Meta is facing pressure to demonstrate the commercial success of its new artificial intelligence efforts following a $14.3 billion investment last year. This investment, which included acquiring roughly half of Scale AI and bringing in Alexandr Wang and his team to form Meta Superintelligence Labs, was intended to pivot the company toward proprietary foundation models. While Meta delivered the Muse Spark AI model in April, the company remains behind competitors like OpenAI, Anthropic, and Google in market share. Analysts note that Meta must now prove it can attract paying users for its AI tools rather than solely using the technology to bolster its core advertising business, which still accounts for 98% of its revenue. While Meta reported 33% revenue growth in the first quarter, its stock has declined 18% over the past 12 months, making it one of the worst performers in the megacap group. Some industry experts characterize the company’s previous open-source strategy with the Llama family as a strategic blunder, noting that the release of Llama 4 failed to captivate developers. The Muse Spark model was designed to integrate into Meta’s existing apps, such as Facebook and Instagram, and hardware like Ray-Ban Meta glasses. However, some community members have expressed disappointment, describing the model as a “yawn” due to its limited accessibility compared to the steady updates provided by competitors. Also, reports indicate internal tensions regarding revenue growth and concerns over morale following the firing of approximately 8,000 workers in May, including some in trust and safety roles. Meta’s path forward involves balancing its new proprietary focus with its historical support for the open-source ecosystem. A company spokesperson stated that Meta still plans to offer outside developers access to Muse Spark’s underlying technology via an API. Meanwhile, the company is exploring efficiency as a differentiator, with some experts suggesting that computationally efficient proprietary models could provide a competitive advantage in the high-cost AI market.