Meta increases compute commitment for its next proprietary model
According to Techub, citing CryptoBriefing, Meta AI chief Alexandr Wang said that the company’s next proprietary model, Watermelon, has been assigned 10 times the compute used for Avocado. The statement is being read by the market as evidence that Meta is adjusting its AI roadmap and moving away from prioritizing the open-source Llama family, instead putting more weight behind closed proprietary systems as it tries to catch up with OpenAI.
In competitive AI markets, larger compute budgets are not unusual on their own. What stands out here is the reported scale of the increase. A tenfold jump suggests a materially more aggressive posture in training resources, model ambition, and product timing. It also indicates that Meta may be reorganizing not just model development, but the broader way it allocates capital and infrastructure to AI programs.
Avocado delay highlights pressure from Google and OpenAI
The report says that Avocado, previously an important Meta model project, was delayed until May 2026 after it lagged behind Google Gemini 3.0 in November last year. That detail is significant because it points to dissatisfaction with the pace or quality of internal progress. It also underlines how intense the pressure has become among major AI developers competing on model capability, release cadence, and downstream commercialization.
At the same time, market chatter cited in the report claims that Watermelon has already reached the level of GPT-5.5. No benchmark results, model size, training stack, or inference cost details were disclosed. Even so, the claim strengthens the view that Meta is redirecting effort toward a higher-performance proprietary model rather than relying mainly on its earlier open strategy.
Scale AI investment adds context to Meta’s strategic reset
The Watermelon update also fits into a broader organizational and capital picture. Meta previously invested $14.3 billion in Scale AI and appointed Alexandr Wang as Chief AI Officer. Taken together, these moves suggest that Meta’s AI push is not limited to technical iteration. It appears to involve funding, leadership structure, and strategic reprioritization across the company’s AI stack.
For crypto professionals, the relevance is less about consumer AI branding and more about how spending concentration by major tech firms could alter market expectations around external infrastructure providers. When a company like Meta leans harder into vertically controlled, proprietary systems, investors often revisit assumptions about who captures value across compute, data, and deployment layers.
Why crypto markets may watch Render and Akash
The report specifically notes that the shift could influence expectations for decentralized compute platforms such as Render and Akash. The reason is straightforward: if large technology companies continue to build and scale more of their own closed AI systems internally, market participants may reassess the realistic addressable demand for third-party decentralized compute networks in AI training and inference workflows.
That said, the source stops short of claiming any direct business impact. It does not provide on-chain evidence, usage metrics, revenue changes, or confirmed commercial relationships tied to the announcement. At this stage, the story is best understood as a signal that could shape sentiment around AI-crypto infrastructure narratives, rather than as proof of a measurable shift in fundamentals.