"A year after spending over $14 billion to bring in Alexandr Wang and a group of his top Scale AI engineers to revamp its artificial intelligence efforts, Meta is at least back on the map in AI," reports CNBC, "though it's still far behind OpenAI, Anthropic and Google in the market." Wang's big accomplishment was the delivery of the Muse Spark AI model in April, marking Meta's first jump into proprietary foundation models and away from a strict adherence to open source, or open weight as it's more commonly called in AI... "Meta needs to provide more proof points of both adoption and commercialization," said Ralph Schackart, an analyst at William Blair who recommends buying the stock. "Investors are looking for Meta to monetize a new AI-first product, beyond the substantial positive impact AI is having on enhancing the advertising models." Wall Street, at least so far, is unimpressed. Meta's stock is down 18% over the past 12 months, the worst performer in the megacap group, along with Microsoft, which has its own challenges in AI. That's even after Meta reported 33% revenue growth in the first quarter, the fastest rate of expansion for any period since 2021. For Meta, the problem started with what some industry experts called, in hindsight at least, a strategic blunder. The company jumped into AI with its Llama family of models, offering an open-source approach that allowed developers to freely tinker, while the other big model makers charged for access. In April of last year, Meta's release of Llama 4 fell flat, failing to captivate developers and leading Zuckerberg to reconsider his company's approach to AI development... Since the release of Muse Spark, Meta has unveiled new AI and business-related subscription plans as part of an effort to expand its business beyond online ads. Historically, it hasn't worked. Meta still counts on ads for 98% of revenue. Schackart said he wants to see "tangible evidence of a growing list of new, AI-first products created by Muse Spark, even if monetization lags." He said that's "what investors are looking for." No matter how good Wang's model may be, Zuckerberg has a high hill to climb with developers coming off the Llama debacle. "I think the AI community largely ignores Meta at this point," said Rob May, CEO of the startup Neurometric, which works in the realm of token engineering.... Krish Subramanian, the CEO of consulting firm KOI AI and former product head at IBM Consulting, said developers are more excited about Google's AI models than what Meta is offering. The appeal of Llama was that it specifically targeted developers wanting open-weight alternative models, while with Muse Spark, Meta has made little effort in that direction, he said. "The lack of developer trust will come back to hit them if they don't focus on third-party developers," Subramanian said, noting that it took years for Microsoft to regain trust from open-source coders during the early days of Azure. "To just focus on a walled-garden kind of an ecosystem and ad revenue as the main source of income, they probably will never become the big player," he said. A Meta spokesperson pointed to Wang's recent comments about the company's continued support for the open-source ecosystem, and said Meta still plans to offer outside developers access to Muse Spark's underlying technology via an API, as it previously announced. "We're already testing with some early partners, and look forward to releasing it this month," the spokesperson said. "That Zuckerberg's metaverse and virtual reality ambitions have generated over $80 billion in total losses since late 2020 makes the AI pitch a tougher sell," the article points out, citing this observation from Howard Yu, business professor at Switzerland's International Institute for Management Development. "He's running out of the space for his credibility to last," Yu said. "I think the virtual reality foray may have burned up a lot of his goodwill in front of investors."

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America's Energy Department "wants to build a single national platform for doing science with AI," reports Communications of the ACM: It is called the Genesis Mission, and the idea is to connect the country's 17 national laboratories, their supercomputers, scientific datasets, and a growing layer of AI models and agents into one system researchers can access. The DOE has taken to calling it 'a national operating system for science.' That means treating compute, data, and AI models the way the country treats power lines and highways, as shared national plumbing everyone else builds on top of. If it works, Genesis will change how scientific work gets organized, checked, and scaled, with AI helping run the whole pipeline from hypothesis to simulation to experiment and back. The pitch is that this is better understood as infrastructure policy than as another research program. Genesis is now moving from announcement into execution. President Trump signed the executive order launching it in November 2025. This past February, the DOE published 26 science and technology challenges for the program, and in March it opened a $294-million call for research teams in fields like nuclear energy, quantum information science, semiconductors, and biotechnology. The program is also beginning to reach beyond U.S. borders. In June 2026, Japan moved to become Genesis's first international partner. The two governments plan to invest a combined $1 billion over five years, with Japan contributing $500 million toward joint work in quantum technology, nuclear fusion, and biotechnology. The stated goal is staying ahead of China in the fields where AI is advancing fastest. The open question is whether a federated platform this big can actually work, or whether it ends up as one more expensive coordination exercise.

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