When Openness Backfires: Meta’s Llama And The New AI Cold War

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Former Meta executive Sarah Wynn-Williams is set to testify before the US Senate Judiciary Committee on Wednesday, revealing how the company’s AI model, Llama, played a critical role in accelerating China’s AI capabilities — particularly contributing to the rise of DeepSeek.

“For years, regulation has focused on the hardware layer — chips, servers, and physical exports. But foundational models are different. They don’t move through ports or carry serial numbers,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “They are shared digitally, openly, and rapidly. Once they’re released, they’re almost impossible to track. At Greyhound Research, we believe the US and its allies must now develop an AI-native regulatory toolkit. The old frameworks simply don’t apply. We’re not advocating for a crackdown on open-source innovation — but we do believe smarter, more precise controls are urgently needed,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research.

As quoted in ComputerWorld.com

Additional comments by Greyhound Research analyst:

Global Influence of Meta’s LLaMA Release

The broader impact of Meta’s decision to open-source LLaMA is still being assessed, particularly as questions surrounding downstream adaptation continue to surface. That said, it’s evident that the model has already influenced the trajectory of global AI development—both in terms of who can build, and how quickly.

At Greyhound Research, we believe LLaMA’s release represented more than a technical milestone. It brought to light a deeper tension now shaping the AI landscape: the push to democratise access, set against the growing need to preserve strategic and sovereign control. By releasing LLaMA’s model weights—the critical foundation of its capabilities—Meta effectively lowered the barriers to entry for a broad set of actors, including institutions outside the traditional sphere of Western partnerships.

To be clear, Meta’s intent was not to empower adversaries. Rather, it sought to unlock innovation across academia, startups, and smaller research ecosystems. However, as Greyhound Fieldnotes from AI teams in Southeast Asia and Eastern Europe confirm, once the model weights became public, they were quickly adapted for a wide variety of goals—some aligned with Meta’s vision, others entirely independent.

This moment marks a critical inflection point in the global AI race. The discussion is no longer limited to breakthroughs in model architecture or training scale. It now includes control over model diffusion, licensing protocols, and usage governance. In today’s geopolitically charged environment, openness—once seen as a virtue—has become a point of strategic vulnerability.

At Greyhound Research, we believe the centre of gravity is shifting. AI progress can no longer be measured by performance alone. Increasingly, the decisive advantage lies in access control: who governs usage, who participates in shaping the rules, and who holds accountability when foundational models ripple into unintended arenas. Greyhound CIO Pulse 2025 data shows a marked rise in enterprise anxiety over model provenance—especially in sectors like healthcare, banking, and public infrastructure.

Ripple Effects of the LLaMA–DeepSeek Allegation

First, it’s important to state with full clarity: there is no formally proven link between Meta’s LLaMA model and China’s DeepSeek at this time. While recent testimony and public commentary have raised concerns, no definitive technical attribution has been presented, and any analysis must proceed with caution.

That said, the pattern itself is not new. At Greyhound Research, we believe China’s national AI strategy has consistently prioritised velocity over originality—absorbing public research, adapting open models, and rapidly deploying at scale. In that context, it would not be surprising if LLaMA’s release, intentionally or not, served as a structural accelerant for China’s AI trajectory.

Greyhound Fieldnotes from global model audits in Q4 2024 show a proliferation of LLaMA-derived forks on public repositories, including training experiments in Mandarin and model fine-tunes integrated into regional platforms. Even if DeepSeek was not trained directly on LLaMA, it likely benefited from the same ecosystem of reusable weights, datasets, and scaffolding openly distributed after LLaMA’s release.

More broadly, we must account for China’s vertical integration strategy. It is not simply importing models—it is plugging them into homegrown AI chips, cloud infrastructure, and tightly controlled deployment environments. This creates a self-sustaining stack that is difficult to monitor, let alone govern.

At Greyhound Research, we believe this is a wake-up call. When foundational models are released without sufficient international safeguards, they can become scaffolding for rival AI ecosystems. And when that happens, influence slips. You are no longer exporting leadership—you are licensing acceleration. Greyhound CIO Pulse 2025 data reflects growing concern among policy leaders and regulators about Western-origin AI models being used in unintended geopolitical contexts.

Impact on Emerging Markets in Southeast Asia, the Middle East, and Africa

This is where the situation becomes most delicate. Countries that have embraced open-source AI in good faith—nations such as Indonesia, Nigeria, and the UAE—now risk being collateral damage in a broader geopolitical response.

At Greyhound Research, we’ve observed that open models like LLaMA have been critical to digital sovereignty efforts across the Global South. These models lower the barrier to entry for local innovators—removing the need for large GPU clusters or billion-dollar training budgets. Greyhound Fieldnotes from national AI programs in Kenya and the UAE confirm that LLaMA has been used to build real-world tools in agriculture, public health, education, and even judicial systems.

However, if the response to the current situation is indiscriminate—through broad export restrictions or sweeping licensing limits—there’s a very real risk that responsible adopters will be cut off from critical resources. Meanwhile, those who have already downloaded and adapted these models may continue their work unregulated and unchecked.

At Greyhound Research, we believe this moment requires nuance, not knee-jerk reaction. Greyhound CIO Pulse 2025 sentiment tracking from March 2025 shows a dip in trust around open-source AI governance in Africa and Southeast Asia. If emerging markets begin to perceive open models as politically unstable assets, they may shift toward building sovereign alternatives or align with ecosystems that offer fewer restrictions—regardless of origin.

There’s also a moral hazard here. If restrictions fall hardest on responsible actors, while others quietly continue development under the radar, we risk not just stalling innovation—we risk reshaping digital alliances. This is not just about models. It’s about trust, inclusion, and who gets to participate in the future of AI.

Regulatory Guardrails and the Road Ahead

This conversation is long overdue. For years, regulation has focused on the hardware layer—chips, servers, and physical exports. But foundational models are different. They don’t move through ports or carry serial numbers. They are shared digitally, openly, and rapidly. Once they’re released, they’re almost impossible to track.

At Greyhound Research, we believe the U.S. and its allies must now develop an AI-native regulatory toolkit. The old frameworks simply don’t apply. We’re not advocating for a crackdown on open-source innovation—but we do believe smarter, more precise controls are urgently needed.

Our recommendations include:

Greyhound Fieldnotes from regulatory advisory work across Southeast Asia reveal a growing interest in such frameworks—but also frustration at the lack of coordinated action across jurisdictions. Meanwhile, Greyhound CIO Pulse 2025 tracking shows that over 40% of enterprise technology leaders now rank “AI governance gaps” as one of their top three emerging risks.

The road ahead is not about closing doors—it’s about designing smarter thresholds. At Greyhound Research, we believe that foundational models must now be treated as infrastructure, not just intellectual property. And in doing so, we must move toward a new paradigm: where openness is accompanied by visibility, and innovation is matched with responsibility.

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