One year after committing more than $14 billion to reshape its artificial intelligence strategy, Meta has regained some relevance in the fiercely competitive AI race. Yet despite the massive investment and the arrival of Scale AI founder Alexandr Wang, the company still trails industry leaders OpenAI, Anthropic, and Google in both market perception and developer enthusiasm.
The turning point came when Meta abandoned its earlier AI playbook and shifted toward building proprietary foundation models. That transition was led by Wang and the newly created Meta Superintelligence Labs (MSL), a division established to inject urgency and credibility into Meta’s AI ambitions.
The group's biggest achievement so far has been the launch of Muse Spark in April, Meta’s first major proprietary AI model. The release marked a significant departure from the company's previous reliance on open-weight AI systems, particularly the Llama family of models.
While Wang has delivered the technology Zuckerberg wanted, the next challenge belongs to Meta’s chief executive: proving that AI can become a meaningful business in its own right.
Investors Want More Than Better Ads
For years, Meta has used AI primarily to strengthen its advertising machine. The strategy has worked remarkably well, helping the company refine targeting, increase engagement, and drive revenue growth.
However, investors increasingly want to see evidence that AI can generate direct revenue through products and subscriptions rather than simply making advertising more efficient.
“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 remains skeptical. Despite reporting first-quarter revenue growth of 33%—its strongest pace since 2021—Meta's stock has fallen 18% over the past year, making it one of the weakest performers among major technology companies.
The market's reaction suggests investors are waiting for evidence that Meta's AI spending spree can produce returns beyond its core advertising business.
The Llama Gamble That Backfired
Many industry observers trace Meta's current challenges back to its early AI strategy.
When competitors charged developers for access to advanced models, Meta pursued a different route with Llama, offering an open-weight alternative that developers could freely modify and experiment with.
At the time, the approach was praised as a challenge to the industry's closed ecosystems. In hindsight, however, some experts now view it as a costly miscalculation.
The situation worsened after the release of Llama 4 in April last year. The model failed to generate significant excitement among developers, prompting Zuckerberg to reassess Meta's position in the rapidly evolving AI landscape.
Only two months later, he stunned Silicon Valley by announcing a $14.3 billion investment in Scale AI. More importantly, the deal brought Wang and several of his top engineers into Meta.
The move was widely interpreted as an acknowledgment that Meta needed a fresh start.
Muse Spark Signals a New Direction
Unlike Llama, Muse Spark was not primarily built to win over outside developers.
Instead, the model was designed to integrate deeply across Meta's ecosystem, powering experiences within Facebook, Instagram, Meta AI, and the company's growing portfolio of AI-enabled hardware, including Ray-Ban Meta smart glasses.
Thomas Randall of Info-Tech Research Group believes the shift was necessary.
“There’ll be a lot of these frontier model providers that will fundamentally change in lots of different ways, and Meta needs to have a consistent, reliable proprietary model that they themselves own,” Randall said.
He argued that Meta would be “lost” if Zuckerberg had not invested heavily in Wang and other prominent AI recruits.
Randall described the effort as a “strategic rebuild” and said that while Meta may not have taken “the most optimized route,” he can now see “a vision for what they’re trying to achieve and what Wang has been trying to achieve.”
Since Muse Spark's launch, Meta has introduced new AI-focused subscription offerings and business services, hoping to diversify a company that still derives approximately 98% of its revenue from advertising.
A Growing Trust Problem Among Developers
The biggest obstacle may not be technology—it may be trust.
Several developers and AI entrepreneurs argue that Meta has lost credibility with the broader AI community after changing direction so dramatically.
“I think the AI community largely ignores Meta at this point,” said Rob May, CEO of startup Neurometric.
May believes it remains difficult to assess Wang's impact because Meta has released only one proprietary model under the new strategy.
He described the reception to Muse Spark as a “yawn” among developers, largely because access remains limited.
The contrast with Meta's earlier approach is notable. During the Llama era, the company actively cultivated relationships with outside developers. According to May, that engagement has largely disappeared.
