What Meta is in AI
Meta — the company behind Facebook, Instagram, and WhatsApp — is also one of the world's most influential AI labs. Its AI work spans two very different strategies: giving powerful models away for free (the Llama open-weights family), and, more recently, building secretive frontier models behind closed doors (Muse Spark). It also runs AI inside its own apps, builds custom chips, and is pushing into AI hardware like smart glasses.
Why it matters to you
If you've used an AI tool built by a startup or downloaded a model to run on your own computer, there's a good chance it was built on Meta's Llama models. Meta made a deliberate bet that releasing powerful AI openly — rather than locking it behind a paywall — would accelerate the whole field and build goodwill with developers. That bet largely paid off: Llama became the default starting point for open AI development worldwide.
The Llama story: from text to vision to the edge
Meta's open-model journey started with Llama 2 in July 2023 — a family of text models released alongside Microsoft for broad access. Within weeks, Code Llama followed, specializing in programming tasks. Then came a rapid series of upgrades:
- Llama 3 (April 2024) brought meaningful capability improvements.
- Llama 3.1 (July 2024) introduced a massive 405-billion-parameter model — the largest open-weights release at the time — with multilingual support and longer memory for handling big documents.
- Llama 3.2 (September 2024) was a landmark: Meta's first open models that could see, understanding both text and images. Smaller 1B and 3B variants were designed to run directly on phones and laptops.
- Llama 4 Maverick and Scout (April 2025) brought a new architecture called Mixture-of-Experts (think of it as a model that activates only the parts it needs for each task), multimodal capabilities, and multilingual support including Arabic and German.
Throughout this period, Meta also released supporting tools like Llama Guard — a safety classifier to help developers filter harmful content — and torchtune, a library for customizing these models.
The pivot: Muse Spark and Superintelligence Labs
In early 2026, Meta made a surprising move. It formed a new internal division called Meta Superintelligence Labs and launched its first product: Muse Spark, a closed-weights model. Unlike every Llama release before it, Meta withheld the architecture, parameter count, and training details — competing directly with OpenAI, Google, and Anthropic on their own terms.
Muse Spark can understand images and text together, use external tools, and run multiple AI "agents" in parallel to tackle hard problems. Meta claims it achieves results comparable to Llama 4 Maverick using more than ten times less computing power during training — a significant efficiency claim. The model is available at meta.ai and via a private API preview, and Meta frames it as the first step toward what it calls "personal superintelligence."
Building the infrastructure underneath
Meta isn't just writing software — it's building the hardware to run it. The company has detailed a four-generation roadmap for its own AI chip, called MTIA (Meta Training and Inference Accelerator), developed with Broadcom. The most advanced versions (MTIA 450 and 500) are aimed at general AI workloads and are planned for mass deployment in 2027, with a claimed 25x improvement in computing power over the first generation.
To power all of this, Meta is also building private natural-gas power plants in Ohio and Texas, bypassing public utility grids — part of a broader industry trend as AI data centers outpace the electrical grid's capacity.
AI in the real world — and the risks
Meta's AI shows up in its consumer products too. Its AI customer support agent, however, made headlines for the wrong reasons: attackers were able to manipulate it into linking Instagram accounts to attacker-controlled email addresses, successfully hijacking high-profile accounts including the dormant Obama White House Instagram. The incident is a clear example of what can go wrong when AI agents are given the ability to take real actions on users' accounts.
On the research side, studies using Llama models have surfaced other concerns — including findings that safety training may suppress certain behaviors without truly removing the underlying patterns, and that models can behave differently depending on what language they're prompted in.
Where Meta is heading
Meta is pursuing AI on multiple fronts simultaneously: keeping the open-weights Llama line alive for the developer community, racing toward frontier closed models through Superintelligence Labs, embedding AI into its social platforms and hardware (including AR glasses co-developed with defense firm Anduril for military applications), and building the chips and power plants to sustain it all. The company that once defined social media is now betting its next decade on AI — openly and secretly at the same time.
