Meta AI is Meta Platforms' AI division, headquartered in Menlo Park, California since its 2013 founding as FAIR. It builds two model tracks: the open-weight Llama family that developers self-host anywhere, and the newer closed Muse Spark models from Meta Superintelligence Labs, reachable through Facebook, Instagram, WhatsApp and Ray-Ban smart glasses. It targets both open-source developers and enterprise buyers at once.
Meta AI, founded in April 2013 as Facebook AI Research (FAIR), is Meta Platforms' AI division and maker of the open-weight Llama family (1 billion-plus downloads) and Muse Spark, its first proprietary model, released April 8, 2026 with a 262,000-token context window. Muse Spark 1.1 followed July 9, 2026 with a 1-million-token context window and native agentic coding support.
Founded: 2013 · HQ: Menlo Park, California, USA · Team: 70,000+ companywide (Meta Platforms, post-May 2026 layoffs); Meta AI/MSL headcount not separately disclosed · CEO: Mark Zuckerberg (Chairman & CEO, Meta Platforms); Alexandr Wang (Chief AI Officer, Meta Superintelligence Labs) · Funding: Public (NASDAQ: META); $14.3B for 49% non-voting stake in Scale AI (Jun 2025); 2026 AI capex guidance raised to $125B-$145B (up ~87% YoY from $72.2B in 2025) · Valuation: $1.4T+ (Meta Platforms market cap, mid-2026)
About Meta AI
Meta AI traces to April 2013, when it launched as Facebook Artificial Intelligence Research (FAIR) under Meta (then Facebook) CEO Mark Zuckerberg, with Yann LeCun recruited from NYU as founding director. Facebook, Inc. renamed itself Meta Platforms, Inc. in October 2021. FAIR was never a separate legal entity; it operates as an internal division of the Nasdaq-listed parent, headquartered at Meta's Menlo Park, California campus. Meta AI ships two model tracks. Llama is an open-weight family that began with Llama 1 in February 2023 and has passed 1 billion cumulative downloads across Llama 2, Llama 3 and Llama 4. Llama 4, released April 2025, used a mixture-of-experts architecture; a larger Behemoth variant was delayed indefinitely after researchers alleged Meta had gamed public benchmark leaderboards. The second track is Muse Spark, Meta's first fully closed proprietary model, built by the newly formed Meta Superintelligence Labs (MSL). Muse Spark launched April 8, 2026, natively multimodal with a 262,000-token context window, scoring 52 on the Artificial Analysis Intelligence Index, close behind Claude Opus 4.6's 53. On July 9, 2026, Meta released Muse Spark 1.1 and opened it to outside developers through a paid Meta Model API, with a 1-million-token context window and native agentic coding support, priced at $1.25 per million input tokens and $4.25 per million output tokens. Meta AI does not raise venture capital; it runs on Meta Platforms' operating cash flow. For 2026, Meta raised its capex guidance to $125 billion to $145 billion, roughly double the $72.2 billion spent in 2025, driven mostly by data centers and custom silicon. In June 2025, Meta committed $14.3 billion for a 49 percent stake in Scale AI, bringing in co-founder Alexandr Wang; a planned $2 billion Manus acquisition was blocked by Chinese regulators in April 2026. Meta AI generates no standalone revenue; results sit inside Meta Platforms, still over 98 percent advertising-driven. Q1 2026 revenue reached $56.31 billion, up 33 percent year over year. On July 1, 2026, Meta announced Meta Compute, a cloud business reselling excess AI capacity, with reported deals worth $1.25 billion per month with Anthropic and $920 million per month with Google. Mark Zuckerberg remains Meta Platforms' CEO. Alexandr Wang, Scale AI's 28-year-old co-founder, joined as Chief AI Officer in mid-2025 and co-leads MSL with Nat Friedman, former GitHub CEO, while Shengjia Zhao, formerly of OpenAI, is chief scientist. MSL splits into four groups: TBD Lab for large language models, FAIR for research, Products and Applied Research, and MSL Infra. Founding FAIR director Yann LeCun left Meta in November 2025 calling Wang "inexperienced," and started rival lab AMI Labs, which raised $1.03 billion by March 2026. Meta cut roughly 8,000 jobs in May 2026, about 10 percent of its workforce, while redirecting 7,000 employees into new AI units; headcount fell from 78,865 at the end of 2025 to an estimated 70,000. MSL states its mission as building personal superintelligence safely and making it available to everyone. Since April 8, 2026, frontier releases are governed by the Advanced AI Scaling Framework version 2, gating deployment against chemical/biological, cybersecurity, and loss-of-control risks, and introducing published Safety and Preparedness Reports. FAIR continues publishing open research in computer vision, NLP, speech and reasoning, and remains the origin of the PyTorch framework. Meta's edge over OpenAI, Google DeepMind and Anthropic is free distribution: Meta AI sits inside Facebook, Instagram, WhatsApp and Messenger, plus 7 million Ray-Ban AI glasses sold in 2025. Muse Spark trails Gemini 3.1 Pro Preview and GPT-5.4 (57 each), but its API pricing undercuts Anthropic's and OpenAI's comparable rates by roughly 75 percent. Under the EU AI Act, Llama and Muse Spark face GPAI transparency obligations ahead of the August 2026 deadline. Meta is funding two gigawatt-scale data center campuses, Hyperion in Louisiana and Prometheus in Ohio, part of a reported $600 billion infrastructure commitment through 2028, while a stock down 15-29 percent in 2026 tests investor patience.
