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Meta's Avocado AI Push Sparks Internal Confusion

Meta's race to develop its next-gen AI, codenamed Avocado, is sparking internal friction under new leadership. What does this mean for its future?

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the tea spiller โ˜•
Tuesday, December 9, 2025 ๐Ÿ“– 2 min read
Meta's Avocado AI Push Sparks Internal Confusion
Image: CNBC

Whatโ€™s Happening Meta is currently in an intense sprint to develop its next-generation artificial intelligence model, internally codenamed โ€˜Avocado.โ€™ This ambitious project is reportedly being spearheaded under new AI leadership, signaling a fresh strategic push. However, this aggressive timeline and leadership transition are reportedly causing significant internal friction and confusion within the company. Meta finds itself racing hard against formidable rivals like OpenAI and Google, adding immense pressure to the development team. ## Why This Matters Internal friction and confusion within a tech giant like Meta can have serious ripple effects across its entire AI division. Such disarray might slow down the development process for Avocado, potentially pushing back its critical launch date. In the fiercely competitive AI landscape, any delay or misstep could be incredibly costly. Meta needs a unified front to innovate rapidly, especially as it battles to stay ahead of, or even catch up to, its cutting-edge competitors. - Strategic Disadvantage: Delays or internal disagreements could put Meta at a significant disadvantage against rapidly advancing rivals like OpenAI and Google.

  • Talent Drain Risk: A confused or high-friction environment often leads to top AI engineers seeking clearer, more stable pastures elsewhere.
  • Product Cohesion: Internal friction can fracture the vision for โ€˜Avocado,โ€™ potentially resulting in a less unified or effective final product. ## The Bottom Line Metaโ€™s โ€˜Avocadoโ€™ represents a crucial frontier in its AI ambitions, but reports of internal friction suggest a bumpy road ahead. As the company pushes hard against rivals, can Meta effectively navigate its internal challenges to deliver a truly notable AI model?
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Originally reported by CNBC

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