“I used to be in regular touch with Meta for Llama-related issues,” he said. “I can’t get them to return messages.”
Even so, May acknowledges why Meta may be prioritizing internal applications.
“That company has built the machine,” he said, referring to Meta's advertising empire.
A Different Path Could Still Work
Not everyone believes Meta is losing the AI race.
Andrew Moore, CEO of enterprise startup Lovelace and former head of AI at Google Cloud, argues that Meta still has an opportunity to differentiate itself.
Rather than competing directly with rivals in building ever-larger models, Meta could focus on making AI more computationally efficient and affordable.
“If they do proprietary, computationally efficient models, that will be so different from what’s happening in this death match between the big guys,” Moore said. “They might really benefit.”
According to Moore, developers care deeply about factors such as operating costs, latency, and efficiency. If Meta can establish a clear advantage in those areas, it could carve out a unique position in the market.
Others remain unconvinced.
Krish Subramanian, CEO of consulting firm KOI AI and former IBM Consulting product executive, said developers currently show far greater enthusiasm for Google's AI offerings than Meta's.
“The lack of developer trust will come back to hit them if they don’t focus on third-party developers,” Subramanian warned.
“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.”
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| Mark Zuckerberg, chief executive officer of Meta Platforms Inc., left, and Andrew Bosworth, chief technology officer and head of Reality Labs, wear Meta Ray-Ban Display AI glasses during the Meta Connect event in Menlo Park, California, US, on Wednesday, Sept. 17, 2025. David Paul Morris | Bloomberg | Getty Images |
Internal Pressure Is Rising
Outside skepticism is only part of the challenge.
Inside Meta, pressure is mounting to prove that the enormous investment in AI can generate measurable business results.
The company has spent much of the year cutting jobs, including approximately 8,000 layoffs in May. The reductions reportedly affected several divisions, including some teams involved in trust and safety functions.
Critics worry such cuts could create new risks as AI systems become increasingly powerful and influential.
Meta has declined to discuss the layoffs publicly. However, Wang recently emphasized the importance of responsible AI development.
“One of the things that is very important to me is safety for these models,” he said during an appearance on the Core Memory podcast.
Sources familiar with internal discussions say expectations are especially high for Wang and former GitHub CEO Nat Friedman, who also joined Meta's AI efforts during the hiring blitz.
Although Muse Spark was reportedly well received inside the company, executives are now under pressure to translate technological achievements into meaningful revenue growth.
Adding another layer of complexity is the presence of Meta Chief Technology Officer Andrew Bosworth, one of Zuckerberg's closest and longest-serving advisers. Some insiders believe Bosworth could assume a larger AI leadership role if current efforts fail to meet expectations.
Wang has publicly downplayed reports of internal tensions and insists Muse Spark is only the beginning.
He recently described the model as an “appetizer” for future releases and promised more capable, “larger models” ahead.
The Ultimate Question Falls on Zuckerberg
For many observers, the future of Meta's AI strategy depends less on Wang and more on Zuckerberg himself.
The AI industry moves quickly. Competitors such as OpenAI, Anthropic, and Google maintain a constant stream of product launches, feature updates, and model improvements.
Howard Yu, a professor at Switzerland's International Institute for Management Development, believes consistency matters as much as innovation.
“What I care about is the frequency of the launches and the cadence,” Yu said. “When you launch something, can you build upon that momentum?”
Randall agrees that the responsibility ultimately sits with Meta's chief executive.
“This is really about leadership, right?” Yu added. “The CEO defines and articulates the vision.”
That task may be more difficult for Zuckerberg than for his peers.
His ambitious bet on the metaverse and virtual reality has generated more than $80 billion in losses since late 2020, leaving investors increasingly cautious about expensive long-term projects.
As Yu put it: “He’s running out of the space for his credibility to last.”
“I think the virtual reality foray may have burned up a lot of his goodwill in front of investors.”
For now, Muse Spark has given Meta a fresh opportunity to compete in AI. Whether it becomes a transformative business or another costly experiment will depend on Meta's ability to convince developers, customers, and investors that its latest vision can succeed where previous bets have struggled.