The Open-Source Betrayal
Mark Zuckerberg wrote a 2,000-word manifesto in 2025 declaring “open source AI represents the world’s best shot at harnessing this technology to create the greatest economic opportunity and security for everyone.” Llama 2 and Llama 3 were open-weight models—anyone could download, modify, and fine-tune them. Tens of thousands of developers built businesses on Llama. Startups used it as their foundation. Meta was the open-source champion.
That’s over. Muse Spark is proprietary and locked down. It’s confined to meta.ai, the Meta AI app, and a select group getting private API access. The model architecture, training data, and weights are withheld. Meta isn’t just going closed—it’s “even more proprietary than paid models from rivals” like OpenAI and Anthropic, which at least offer broader API access.
Why? Open-source became a competitive liability. Chinese models captured 41% of Hugging Face downloads by late 2025. Sharing model weights helped the ecosystem but didn’t help Meta beat OpenAI. Meta rebuilt its entire AI operation in summer 2025 under Wang’s leadership because open-weight wasn’t closing the gap.
The $14.3 Billion Gamble on Alexandr Wang
In June 2025, Meta invested $14.3 billion in Scale AI, acquiring a 49% stake without voting power. Alexandr Wang, Scale’s CEO, left to lead Meta Superintelligence Labs. Jason Droege, Scale’s strategy chief, took over as CEO. Wang stayed on Scale’s board as a director, but his focus shifted to Meta’s proprietary push.
Scale AI—founded in 2016 by Wang and Lucy Guo—provides data labeling and model evaluation services to Google, Microsoft, OpenAI, and Meta. Meta was already one of Scale’s biggest clients. The $14.3 billion deal was about buying Wang’s expertise in data pipelines, evaluation frameworks, and training optimization.
Muse Spark is Wang’s first model. Can it justify the price tag?
Muse Spark Benchmarks: 4th Place Isn’t Impressive
Muse Spark scores 52 on the Intelligence Index, placing 4th overall. Gemini 3.1 Pro and GPT-5.4 tie at 57. Claude Opus 4.6 scores 53. Meta is playing catch-up.
The model has one strength: health AI. Its HealthBench Hard score of 42.8 beats GPT-5.4’s 40.1 and crushes Gemini’s 20.6. Meta collaborated with over 1,000 physicians to train the model on medical topics, and it shows.
Everything else? Weak. Abstract reasoning (ARC-AGI-2) scores 42.5 while GPT-5.4 scores 76.1—nearly double. Coding performance (Terminal-Bench 2.0) is 59.0 versus GPT-5.4’s 75.1, a 16-point gap. Agentic tasks (GDPval-AA) put Muse Spark at 1,444 ELO, trailing GPT-5.4 by 228 points and Claude by 163 points.
Meta’s “Contemplating mode”—advanced test-time reasoning—reaches 58% accuracy on Humanity’s Last Exam. That’s competitive, but not dominant. The rebuilt pretraining stack achieves capabilities “with over an order of magnitude less compute” than Llama 4, which is impressive efficiency. But efficiency doesn’t matter if you’re still losing to OpenAI and Google on the benchmarks developers care about.
Token efficiency is strong: 58 million output tokens matches Gemini and beats Claude’s 157 million. But token efficiency isn’t why Meta went proprietary. Meta went proprietary to beat OpenAI. Right now, they’re not.
The Developer Community Feels Abandoned
The r/LocalLLaMA community is furious. Developers who built on Llama’s open weights feel betrayed. A Meta spokesperson said “current Llama models will continue to be available as open source,” but didn’t address future Llama development. That’s a non-answer.
Wang posted on X that Muse Spark is “step one,” bigger models are in development, and Meta “plans to open-source future versions.” The community is skeptical. Meta said it believed in open AI. Then it didn’t.
The debate breaks three ways. Pragmatists argue open-source became a competitive liability and Meta had no choice. Idealists say Meta broke promises and destroyed developer trust. Realists say wait for the next model and judge Meta by what it ships, not what it promises.
What’s Next for Meta AI
Muse Spark is rolling out to Facebook, Instagram, WhatsApp, Messenger, and Ray-Ban Meta glasses. It’s available now at meta.ai and the Meta AI app. “Contemplating mode” is rolling out gradually. A private API preview is opening to select users. Meta is investing in the Hyperion data center to support scaling.
Wang hinted at bigger models already in development. Will they be open-weight or proprietary? If Meta keeps future models closed, the open-source era of Meta AI is over. If Muse Spark can’t close the performance gap, the proprietary pivot looks like a $14.3 billion mistake.
The question isn’t whether Meta can build AI models. It’s whether Meta can beat OpenAI and Google while alienating the developer community that supported Llama. Right now, Muse Spark ranks 4th. That’s not winning.
