Meta's AI strategy transformation is spearheaded by Alexandr Wang, who, drawing on his background as the founder of Scale AI, is driving deep integration within the company's super-intelligence lab. This move underscores Meta's heavy reliance on large language models and reflects a broader industry shift from single-function AI toward general-purpose intelligence, with surging demand for computing power set to drive infrastructure upgrades. The 2026 product lineup includes the Mango multimodal model, designed for unified image and video generation and understanding, as well as the Avocado large language model, which aims to achieve a generational leap in coding capabilities and explore "world models"—building a deep understanding of the physical environment through visual cognition.
Talent acquisition has become Meta’s core tactic, with Mark Zuckerberg personally luring over 20 core researchers from OpenAI to bolster the R&D team’s technical strength. This move stems from the white-hot competition in the multimodal AI market: Google, leveraging its Gemini 2.5 Flash Image technology, has seen its Nano Banana tool surpass 650 million users, highlighting the explosive potential of visual generation tools. Meanwhile, OpenAI maintains its technological moat through Sora and the upgraded ChatGPT Images, forcing Meta to accelerate its innovation pace. Industry analysts note that Meta’s earlier partnership with Midjourney on the Vibes video generator already demonstrated its video-generation capabilities, and the 2026 series is seen as a critical milestone toward realizing the vision of "personal super intelligence"—an AI that seamlessly integrates into individual lives.
As the complexity of large models continues to climb, the supporting role of high-performance chips and infrastructure becomes increasingly critical. Meta’s full-scale counteroffensive is not just about technological breakthroughs but also about building a sustainable ecosystem. Strategy experts believe that with the "Tropical" model series, Meta is trying to seize the lead in the computing power race, pushing the entire AI industry toward greater efficiency and intelligence while setting new standards for future infrastructure upgrades.