Meta just delayed its Avocado AI model to May—the second time the flagship project has slipped—after internal tests showed it underperforms Google, OpenAI, and Anthropic. The company is now so desperate it’s considering licensing Google’s Gemini to power its own AI products. This isn’t just a delay. For a company spending $135 billion on AI infrastructure in 2026 alone, it’s a crisis of execution.
The Performance Gap Nobody Expected
Avocado was supposed to be Meta’s answer to GPT and Gemini. Instead, internal benchmarks place it between Google’s Gemini 2.5 and Gemini 3.0—respectable, but not frontier-level. The model struggles with logical reasoning, software development, and long-form writing. It can’t match the “agentic” behavior (autonomous decision-making) that defines leading AI systems.
Sure, Avocado beats Meta’s older models and edges out Gemini 2.5. But when you’re marketing a next-generation flagship, “better than last year’s Google model” isn’t the flex you want. Especially when your competitors just shipped updates in the same week you announced a delay.
Outspending Everyone, Falling Behind Anyway
Here’s the paradox: Meta is outspending every AI rival and losing ground technically. The company’s 2026 capital expenditure guidance sits at $115 to $135 billion, up from $60-65 billion in 2025 and $35-40 billion in 2024. That’s not gradual scaling. That’s an all-in bet.
Where’s the money going? Data centers like Hyperion in Louisiana—a 2,250-acre, $10 billion facility generating 5 gigawatts of compute power, partnered with a nuclear plant to handle the energy load. Prometheus in Ohio, powered by natural gas, comes online this year. Meta’s building the infrastructure of an AI superpower.
But infrastructure without competitive models is just expensive servers. Google, OpenAI, and Anthropic are shipping frontier AI with smaller budgets. Meta’s proving that capital spending alone doesn’t win the AI race—execution does.
The Licensing Admission
The clearest sign of trouble: Meta’s AI division is discussing temporarily licensing Google’s Gemini to power its own products while Avocado development continues. No final decision yet, but the fact it’s even on the table is extraordinary.
Think about it. A company spending $135 billion per year on AI is considering paying a direct competitor for the technology it can’t build fast enough internally. That’s not a stopgap—it’s an admission that Meta’s development velocity can’t keep pace with its product roadmap.
If that deal happens, Google gets revenue and validation. Meta gets a band-aid.
A Pattern, Not a Hiccup
This is Avocado’s second delay. The model was originally targeted for late 2025, then pushed to Q1 2026, and now May or later. Meanwhile, Meta’s already developing Watermelon, Avocado’s successor, before Avocado even ships.
One delay is understandable in AI development. Two delays suggest systemic problems. In a race where competitors launched 12+ models in a single week (March 1-8), every month of slippage compounds the disadvantage.
Investors Price in the Problem
Meta’s stock fell 4.18 percent to $611.48 on March 13 when the delay leaked—the sharpest drop in four months. The broader market was down only 0.4 to 0.9 percent that day. This was Meta-specific bad news.
The stock has traded at a premium valuation largely because investors believed in Meta’s AI leadership narrative. Delays that expose competitive weakness directly challenge that story. If Meta can’t deliver frontier models despite record spending, what justifies the premium?
The Competition Isn’t Waiting
While Meta delays, rivals advance. Google just expanded its Pentagon AI contracts with Agent Designer, a no-code tool for building custom AI agents across the DOD’s 3 million-person workforce. OpenAI retired GPT-5.1 and migrated users to GPT-5.3 and GPT-5.4. Anthropic launched a $100 million partner network targeting enterprises.
The AI race is accelerating, not pausing. Meta’s falling behind in a market that punishes second place.
What This Means for Developers
If you’re building on Meta’s AI platforms, the Avocado delay raises practical questions. Can you count on Meta to deliver competitive foundation models? Should you hedge with multi-platform strategies? If Meta’s licensing Gemini internally, should you just use Gemini directly?
The broader question is strategic: Can capital spending alone win the AI race, or does execution matter more? Meta’s $135 billion budget hasn’t produced a model that beats Google’s. That’s a data point every developer evaluating AI platforms should consider.
The Avocado delay isn’t just about one model slipping two months. It’s a stress test of Meta’s entire AI strategy—and right now, the cracks are showing.
