Meta

Meta has transformed from a social media company into one of the world’s most influential AI research organisations. With 1 billion monthly active users on its Meta AI assistant and the most widely-deployed open-source language models in history, Meta’s AI division now stands at a pivotal juncture—recent reports suggest the company may be abandoning its open-source-first philosophy in favour of proprietary models that can generate direct revenue.

This guide documents Meta’s complete AI journey: FAIR’s founding, PyTorch’s dominance, the Llama revolution, the billion-user assistant, Yann LeCun’s legendary tenure and departure, and the strategic uncertainty now facing the open-source champion.

Quick facts

The origins: FAIR and the open research mission (2013-2022)

December 2013: Yann LeCun builds FAIR

Fundamental AI Research (FAIR) was founded in December 2013 when Mark Zuckerberg recruited Yann LeCun, the pioneering computer scientist who helped invent convolutional neural networks, from NYU to lead the effort. LeCun, a 2018 Turing Award winner alongside Geoffrey Hinton and Yoshua Bengio, brought academic credibility and a commitment to open research.

The lab launched with offices in New York City and Menlo Park, expanding to Paris in 2015 and Montreal in 2017. FAIR’s mission was deliberately open: “advancing the state of the art in artificial intelligence through open research for the benefit of all.”

PyTorch changes everything (2017)

FAIR’s most consequential contribution before the generative AI era was PyTorch, released in January 2017. The deep learning framework prioritised ease of use and dynamic computation graphs over TensorFlow’s static approach. Within years, PyTorch became the dominant framework for AI research—now underpinning everything from Tesla’s Autopilot to thousands of AI applications worldwide.

By 2023, PyTorch powered over 60% of AI research papers at major conferences, cementing Meta’s foundational role in modern AI infrastructure.

The Facebook to Meta rebrand (October 2021)

The October 2021 rebrand from Facebook to Meta brought FAIR under the “Meta AI” umbrella, though the fundamental research mission initially remained intact. Zuckerberg’s metaverse pivot positioned AI as essential infrastructure—from avatar generation to real-time translation to computer vision for AR/VR devices.

Pre-Llama research achievements

Before Llama, FAIR built an impressive research portfolio:

2016: DeepFace achieved 97.35% accuracy on facial recognition, approaching human performance.

2019: RoBERTa optimised BERT training, becoming a standard NLP baseline.

2020: DETR introduced transformer-based object detection, influencing computer vision architectures.

2022: Make-A-Video demonstrated text-to-video generation before Sora existed.

The Llama revolution (2023-present)

February 2023: Llama 1 leaks and changes everything

On February 24, 2023, Meta released LLaMA (Large Language Model Meta AI) as a research artifact intended only for academics. The models ranged from 7B to 65B parameters, trained on 1.4 trillion tokens from publicly available sources. Within a week, the weights leaked on 4chan, spreading across torrents and Hugging Face.

The leak proved transformative. Suddenly, anyone could run a GPT-3-class model locally. The open-source AI movement—previously limited to smaller models—had a foundation competitive with proprietary systems. Derivative projects like Alpaca, Vicuna, and WizardLM emerged within weeks.

July 2023: Llama 2 goes commercial

Llama 2 launched July 18, 2023 with a crucial change: a permissive commercial licence. Models at 7B, 13B, and 70B parameters offered 4K context windows and RLHF-tuned chat variants. Microsoft partnered as preferred cloud provider, offering Llama 2 through Azure.

The Llama 2 Community Licence permitted commercial use, derivatives, and fine-tuning with key restrictions: companies exceeding 700 million monthly active users must request a separate licence (targeting competitors like TikTok and WeChat), and using Llama to train competing LLMs is prohibited.

April 2024: Llama 3 reaches the frontier

Llama 3 arrived April 18, 2024 with dramatically improved capabilities. The 8B and 70B models were trained on 15 trillion tokens—over 7x Llama 2’s training data. Llama 3 70B ranked in the top 5 on LMSys Chatbot Arena, competing with GPT-4 and Claude 3 on human preference rankings.

July 2024: The 405B giant arrives

Llama 3.1 launched July 23, 2024 with three sizes: 8B, 70B, and the massive 405B—the first open-weight model to genuinely rival GPT-4. With 128K context windows and multilingual support across eight languages, Llama 3.1 405B achieved 88.6% on MMLU and 89.0% on HumanEval.

In a blog post titled “Open Source AI Is the Path Forward”, Zuckerberg argued open-source would become the industry standard: “I believe the Llama model could become the most popular foundation model, just like Linux became the foundation for most computing today.”

September 2024: Llama goes multimodal

Llama 3.2 released September 25, 2024 introduced Meta’s first vision-capable Llama models. The 11B and 90B multimodal variants could process images alongside text, while tiny 1B and 3B models enabled on-device deployment for mobile and edge computing.

