Meta May Rethink Open Source AI Strategy Amid Behemoth Delays

Key Takeaways:

• Meta has delayed the release of its powerful AI model Behemoth after underwhelming internal performance and paused further testing following the launch of its Superintelligence Lab.

• Leaders inside Meta are now weighing a shift toward closed-source model development, according to sources cited by The New York Times.

• Meta says its position on open source is “unchanged,” but did not address whether Behemoth specifically will be released.

• CEO Mark Zuckerberg has historically championed open-source AI as a differentiator, though past statements suggest he’s open to holding back certain models.

• A pivot to closed models could impact startups and researchers who rely on open foundation models and reshape the AI competitive landscape.

Internal Debate Over Meta’s AI Strategy

Meta’s commitment to open-source AI, once a defining feature of its strategy, is reportedly under reconsideration. According to The New York Times, leadership within Meta’s newly formed Superintelligence Lab has discussed pivoting away from releasing its next major model, Behemoth, as open source—and instead pursuing a closed approach.

Sources told the Times that Meta recently completed training Behemoth, but decided not to release it due to underwhelming performance. Testing reportedly stopped altogether after the new lab launched. While no final decision has been made, these discussions signal potential friction between Meta’s open-source legacy and its evolving ambitions in AI.

A Meta spokesperson declined to comment specifically on Behemoth but told TechCrunch, “We plan to continue releasing leading open-source models. We haven’t released everything we’ve developed historically and we expect to continue training a mix of open and closed models going forward.”

What Behemoth Represents — and Why It Matters

Behemoth was expected to be a powerful next-generation model, extending Meta’s influence in the open-source community that has rapidly coalesced around its Llama model family. If Meta ultimately holds Behemoth back, it could signal a meaningful shift in how the company sees openness—not as a mission, but as a strategy subject to change.

CEO Mark Zuckerberg has previously been vocal about the advantages of openness, especially when contrasting Meta with more closed competitors like OpenAI. But he has also acknowledged the limits of that philosophy. On a podcast in 2023, he explained:

“We’re obviously very pro open-source, but I haven’t committed to releasing every single thing that we do. I’m basically very inclined to think that open sourcing is going to be good for the community and also good for us because we’ll benefit from the innovations. If at some point, however, there’s some qualitative change in what the thing is capable of, and we feel like it’s not responsible to open source it, then we won’t. It’s all very difficult to predict.”

This nuanced stance offers room for Meta to pivot without entirely abandoning its public messaging.

Rising Costs, Monetization Pressures, and Talent Wars

Meta’s investments in AI have intensified in recent quarters, including:

• Signing top researchers with multimillion-dollar compensation packages.

• Expanding its data center infrastructure.

• Building models aimed at achieving artificial general intelligence (AGI).

These efforts have placed pressure on Meta to commercialize more of its AI research—beyond its traditional ad business. Closed models could offer more flexible monetization pathways, particularly as Meta’s internal teams pursue proprietary assistants like Meta AI.

Despite operating one of the world’s top AI labs, Meta still trails companies like OpenAI, Anthropic, Google DeepMind, and xAI when it comes to commercial traction. Closing off some models could help protect Meta’s IP and give it a competitive edge in areas where openness may no longer serve its long-term business goals.

Impact on Open-Source Ecosystems and Global Competition

If Meta steps back from open-source leadership, the consequences could ripple far beyond its own walls. The Llama models have become essential building blocks for hundreds of startups and research labs working on fine-tuning, alignment, and domain-specific applications. Without steady releases from Meta, momentum in open foundation models may slow, leaving smaller players more reliant on older checkpoints or community-led alternatives.

The timing is also notable as OpenAI has delayed its own promised open model, and fewer major players seem inclined to release powerful weights without constraints. That may centralize power around closed systems and shift AI development back toward corporate silos.

Internationally, Meta’s retreat from openness could cede ideological and strategic ground to China, where companies like DeepSeek and Moonshot AI have embraced open sourcing as a means of accelerating domestic innovation and influence. If U.S. firms reduce their open contributions, the global balance of AI research accessibility may begin to tilt.

Fast Facts for AI Readers

Q: What is Meta’s Behemoth model?

A: Behemoth is a next-generation large language model trained by Meta, expected to follow Llama but has not been released.

Q: Why hasn’t Behemoth been released?

A: Reports suggest its internal performance fell short of expectations, and testing stopped after Meta’s Superintelligence Lab launched.

Q: Is Meta ending its open-source AI strategy?

A: Not officially. Meta says it still supports open source, but internal discussions and past remarks suggest flexibility in how and when it releases models.

Q: What could change if Meta stops open sourcing?

A: It could slow innovation in the open AI ecosystem and strengthen the position of closed-model companies like OpenAI and Google DeepMind.

What This Means

Meta’s potential shift away from open-source AI isn’t just a product decision—it’s a signal that the balance of power in AI development may be tilting. For years, Meta used openness as a strategic counterweight to closed platforms like OpenAI and Google DeepMind, positioning its models as infrastructure for a broader ecosystem of innovation. That openness gave startups, academics, and independent researchers rare access to cutting-edge AI—and helped Meta shape public narratives around transparency, speed, and trust.

A retreat from that posture could narrow the paths available to smaller players, entrenching the dominance of a few corporate labs with the compute, capital, and proprietary data needed to train large models. It may also shift how governments and global institutions regulate or support open AI research—especially in light of geopolitical tensions with countries like China, which has made open source a lever of influence.

Ultimately, if Meta chooses to prioritize control and monetization over openness, it will redefine not just its own roadmap but the direction of AI progress around the world. The decision won’t just shape what Meta builds—but who else gets to build on top of it.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.