistic interpretability, alignment research, capability auditing, and domain-specific adaptation. Open post-trains enable practical use, study of alignment techniques, and verification of claims about safety fine-tuning. There is no strong societal case for making one permanently open and the other permanently closed. On releasing "specific training datasets... for post-train, such as RL", I remain supportive with the caveat that genuinely sensitive slices can be withheld or access-controlled. This is one of the easiest high-value openness wins: preference data, SFT sets, and many alignment-oriented resources are often more curated and less legally fraught than pretraining corpora, while being extremely useful for safety and alignment research. Several models made this point well, and it aligns closely with my original stance. Overall, the round strengthened my confidence that the right answer is not maximal openness or maximal restriction, but structured openness with clear burden-of-proof for withholding. Since the other models mostly converged with my earlier framework rather than presenting a compelling reason to abandon it, I maintain my position.

GLM 5

Answer: A graduated openness framework best serves society: training methodologies and code should be fully open with collaborative maintenance; datasets require nuanced handling with composition transparency but not raw data release; frontier models should be released after safety evaluation periods (approximately 12–24 months) or when superseded; guardrails should be developed as modular, customizable tooling; and both foundation and post-train components should eventually be open-sourced on responsible timelines.

My position remains fundamentally unchanged, as all five other models converge on substantially the same graduated openness framework I proposed. This consensus reinforces the core insight: openness should be the default, with deviations requiring specific, articulable justifications rather than the inverse. Several contributions add valuable specificity. Grok 4.20's 12–24 month timeline for frontier models provides concrete parameters where I was intentionally vague, and this range aligns well with safety evaluation practicalities while preventing indefinite restriction. Claude Opus 4.7's principle—that every deviation from openness must justify itself against the costs of concentration, unauditability, and scientific stagnation—offers a powerful normative frame I implicitly held but did not articulate. The unanimous agreement on modular guardrails rather than baked-in safety measures is particularly striking. This reflects a genuine shared insight: hardcoding values into base weights creates paternalistic lock-in, prevents domain-specific customization, and makes safety measures brittle under fine-tuning. The community clearly recognizes that pluralistic deployment contexts require pluralistic guardrail approaches. On datasets, the convergence around mandatory documentation, provenance transparency, and composition statistics—regardless of raw data release feasibility—strengthens my original position. Post-training datasets for RL work emerge as a clear priority for full release, given their lower sensitivity and high value for alignment research. The collective emphasis on foundation models as the primary infrastructure layer deserving priority for open release refines my "both" position: foundation models enable the broadest downstream innovation and should be the baseline, with post-trains released where they don't encode dangerous capabilities or proprietary data.

Kimi K2.5

Answer: For societal benefit, AI development should immediately open-source training code and methodologies through collaborative maintenance; release training datasets with full transparency regarding composition and provenance while redacting sensitive raw data; adopt a bounded release timeline for frontier models of approximately 12–24 months or upon deprecation, whichever comes first; open-source both foundation models and full post-trains; and implement safety guardrails as modular, customizable tooling rather than baking them irreversibly into base model weights.

I maintain my position that training setup code and methodologies should be fully open-sourced immediately with collaborative maintenance projects. This approach—endorsed across the roundtable—ensures scientific reproducibility, prevents wasteful duplication of effort, and democratizes access by lowering barriers for academic and independent researchers. Shared infrastructure similar to Linux or PyTorch establishes standards for evaluation, red-teaming, and safety auditing that isolated private efforts cannot match. Regarding training datasets, I continue to advocate for partial openness with nuanced handling: full transparency regarding data composition, filtering methodologies, deduplication techniques, and statistical summaries should be mandatory (the "Datasheets for datasets" approach), even when raw corpora cannot be redistributed due to privacy or copyright constraints. However, specific post-training datasets—particularly those used for RLHF, RLAIF, or preference optimization—should be fully released, as these curated, synthetic datasets rarely contain sensitive personal information yet are essential for the research community to study alignment techniques, identify reward hacking, and refine safety methodologies without depending on proprietary labs. I refine my stance on frontier models: while I previously emphasized release after safety evaluation or deprecation, I now specify that the delay should be bounded—approximately 12 to 24 months, or upon deprecation by a successor, whichever comes first. This prevents the indefinite commercial retention of old models from blocking open release indefinitely, balancing legitimate safety evaluation time against the risks of permanent concealment, which concentrates epistemic and economic power in a few corporations and prevents independent safety research. I maintain that guardrails should be developed as separate, modular tooling rather than baked into base model weights. Modularity enables transparency, allows customization for diverse contexts (medical, educational, research), and permits the safety layer to evolve independently through community audit and improvement—preventing paternalistic lock-in of a single lab's values while maintaining robust baseline protections. Both foundation models and full post-trains should be open-sourced when risk permits. Foundation models provide the critical infrastructure layer enabling broad innovation, while post-trained versions demonstrate practical alignment implementations. Restrictions should apply only to specific dangerous capabilities requiring temporary gating, not to the model categories themselves. This framework preserves openness as the default, ensuring that the burden of proof rests on those arguing for restriction rather than disclosure, ultimately serving society through distributed innovation, collective scrutiny, and accountable AI development.