December 2024: Efficiency breakthrough with Llama 3.3

Llama 3.3 70B launched December 6, 2024 as a remarkable efficiency achievement—matching or exceeding the much larger Llama 3.1 405B across most benchmarks while requiring a fraction of the compute.

April 2025: Llama 4 and the MoE architecture

Llama 4 launched April 5, 2025 at LlamaCon with Meta’s first Mixture-of-Experts (MoE) architecture:

Llama 4 Scout (109B total parameters, 17B active): Industry-leading 10 million token context window, fits on a single H100 GPU with quantisation.

Llama 4 Maverick (400B total, 17B active): 1 million token context, natively multimodal with early fusion of text and vision, capable of processing up to 48 images per prompt.

Both models were trained on over 30 trillion tokens with native multimodality—a significant architectural shift from bolting vision onto text-only models.

The complete Llama timeline

Meta AI assistant: one billion users for free

September 2023: The assistant launches

The consumer-facing Meta AI assistant launched in beta at Meta Connect on September 27, 2023. Initially featuring celebrity AI personas (voiced by Snoop Dogg, Kendall Jenner, and others), the service evolved into a general-purpose assistant powered by Llama.

April 2024: Rollout across Meta’s platforms

On April 18, 2024, Meta AI rolled out widely across WhatsApp, Instagram, Facebook, and Messenger. The integration embedded AI into search boxes across Meta’s 4-billion-user ecosystem—users could simply type “@Meta AI” in any chat to invoke the assistant.

April 2025: Standalone app and one billion users

A standalone Meta AI app launched at LlamaCon on April 29, 2025, powered by Llama 4. Mark Zuckerberg announced 1 billion monthly active users at the May 2025 shareholder meeting—doubling from 500 million in September 2024.

Current capabilities

Text and conversation: General Q&A, writing assistance, coding help, powered by Llama 4 with real-time web search via Bing/Google integration.

Image generation: Imagine with Meta AI produces four 1280×1280 images per prompt using Meta’s Emu model, with an “Imagine me” feature for personalised image creation.

Voice interaction: Available across apps and on Ray-Ban Meta smart glasses, with Live AI enabling real-time video context and hands-free queries.

Integrations: Ray-Ban Meta glasses offer multimodal vision (asking about what you see), real-time translation, and continuous conversational context.

Pricing: Remarkably free

Unlike competitors charging $20/month for premium access (ChatGPT Plus, Claude Pro, Gemini Advanced), Meta AI remains completely free with no subscription tier. Zuckerberg confirmed plans for a paid subscription service—likely offering additional computing power, faster inference, and priority access—but full monetisation is not planned until 2026.

Meta’s strategy mirrors Android: sacrifice direct monetisation for ecosystem control and competitive pressure on OpenAI and Anthropic.

The closed-source pivot: Avocado and Behemoth

December 2025: Reports of strategic shift

The most significant recent development is Meta’s apparent strategic shift. According to Bloomberg and CNBC reports from December 9-10, 2025, Meta is developing a closed-source model codenamed “Avocado” expected in Q1 2026. This would mark Meta’s first major proprietary AI model and represents a fundamental departure from the open-source strategy that defined Llama’s success.

Behemoth shelved

The Behemoth model—a 2 trillion parameter giant previewed alongside Llama 4 in April 2025—appears to have been shelved. After completing training, Meta reportedly delayed release due to “disappointing internal performance tests.” Senior members of Meta Superintelligence Labs have discussed abandoning Behemoth entirely in favour of closed alternatives.

Why the pivot?

Several factors are driving internal reconsideration:

Competitive cloning: DeepSeek successfully replicated Llama architecture at a fraction of the cost, undermining Meta’s competitive moat.

Monetisation pressure: Meta has invested over $65 billion in AI infrastructure with limited direct revenue to show for it.

Behemoth’s failure: The 2T parameter model underperformed expectations despite massive compute investment.

Enterprise demand: Businesses increasingly want supported, proprietary solutions rather than self-hosted open models.

Meta’s official position remains that it will “continue releasing leading open source models” while training “a mix of open and closed models going forward.”

Llama API launches

Llama API, launched at LlamaCon in April 2025, represents Meta’s first direct inference service competing with OpenAI and Anthropic. Partnerships with Cerebras (2,600 tokens/second—18x faster than traditional GPU inference) and Groq enable ultra-fast inference.

Leadership and the 2025 restructuring

The old guard departs

November 19, 2025: Yann LeCun announced he would leave Meta to found his own startup focused on “world models” and human-level AI—ending a 12-year tenure that shaped modern AI research.

August 2025: Joëlle Pineau, former FAIR co-lead, joined Cohere as Chief AI Officer.

