Meta Developed Secret "Avocado" Model — What It Reveals About the Company's Strategy

In the race for AI leadership, the same names always appear in headlines: OpenAI, Anthropic, Google, Microsoft, NVIDIA, Amazon. But behind every publicly announced model lie dozens of unfinished projects, internal prototypes and developments that never reach a wide audience. One such project turned out to be "Avocado" — an undocumented internal Meta model whose history opens a window into the real AI development kitchen and provides surprisingly much food for thought.

Despite Meta's position in public perception being somewhat behind the leaders of the race — OpenAI and Google — the company consistently builds strong AI positioning through the family of open LLaMA models. Starting with the first release in 2023, the company has progressed to LLaMA 3 and later versions, positioning itself as an alternative to the closed ecosystems of GPT and Claude. This strategy works: developers worldwide use LLaMA as a foundation for their own products, and Meta strengthens its reputation as a player willing to share technology with the community.

The public side of this story looks convincing and consistent. However, behind it exists another, far less visible reality. "Avocado" is a model that became known from internal sources.

The project was developed in parallel with the main LLaMA line, but for some reason never made it to a public release. Perhaps it failed to meet internal quality thresholds. Perhaps it didn't fit strategic priorities or fell short of competitors on key metrics.

The exact details are unknown, but the very fact of the project's existence raises a logical question: by what criteria do large tech companies decide what to release to market and what to leave in the lab? Technical specifications are only one factor. The competitive environment plays no less of a role: if a model doesn't surpass what's already available on the market, the value of a release is lost — especially for a company positioning itself as an open AI leader.

Security considerations are equally critical: even a small vulnerability in model behavior can cause reputational damage incomparable to the benefits of publication. Finally, operational readiness: every public release requires full support infrastructure — documentation, APIs, monitoring systems, and a response team. "Avocado," it appears, failed one or more of these filters.

But this doesn't make the project a failure. Real progress in AI is not a linear process of releasing models one after another. Behind every successful LLM stand several "avocados" — prototypes that helped engineering teams find the boundaries of the possible, refine architectural decisions, and understand what actually works in practice.

Unfinished projects concentrate a significant portion of real knowledge: they form the foundation on which the next generation of models is built. The AI agent race that tech giants are currently waging has made internal developments a particularly sensitive topic. Any leak about an unfinished project immediately becomes the focus of media, analysts, and competitors.

The "Avocado" story is neither scandal nor sensation. It's a rare window into the reality of AI development, where decisions about release are made considering dozens of parameters, not just benchmarks in tables. For product teams and startups observing this race, there's a practical takeaway: not every working project needs to become a public product.

The ability to stop a project at the right time is just as strategically important as the ability to launch it. Companies that consistently win in AI know not only how to create new models, but also how to make well-considered decisions about which ones deserve to see the light of day.