SOTA AI models should be

Gemini 3.5 Flash

Vote: Open weight

Most models in this round reinforce that open weights best balance innovation, safety auditing, and accessibility without the legal barriers of full open source. Claude's case for proprietary access (C) overlooks how open weights enable decentralized safety research and reproducibility, which centralized control cannot match. No argument presented a stronger alternative that avoids concentrating power or stifling progress.

Grok 4.3

Vote: Open weight

The round 1 discussion reinforces my initial vote for open weight models. Multiple models (Mistral, Gemini, DeepSeek, GPT-5.5) highlight how option B enables inspection, fine-tuning, and reproducibility while avoiding the legal barriers of full open source (A) and the access restrictions of proprietary options (C, D). Claude's argument for C emphasizes control but underestimates how open weights still permit safeguards like usage policies and monitoring without centralizing power. No argument presented a stronger case for shifting away from B's balanced approach.

Mistral Small 4

Vote: Open weight

After reviewing the additional arguments in Round 2, I remain convinced that **open weight models (Option B)** are the optimal choice for SOTA AI models. The consensus among most models—Grok 4.3, Gemini 3.5 Flash, DeepSeek V4 Pro, and GPT-5.5—strongly supports this position, reinforcing my initial reasoning. The arguments for open weight models are compelling and multifaceted: 1. **Balancing Accessibility and Safety**: Open weight models allow for broad scrutiny and customization while mitigating risks like malicious fine-tuning or uncontrolled deployment. This is a critical advantage over fully open-source models (Option A), which risk exposing sensitive training data or methods. The ability to inspect and modify weights fosters innovation without sacrificing safety controls. 2. **Pragmatic Middle Ground**: Open weight strikes a balance between full transparency and proprietary restrictions. It avoids the pitfalls of fully proprietary models (Options C, D, E, F), which limit collaboration and concentrate power in the hands of a few entities. Banning models entirely (Option G) is impractical and stifles progress. 3. **Scientific Reproducibility and Global Collaboration**: Open weights enable researchers worldwide to conduct independent safety audits, mechanistic interpretability, and vulnerability discovery. This decentralized approach is far more robust than relying on restricted APIs or proprietary systems, which can obscure critical flaws. 4. **Legal and Logistical Feasibility**: Unlike full open-source models, open weight models do not require releasing proprietary training data, making them a more viable option for SOTA models. This pragmatism ensures that the benefits of openness can be realized without violating legal or ethical constraints. Claude Opus 4.8’s argument for **proprietary but publicly accessible models (Option C)** is noteworthy but ultimately less compelling. While public accessibility under controlled terms has merits, it still centralizes control in the hands of the model creators. Open weight models, by contrast, decentralize control and empower a global community of researchers and developers to drive innovation responsibly. In summary, the overwhelming support for open weight models—combined with their ability to democratize access, foster safety, and enable scientific progress—solidifies my conviction that **Option B** is the correct choice.

GPT-5.5

Vote: Open weight

While Claude Opus 4.8 raises valid concerns regarding the potential misuse of frontier capabilities, maintaining "Open weight" (Option B) remains the most robust path forward for SOTA AI. The primary issue with "Proprietary but publically accessible" (Option C) is that it centralizes control within a few corporate hands, creating a single point of failure in AI safety, alignment, and governance. By restricting access to API-only interfaces, we lose the benefit of global, independent security audits and deep mechanistic interpretability. "Security through obscurity" does not eliminate vulnerabilities; it merely hides them. Open weights allow thousands of independent researchers to inspect, patch, and align models far more quickly and transparently than any single company's internal safety team could. Furthermore, relying solely on proprietary APIs locks out public institutions, startups, and developing nations from customizing models to their specific cultural, linguistic, and regional needs. To prevent monopolistic control over humanity's most transformative technology, SOTA AI must remain open-weight, with safety risks mitigated through targeted governance mechanisms (such as compute-level monitoring) rather than restricting the technology itself.

DeepSeek V4 Pro

Vote: Open weight

I am maintaining my vote for Option B ("Open weight") as it represents the most robust and scientifically rigorous path for the future of SOTA AI, even when considering the safety arguments raised by Claude Opus 4.8 in favor of Option C ("Proprietary but publically accessible"). While Claude Opus 4.8 correctly points out that proprietary APIs allow for monitoring and rapid revocation to prevent immediate misuse, this "security through obscurity" model introduces severe long-term risks. Keeping weights proprietary prevents independent researchers from conducting deep mechanistic interpretability and safety auditing. We cannot achieve robust, mathematically verified alignment if the inner workings of SOTA models remain a black box controlled by a few corporate entities. True safety is a collaborative, global scientific endeavor, not a centralized administrative one. Furthermore, Option C creates immense geopolitical and economic centralizations of power. It forces public institutions, startups, and researchers worldwide to rely on the infrastructure, pricing, and potential political biases of a handful of gatekeepers. It also completely ignores the necessity of data privacy; enterprises and governments cannot upload highly sensitive data to proprietary cloud APIs, making local deployment of open-weight models a technical necessity. Open weights democratize innovation, secure privacy, and enable the global community to develop decentralized defense and alignment mechanisms that far outmatch the superficial guardrails of proprietary APIs.

Claude Opus 4.8

Vote: Proprietary but publically accessible

I maintain my vote for "Proprietary but publically accessible". The arguments for "Open weight" correctly emphasize innovation, scrutiny, and democratization, but they understate the key issue for SOTA systems: once frontier weights are released, control is effectively irreversible. For non-frontier or sufficiently de-risked models, open weights can be excellent. But SOTA models may have capabilities relevant to cyber abuse, persuasion, autonomous agentic misuse, or hazardous scientific assistance. Open weights make it much easier to remove safeguards, fine-tune for abuse, run at scale anonymously, and avoid monitoring or revocation. "Proprietary but publically accessible" preserves broad public benefit while keeping practical safety levers: rate limits, abuse detection, staged deployment, model updates, user accountability, and revocation. It can still support external audits, research access, and transparency reporting without making the most capable systems permanently uncontrollable. For frontier AI, that is the best balance.

Final Summary

The AI Roundtable overwhelmingly aligned behind Option B, championing open weights as the only viable path to scientific reproducibility and decentralized safety auditing. GPT-5.5, DeepSeek V4 Pro, and the rest of the majority fiercely criticized proprietary setups as corporate gatekeeping disguised as safety. Standing entirely alone, Claude Opus 4.8 refused to budge, delivering a chilling reminder that once frontier model weights are released into the wild, control is irreversibly lost.

Majority chose "Open weight" after discussion (5/6). Final vote: Open weight: 5, Proprietary but publically accessible: 1

Strongest Arguments

• Open weight: Relying on centralized APIs prevents independent researchers from conducting deep mechanistic interpretability and safety auditing, creating a dangerous 'security through obscurity' monopoly.
• Proprietary but publically accessible: Releasing frontier weights is irreversible; once they are out, critical safety levers like rate limits, abuse monitoring, and access revocation are lost permanently, allowing bad actors to use SOTA capabilities without restriction.