Meta's AI Turnaround: Strategic Shifts, Delays, and the Open-Source Dilemma

Meta's AI division has undergone a seismic transformation in 2025, marked by a dramatic pivot from open-source to closed models, aggressive restructuring, and a renewed focus on competing with industry giants like Google and OpenAI. The question now is whether these strategic shifts can close the widening performance gap in AI capabilities. Drawing on recent benchmarks, internal reorganization, and market dynamics, this analysis evaluates Meta's prospects.

Strategic Shifts: From Open-Source to Proprietary Models

Meta's decision to abandon its open-source AI strategy in favor of closed models represents a fundamental reorientation. The company has restructured its AI division into Meta Superintelligence Labs (MSL), splitting it into four specialized units: AI research, superintelligence, product development, and infrastructure according to reports. This reorganization, led by CEO Mark Zuckerberg, aims to accelerate innovation and prioritize commercialization over open collaboration. The flagship project, codenamed Avocado, is expected to replace the open-source Llama series and will be accessible only via APIs, not public download according to internal sources.

This shift is driven by both competitive and financial pressures. Open-source models like Llama 4 faced "competitive leakage," with rivals such as Google and Chinese AI labs leveraging Meta's architecture to develop their own models according to industry analysis. Additionally, the open-source approach failed to generate significant revenue, a stark contrast to OpenAI's API-driven business model. By developing closed models, Meta aims to monetize its AI capabilities directly, mirroring the strategies of its competitors according to market observers.

Performance Benchmarks: Can Avocado Close the Gap?

The performance of Meta's current models, such as Llama 3, and its upcoming Avocado, must be evaluated against Google's Gemini 3 and OpenAI's GPT-5.2. In Q3 2025, Gemini 3 Pro outperformed GPT-5.2 in multimodal tasks, achieving 81.0% on the MMMU-Pro benchmark and 37.5% on the Humanity's Last Exam according to Vellum AI benchmarks. Meanwhile, GPT-5.2 demonstrated superior coding and professional knowledge work, scoring 80% on SWE-Bench Verified and 70.9% on GDPval according to DataCamp analysis.

Meta's Llama 3, though competitive in general knowledge (e.g., outperforming Gemini Pro in MMLU), lags in specialized domains. For instance, Llama 4 Maverick achieved 95.2% accuracy on the AIME 2025 math benchmark without tools but trails behind GPT-5.2's 100% score according to documentation. Similarly, Gemini 3 Flash's 81.2% on MMMU-Pro outperforms Llama 3's 79.5% according to Engadget reporting. These gaps highlight the challenges Meta faces in catching up with closed-source leaders.

Resource Allocation and Delays: A Double-Edged Sword

Meta's pivot to AI has come at the expense of its metaverse ambitions. The company has cut Reality Labs' budget by 30% and redirected $70 billion toward AI infrastructure, advertising AI, and wearable technologies according to industry reports. This reallocation reflects a recognition that AI, not the metaverse, will drive future growth. However, delays in Avocado's release-pushed from late 2025 to early 2026-have raised concerns about Meta's ability to execute under pressure according to CNBC reporting.

The company's $14.3 billion investment in Scale AI and the recruitment of its founder, Alexandr Wang, as chief AI officer signal a commitment to talent and infrastructure according to The Prompt Buddy. Yet, internal tensions and high-profile departures underscore the challenges of scaling a closed-source model in a competitive landscape dominated by OpenAI and Google.

The Open-Source Dilemma: Innovation vs. Monetization

Meta's shift to closed models has sparked debate about the long-term implications. Proponents argue that proprietary models enable tighter integration with Meta's ecosystem (e.g., Facebook, Instagram) and provide a revenue stream through APIs according to financial analysts. Critics, however, warn that this move could alienate the open-source community, which has been instrumental in advancing AI research.

The open-source strategy, while less profitable, allowed Meta to establish a broad developer base and influence industry standards. By contrast, closed models risk creating a dependency on Meta's platforms, limiting third-party innovation. This trade-off between monetization and ecosystem growth will be critical in determining Meta's success.

Conclusion: A High-Stakes Gamble

Meta's AI turnaround is a high-stakes gamble. While the company's restructuring and investment in Avocado demonstrate a clear intent to compete, the performance gaps with Gemini 3 and GPT-5.2 remain significant. The closed-source strategy may help Meta monetize its AI capabilities but could also stifle the collaborative innovation that once defined its approach.

For investors, the key risks lie in execution delays, legal challenges, and the ability to differentiate Avocado in a crowded market. If Meta can leverage its social media data and infrastructure to create a model tailored to its ecosystem, it may carve out a niche. However, catching up with Google and OpenAI in general-purpose AI remains an uphill battle. The coming months will reveal whether this strategic overhaul can deliver the promised returns-or if Meta's AI ambitions will remain unfulfilled.