For two years, Meta has positioned itself as the company that would democratize frontier AI. Its open-model strategy challenged the closed ecosystems being built by rivals.
Now that narrative is facing its first real stress test.
Meta’s upcoming AI model Avocado, originally expected to launch in early 2026, has reportedly been pushed back after internal tests showed the system struggling to match the latest frontier models. The delay itself isn’t unusual in AI development.
What matters is why it happened — and what it reveals about the current hierarchy of the AI race.
Why Avocado Failed the Gemini 3 Benchmark
Inside Meta’s AI teams, Avocado was meant to close the capability gap with the industry’s leading models.
Early benchmarks reportedly told a more complicated story.
The model performed well in several reasoning and coding tests, outperforming earlier systems such as Gemini 2.5. But it failed to surpass the newer generation of models released by Google, particularly the latest versions of Google Gemini.
That result placed Avocado in an uncomfortable middle ground:
competitive, but not frontier-defining.
For a company investing tens of billions into AI infrastructure, “competitive” isn’t the benchmark. Leadership wants breakthrough performance.
The $135 Billion AI Bet
The stakes are enormous.
Meta plans to spend roughly $115–$135 billion on infrastructure and AI development in 2026, one of the largest technology investment cycles in the company’s history.
CEO Mark Zuckerberg has repeatedly framed this spending as a long-term push toward advanced artificial intelligence that can power everything from digital assistants to autonomous software agents.
Avocado was expected to be a cornerstone of that strategy.
Instead, the model is now being reworked while engineers attempt to improve reasoning depth and training efficiency.
Inside Meta’s Superintelligence Labs
Another factor shaping the Avocado delay is the internal restructuring of Meta’s AI research organization.
The company recently consolidated several teams into Meta Superintelligence Labs (MSL) — an ambitious new division focused on pushing toward artificial general intelligence.
The initiative is led by Alexandr Wang, the founder of Scale AI, whose expertise in training data infrastructure has become increasingly valuable as models demand higher-quality datasets.
MSL’s mandate is simple but daunting:
build systems that can rival — and eventually surpass — the most advanced AI models on the market.
Avocado was meant to be the first visible step toward that goal.
The Licensing Plot Twist
Perhaps the most surprising development surrounding Avocado is the contingency plan reportedly being discussed inside Meta.
According to industry reports, the company has considered temporarily licensing Google’s Gemini technology to support some AI features while Avocado continues development.
If that scenario unfolds, it would represent a symbolic reversal.
Meta has spent years promoting an open-model strategy, arguing that open AI ecosystems would outperform closed ones. Relying on a competitor’s model, even briefly, would highlight how difficult the frontier race has become.
The Other Models: Mango and Watermelon
Avocado is only one piece of Meta’s expanding AI roadmap.
Two additional projects have recently surfaced in leaks and industry reporting:
Mango
A multimodal system focused on image and video generation, designed to compete with next-generation creative AI tools.
Watermelon
An internal successor to Avocado that could introduce more advanced reasoning capabilities and improved long-context performance.
These projects suggest Meta is planning a multi-model ecosystem, similar to strategies being pursued across the industry.
The Hard Truth About Frontier AI
The Avocado delay points to a broader shift in the AI landscape.
In the early days of large language models, progress often came from scaling:
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larger datasets
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more parameters
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bigger GPU clusters
Today that formula is no longer enough.
Frontier labs are now racing to solve deeper challenges:
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reasoning and planning
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long-term memory
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autonomous agents
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alignment and reliability
These problems behave less like engineering tasks and more like scientific research.
Breakthroughs are unpredictable.
What Happens Next
Meta still holds several powerful advantages:
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massive user distribution through its platforms
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one of the largest AI compute infrastructures in the world
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a thriving developer ecosystem around open models
But the frontier race has tightened.
Companies like Anthropic, Google, and OpenAI continue to push forward with rapid improvements in reasoning and agentic AI.
If Avocado eventually launches with a major performance leap, the delay will be forgotten.
If it doesn’t, the industry may start asking a harder question:
Is Meta still chasing the frontier — or slowly falling behind it?
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