In December, media reports first surfaced that Meta Platforms (NASDAQ:META | META Price Prediction) was quietly developing a next-generation AI model code-named Avocado, positioned as the successor to its popular Llama series of large language models. The model had been internally targeted for a first-quarter launch, meaning this month.
Yesterday, though, The New York Times reported that Meta has pushed the rollout back to at least May — and possibly June — after internal tests showed Avocado lagging behind rivals such as Google’s Gemini 3.0 in critical areas like reasoning, coding, and writing. While the new model still outperformed Meta’s prior offerings, the gap with frontier competitors proved too wide for comfort.
The delay immediately sparked debate over whether this setback calls into question the wisdom of Meta’s enormous spending in AI infrastructure.
Shifting from Open to Closed
Avocado marks a clear departure from Meta’s long-standing philosophy. Previous Llama models were open-source, freely available for developers worldwide to download, fine-tune, and deploy. That approach fueled rapid community adoption and positioned Meta as a democratizing force in AI. Avocado, however, is being developed as a proprietary, closed-source system — much like those from OpenAI, Google, and Anthropic.
The advantages of going closed are straightforward and compelling. Proprietary models allow higher profit margins because Meta can control distribution, charge for premium access, or integrate the technology exclusively into its own products across Facebook, Instagram, and WhatsApp. A wider economic moat also emerges: rivals cannot simply fork the code and leapfrog Meta’s progress.
In an industry where every incremental edge matters, owning the intellectual property outright can translate into sustained competitive advantage and recurring revenue streams that open-source releases simply cannot match.
Caught in the AI Arms Race
Yet Meta enters this new chapter from behind. Llama 4 disappointed last year, and the rapid pace of innovation elsewhere has only widened the gap. Google’s Gemini models have advanced dramatically in just months, while Anthropic’s Claude continues to set benchmarks in safety and reasoning.
Even if Avocado launches in May or June after further polishing, analysts worry it could still trail the latest versions of Gemini or Claude by the time it reaches users. The company has reportedly considered temporarily licensing Gemini to power its AI features in the interim, underscoring the urgency.
The ramifications of delay are real but perhaps less catastrophic than critics suggest. In the short term, Meta risks ceding mindshare and momentum to faster-moving rivals. Advertisers and developers may hesitate if Meta’s AI tools appear second-rate. Share-price volatility is already evident whenever capex headlines surface. However, rushing a subpar model to market could inflict deeper damage — eroding user trust and inviting unfavorable comparisons that linger far longer than a few months of waiting.
History shows that in AI, “good enough” rarely stays good enough; competitors iterate relentlessly. A polished Avocado, even if late, stands a better chance of delivering meaningful differentiation than a rushed release that merely checks a box.
Key Takeaway
Meta has undeniably come far in AI. From pioneering open-source Llama models that powered countless startups to now building proprietary frontier systems, the company has transformed itself from follower to serious contender. Still, investors remain uneasy about the scale of spending: Meta guided capital expenditures for 2026 at $115 billion to $135 billion, with the bulk earmarked for AI data centers and chips. Those eye-watering figures conjure uncomfortable memories of the metaverse push, where tens of billions yielded little tangible return and significant losses.
Ultimately, the delay appears worth it. Accepting “good enough” and shipping Avocado on the original timeline might have satisfied quarterly optics but would have left Meta vulnerable in a winner-take-most market. By taking extra time to close the performance gap, Meta prioritizes quality and long-term relevance over speed.
Continued innovation — rather than premature celebration — positions the company to protect its massive investment and potentially surprise skeptics when the model finally arrives. In the unforgiving arena of frontier AI, late can indeed prove better than never.
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