It’s everyone but Meta in a new AI standards group

It appears Meta has opted to go in a whole new direction in response to this week’s formation by The Linux Foundation of a group called the Agentic AI Foundation (AAIF), designed to help enterprises develop and manage AI agents through a “shared ecosystem of tools, standards and community-driven innovation.”

The group is made up of a who’s-who of the tech industry, ranging from AWS and OpenAI to Google, Microsoft, IBM, and Cisco. The one name missing from the list is Meta, and, according to a Bloomberg article published on Wednesday, there is a reason for that: the firm is working on a new proprietary model, code-named Avocado, that will generate revenue.

Brian Jackson, principal research director at Info-Tech Research Group, said, ”[Meta was] never interested in a truly open source model approach, just an open weights model approach. To really commit to open source, [it] would have to be willing to share its training data and give up control over model governance.”

Weights, he said, “are just the different knobs along the neural pathways that can be tweaked when training a model. Clearly, [Meta] views its training data as a competitive differentiator or sees some other risk in making it public. It also wants to maintain control over the governance of its models in terms of how they can be integrated with other vendors’ platforms.”

Jackson pointed out that, now that it sees that The Linux Foundation is going to better define a standard for truly open source models, Meta realizes it isn’t going to be able to define the space and distribute its model in the way it intended.

Open weights models vs. open source software

Asked whether developing cutting-edge open source models is becoming too expensive for anyone to contemplate doing without some sort of revenue stream, he noted that at AWS re:Invent last week, AWS CEO Matt Garman had some interesting comments about these open weights models in an analyst Q&A session.

Jackson said, “he pointed out that open source software works because the community contributes back to the projects. But with open weights models, only the provider contributes to the release. These business models are too expensive and not a long-term play,” he said, “and providers may eventually have to charge for them.”

Meta, he said, is proving Garman right. “They didn’t really have a clear business model to collect revenue on Meta’s open weights models,” he said. “Perhaps part of their strategy was to commoditize LLMs and undermine the business models of their competitors.”

But the scale of these models, said Jackson, “continues to grow, and competition is pushing AI makers to invest more into training techniques, talent, and infrastructure to support it all. So, Zuckerberg has to pivot and find a way to monetize Meta’s work here. The best way in the industry to do that is to put a gated API on your model and charge a price per token.”

Sanchit Vir Gogia, the chief analyst at Greyhound Research, said Meta’s pivot from open source AI to a closed, monetized model architecture “marks a deliberate departure from the cooperative direction that most of the AI industry is now moving toward. This is not a tactical adjustment. It is a structural shift that signals a different philosophical stance on the future of AI infrastructure.”

Meta positioning itself as ‘self-contained island’

He added that while organizations such as OpenAI, Google, Anthropic, Microsoft, and others are aligning under the Agentic AI Foundation to create open, neutral standards for agent interoperability, Meta is choosing vertical integration and platform control.

This, said Gogia, looks like a shift in how Meta wants to position its AI bets commercially. “The open source chapter, impactful as it was, always had an expiry date once performance demands, infrastructure overheads, and monetization pressure started to close in,” he noted.

Staying competitive at the frontier, he added, “now means keeping optimization in-house, running tighter R&D loops, and owning the entire stack. The move toward a closed model, with Avocado at the centre, tells us Meta no longer sees its AI as fuel for the ecosystem. It sees it as product — something to sell, protect, and scale.”

This shift is not surprising, said Gogia, “but it is consequential. It reshapes how Meta will be perceived by developers, enterprise buyers, and industry partners. Openness earned Meta trust and relevance when it was trying to gain ground. Closing the stack now allows for performance control, monetization levers, and vendor differentiation.”

In addition, he pointed out, “it also isolates Meta from the standards-led coalition building that is defining the next phase of agentic AI. That isolation may serve short-term commercial objectives, but it risks long-term architectural compatibility in a world that is trending toward interoperable intelligence.”

By staying outside of the AAIF framework, the likely result for Meta is architectural fragmentation, he noted. “Enterprises may find that agents developed within Meta’s platforms are functionally incompatible with broader industry patterns. This may benefit Meta’s platform stickiness, but it undermines the broader ecosystem’s push for composability, portability and open orchestration.”

In a world where CIOs are demanding interoperable intelligence, Gogia said, “Meta is positioning itself as a self-contained island. That may serve its own apps and ad systems, but it puts it out of sync with where collaborative infrastructure is heading.”