Meta CEO Mark Zuckerberg makes a keynote speech at the Meta Connect annual event at the company’s headquarters in Menlo Park, Calif., on Sept. 25, 2024.
Manuel Orbegozo | Reuters
Meta is debuting its first major artificial intelligence model since the costly hiring of Scale AI’s Alexandr Wang nine months ago, as the Facebook parent aims to carve out a niche in a market that’s being dominated by OpenAI, Anthropic and Google.
Dubbed Muse Spark and originally codenamed Avocado, the AI model announced Wednesday is the first from the company’s new Muse series developed by Meta Superintelligence Labs, the AI unit that Wang oversees. Wang joined Meta in June as part of the company’s $14.3 billion investment in Scale AI, where he was CEO.
Meta is desperate to regain momentum in the fiercely competitive AI market following the disappointing debut of its Llama 4 family of AI models last April. With Llama, Meta had been taking an open-source approach to AI, in contrast to the proprietary models offered by rivals, but Llama 4 failed to captivate developers, leading to CEO Mark Zuckerberg to pivot his strategy.
“Over the last nine months, Meta Superintelligence Labs rebuilt our AI stack from the ground up, moving faster than any development cycle we have run before,” Meta said in a blog post on Wednesday. “This initial model is small and fast by design, yet capable enough to reason through complex questions in science, math, and health. It is a powerful foundation, and the next generation is already in development.”
The new Muse Spark will be proprietary, instead of open source, with the company saying there is “hope to open-source future versions of the model.”
Meta said in a technical blog about the new model that that improved AI training techniques along with rebuilt technology infrastructure has enabled the company to create smaller AI models that are as capable as its older midsize Llama 4 variant for “an order of magnitude less compute.”
“Muse Spark offers competitive performance in multimodal perception, reasoning, health, and agentic tasks,” Meta said in the post. “We continue to invest in areas with current performance gaps, specifically long-horizon agentic systems and coding workflows.”
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