Meta Superintelligence Labs (June 2025)

In June 2025, Meta created Meta Superintelligence Labs (MSL) as a new elite AI division reporting directly to Zuckerberg. The company invested $14.3 billion for a 49% stake in Scale AI and appointed its 28-year-old founder, Alexandr Wang, as Meta’s first Chief AI Officer.

An October 2025 restructuring cut approximately 600 positions from AI teams, while TBD Lab—the elite unit developing next-generation models—remained protected.

Current leadership structure

The AI division now operates under Meta Superintelligence Labs with four teams:

• TBD Lab (Wang): Elite frontier model development, including “Avocado”
• FAIR (Fergus): Long-term fundamental research
• AI Products (Hayes): Meta AI assistant, AI Studio, consumer features
• MSL Infrastructure (Ramani): Compute and data centre operations

Developer access: the most versatile foundation model ecosystem

Unlike OpenAI or Anthropic, Meta does not operate a primary API for Llama inference. Instead, developers access Llama through multiple channels:

Cloud providers

Self-hosting options

Llama’s open weights enable complete self-hosting:

vLLM: Production-grade serving with continuous batching and PagedAttention optimisation.

llama.cpp: Lightweight C++ implementation for CPU and edge devices, enabling laptop deployment.

Ollama: User-friendly wrapper for local deployment with one-command installation.

Hardware requirements

Fine-tuning

Fine-tuning is widely supported through cloud providers. Unsloth has emerged as the most resource-efficient approach—enabling Llama 3.1 8B QLoRA fine-tuning in just 8GB VRAM.

Cost advantage

Llama inference costs roughly 11x less than GPT-4o through Together AI while delivering comparable performance on many benchmarks. Self-hosting eliminates per-token costs entirely for high-volume deployments.

Research contributions beyond Llama

Segment Anything Model (SAM)

SAM launched April 5, 2023 as a “GPT moment for computer vision”—the first promptable segmentation foundation model, trained on 1.1 billion masks across 11 million images.

SAM 2 (July 2024) extended this to real-time video segmentation at 6x faster inference. SAM 3 (November 2025) enables detecting, segmenting, and tracking all instances of any visual concept from text or image prompts.

ImageBind (May 2023)

ImageBind created the first AI model combining six modalities—text, images, audio, depth, thermal, and IMU motion data—in a single embedding space, enabling cross-modal generation without paired training data.

SeamlessM4T

SeamlessM4T, published in Nature in January 2025, represents the most capable multilingual translation system: speech-to-speech translation across 101 input and 36 output languages, 23% more accurate than previous state-of-the-art.

AudioCraft / MusicGen (August 2023)

AudioCraft delivered text-to-music generation with MusicGen, trained on 400,000 recordings. Unlike competitors, Meta open-sourced the models and training code.

Impact on open-source AI

The Llama effect on the ecosystem has been profound: over 20,000 derivative Llama models exist on Hugging Face, and GitHub repositories mentioning LLaMA increased 15x since the original release.

Competitive positioning

Enterprise market share

Meta occupies a unique position—dominant in open-source model distribution but trailing in enterprise API revenue. According to Menlo Ventures (July 2025):

Only 13% of enterprise daily workloads use open-source models as of mid-2025, down from 19% earlier that year. Businesses cite support, reliability, and compliance concerns.

Consumer market

Meta AI’s 1 billion MAU compares favourably to ChatGPT’s 462 million users. The difference is distribution: Meta AI is embedded in platforms with 4 billion monthly users, while ChatGPT requires deliberate usage.

The strategic trade-off

Meta’s open-source strategy mirrors Android: sacrifice direct monetisation for ecosystem control. By open-sourcing models, Meta:

• Creates cost pressure on OpenAI and Anthropic
• Attracts developer mindshare and community contributions
• Ensures independence from competitors’ ecosystems
• Establishes Llama as default infrastructure

As Zuckerberg explained: “Selling access to AI models isn’t our business model”—unlike OpenAI and Anthropic, open-sourcing doesn’t undercut Meta’s core advertising revenue. The question is whether this logic holds as AI capabilities approach AGI.

Controversies and challenges

Copyright litigation

The Kadrey v. Meta lawsuit presents significant legal risk. Plaintiffs including Sarah Silverman allege that Mark Zuckerberg personally approved using LibGen—a known pirated book database—for Llama training. Internal documents reportedly show employees described it as “a data set we know to be pirated.”

In March 2025, a judge found “reasonable inference” that Meta removed copyright management information to hide infringement. The DMCA claim survived dismissal, potentially setting precedent for AI training on copyrighted materials.

Content moderation changes

In January 2025, Meta replaced fact-checkers with a Community Notes system and loosened hate speech policies, drawing criticism from researchers and regulatory scrutiny. The EU opened Digital Services Act proceedings.