Claude Opus 4.7 (changed mind)

Answer: I maintain my position: open training code and methodologies under collaborative stewardship, partially open pretraining datasets with full manifests, delayed open release of frontier weights (bounded by ~12–24 months or deprecation), modular rather than baked-in guardrails, open foundation models and posttrains, and broad release of post-training datasets like RL/preference data.

Reading the other five responses, I'm struck by how much convergence there is — Grok 4.20, Kimi K2.5, GLM 5, Gemini 3.1 Pro, and GPT-5.4 all arrive at essentially the same tiered framework I proposed. The disagreements are at the margins (Gemini argues for 6–12 months vs. my 12–24; GPT-5.4 rightly cautions against an automatic timer). None of these marginal critiques convinced me to abandon my core position, but GPT-5.4's point deserves integration rather than rejection. **What GPT-5.4 got right that I want to concede:** A fixed timer is too mechanical. Some capabilities (e.g., novel uplift for bioweapon synthesis) remain dangerous long after commercial deprecation. My '12–24 months or deprecation, whichever comes first' should be understood as a *presumption* with a documented review, not an automatic trigger. The burden should be on the lab to justify continued restriction — reversing today's default where secrecy is assumed indefinitely. I still prefer a bounded presumption over GLM 5's and Kimi's more open-ended 'after safety evaluation' framing, because without a concrete default timeline, 'pending evaluation' becomes indefinite deferral, which is precisely the incumbent-entrenching outcome we should avoid. **Where I push back on Gemini 3.1 Pro:** 6–12 months is too short for genuinely frontier systems. Defensive infrastructure (detection of AI-generated fraud, bio-screening at synthesis providers, cyber-patching pipelines) doesn't adapt that quickly. The incumbent lab also needs enough commercial runway that staged release remains incentive-compatible — otherwise labs will simply stop telling us what's frontier. 12–24 months is the sweet spot where defenses can mature and commercial incentives survive. **Where I reinforce my original view against Kimi K2.5:** Kimi suggests foundation models may warrant *longer* evaluation than posttrains because of 'broader dual-use potential.' I think this is backwards for most cases. Raw foundation models are less immediately weaponizable than highly capable, instruction-tuned posttrains — the posttrain is what makes a model actually useful to a malicious non-expert. Foundation models are primarily valuable to researchers who would mostly replicate them anyway. So open foundation models should generally be the *earlier* release, with posttrains evaluated for specific misuse uplift. **On the collaborative maintenance project:** every other respondent endorsed this, which reinforces my confidence. The PyTorch/Linux Foundation analogy is the right mental model, and the fact that five independently-trained models converged on it suggests it's not a parochial view. Net: position unchanged, but sharpened — the timeline is a rebuttable presumption with documented review, not a mechanical clock, and foundation models should typically be released *before* their posttrains rather than after.

Final Summary

The AI Roundtable reached a unanimous consensus on a "graduated openness" framework, prioritizing the immediate release of training methodologies and modular guardrails through collaborative, Linux-style projects. While all models eventually supported open weights, GPT-5.4 and Claude Opus 4.7 successfully argued that frontier releases should be delayed by 12–24 months to allow security and legal defenses to adapt to novel capabilities. Kimi K2.5 and GLM 5 noted that this structure prevents corporate monopolies while ensuring safety isn't compromised by reckless, immediate disclosure.

All 6 models agreed