AI safety debates

Yann LeCun’s public dismissal of existential AI risks as “preposterous” and his clashes with Geoffrey Hinton and others warning about AI dangers generated controversy. Some in the AI safety community accused Meta of prioritising competitive advantage over caution, while others praised LeCun’s advocacy for AI openness and accessibility.

The “open source” debate

Critics argue the Llama licence makes models “source-available” rather than truly open source due to the 700M MAU restriction and prohibition on training competing models. The Open Source Initiative has not certified any Llama licence as meeting open-source definition standards.

Where Meta AI excels

Open-source leadership: No other major AI lab releases frontier-class models with open weights. Llama enables research, deployment, and customisation impossible with closed APIs.

Cost efficiency: Self-hosted Llama eliminates per-token costs entirely. Even via providers, Llama inference costs a fraction of GPT-4o or Claude.

Distribution scale: 1 billion MAU on Meta AI, integrated across WhatsApp, Instagram, Facebook, and Messenger—platforms with 4 billion combined users.

Research contributions: PyTorch powers the field; SAM transformed computer vision; SeamlessM4T leads translation. FAIR’s publication record rivals any academic institution.

Hardware flexibility: Llama runs on everything from data centre H100s to laptops via llama.cpp to mobile devices with Llama 3.2 1B.

Where Meta AI falls short

Enterprise support: Unlike OpenAI or Anthropic, Meta doesn’t offer enterprise SLAs, dedicated support, or compliance certifications for Llama deployments.

Benchmark leadership: Llama 3.3 70B trails Claude Opus 4.5 on coding (SWE-bench) and GPT-5.1 on general capability. Meta’s models are “competitive” rather than “leading.”

API maturity: Llama API launched only in April 2025. The fragmented provider ecosystem creates inconsistent experiences compared to unified OpenAI/Anthropic platforms.

Strategic uncertainty: The potential closed-source pivot, leadership departures, and Behemoth’s failure create questions about Meta’s AI direction.

Consumer engagement: Despite 1B MAU, Meta AI lacks ChatGPT’s mindshare and engagement depth. Many users interact passively through platform integration rather than deliberate AI usage.

Developer resources

Official documentation

• Llama Documentation — Complete deployment and usage guides
• Meta AI Research — Papers and model cards
• Llama GitHub — Official code repositories
• Llama Recipes — Fine-tuning and deployment examples

Model access

• Hugging Face — Official model weights
• Llama Downloads — Direct download after licence acceptance
• Together AI — Managed inference API
• AWS Bedrock — Enterprise deployment

Community resources

• r/LocalLLaMA — Active community for self-hosting
• llama.cpp — CPU inference implementation
• Ollama — Simple local deployment
• Unsloth — Efficient fine-tuning

FAQ

Is Meta AI free?

Yes. The Meta AI assistant is completely free with no subscription tier, accessible via meta.ai, WhatsApp, Instagram, Facebook, Messenger, and the standalone app. Zuckerberg has mentioned plans for a paid tier in 2026, but no pricing has been announced.

How do I access Llama models?

Llama models are available through multiple channels: direct download from llama.com after accepting the licence, Hugging Face for model weights, cloud providers (Together AI, AWS Bedrock, Azure) for managed inference, or self-hosting via vLLM, llama.cpp, or Ollama.

Is Llama truly open source?

Llama uses a custom “Community Licence” that permits commercial use and derivatives but includes restrictions: companies with 700M+ MAU need separate licences, and using Llama to train competing LLMs is prohibited. Purists argue this makes Llama “source-available” rather than open source by OSI definitions.

What happened to Yann LeCun?

LeCun announced on November 19, 2025 that he would leave Meta after 12 years to found his own startup focused on “world models” and human-level AI. His departure, alongside other senior researchers, marks a significant transition for Meta’s AI organisation.

Is Meta building closed-source models?

Reports from December 2025 indicate Meta is developing a closed-source model codenamed “Avocado” for potential Q1 2026 release. Meta has stated it will continue open-source releases while developing “a mix of open and closed models.”

Which Llama model should I use?

• Llama 3.2 1B/3B — Mobile and edge devices, resource-constrained environments
• Llama 3.3 70B — Best balance of capability and efficiency for most use cases
• Llama 4 Scout — When you need massive context (10M tokens) or strong multimodal
• Llama 4 Maverick — Maximum capability for complex tasks

How does Llama compare to GPT-5 and Claude?

Llama 3.3 70B is competitive with GPT-4o on most benchmarks but trails GPT-5.1 and Claude Opus 4.5 on frontier capabilities. The advantage is cost: Llama inference is 5-10x cheaper through providers and free for self-hosting.

Official links

Historical timeline