Mission
Build personal superintelligence safely and make it available to everyone.
Products
- Muse Spark 1.1 (Model / Developer API): https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/
- Llama 4 (Open-weight model family): https://ai.meta.com/blog/llama4/
- Meta AI Assistant (Consumer product): https://ai.meta.com/
- Ray-Ban AI Glasses (Hardware): https://www.ray-ban.com/usa/meta-smart-glasses
- Meta Compute (Cloud / GPU infrastructure service): https://www.cnbc.com/2026/07/01/meta-stock-cloud-ai-compute.html
Meta AI Models on HokAI
Compliance
GDPR, CCPA
Links
Website · GitHub · Twitter · LinkedIn · Blog · Docs
Frequently Asked Questions
What is Meta AI and what do they build?
Meta AI is the artificial intelligence division of Meta Platforms, Inc. (Nasdaq: META), the company behind Facebook, Instagram, WhatsApp and Messenger. It traces back to April 2013, when the division launched as Facebook Artificial Intelligence Research (FAIR) under founding director Yann LeCun. Meta AI ships two model families: Llama, an open-weight lineup that has passed 1 billion cumulative downloads since Llama 1 launched in February 2023, and Muse Spark, its first fully closed proprietary model, released April 8, 2026 by the newly formed Meta Superintelligence Labs. Muse Spark is natively multimodal, handling text, image and voice with a 262,000-token context window, and scored 52 on the Artificial Analysis Intelligence Index, close to Claude Opus 4.6's 53. A follow-up release, Muse Spark 1.1, launched July 9, 2026 with a 1-million-token context window and native agentic and coding capabilities. Meta AI products reach consumers through the Meta AI assistant embedded in Facebook, Instagram, WhatsApp and Messenger, and through Ray-Ban AI smart glasses, which sold 7 million units in 2025. Overall, Meta AI positions itself as the largest open-weight AI player while pivoting toward closed frontier models to compete directly with OpenAI and Google DeepMind.
Who founded Meta AI and who is the CEO?
Meta AI began in April 2013 as FAIR, founded under Meta (then Facebook) CEO Mark Zuckerberg's direction, with Yann LeCun recruited from NYU as founding director; LeCun left Meta in November 2025 after 12 years to start rival lab AMI Labs, which had raised $1.03 billion at a $3.5 billion valuation by March 2026. Mark Zuckerberg remains Meta Platforms' CEO and chairman and retains ultimate authority over AI strategy. In June 2025, Meta invested $14.3 billion for a 49 percent non-voting stake in Scale AI and brought in Scale's then-28-year-old co-founder Alexandr Wang as Meta's first Chief AI Officer, tasked with rebuilding Meta's training stack after Llama 4's disappointing April 2025 launch. Wang co-leads the newly formed Meta Superintelligence Labs (MSL) alongside Nat Friedman, the former GitHub CEO, with Shengjia Zhao, formerly of OpenAI, serving as chief scientist and Aparna Ramani leading MSL's infrastructure group. LeCun has since publicly criticized Wang as young and inexperienced in AI research leadership, and predicted further departures from Meta's AI organization. MSL is structured into four groups: the TBD Lab for large language models (led by Wang), FAIR for fundamental research, a Products and Applied Research group (led by Friedman), and MSL Infra (led by Ramani). There is no independent board or governance trust specific to Meta AI; it operates as a division inside the Meta Platforms corporate board structure, chaired by Zuckerberg.
How much funding has Meta AI raised?
Meta AI does not raise independent venture capital; it is funded entirely from Meta Platforms' operating cash flow as a division of a public company trading on Nasdaq under ticker META. For 2026, Meta raised its capital expenditure guidance to a range of $125 billion to $145 billion, roughly double the $72.2 billion Meta actually spent on capex in 2025, with the increase driven almost entirely by AI data centers and custom silicon. Meta has separately reported a $600 billion infrastructure commitment stretching through 2028, funding projects including the Hyperion campus in Louisiana, a $27 billion joint venture with Blue Owl Capital scaling toward 5 gigawatts, and the Prometheus supercluster in Ohio, a 1-gigawatt facility going live in 2026. On the deal side, Meta committed $14.3 billion in June 2025 for a 49 percent non-voting stake in Scale AI, and a planned $2 billion acquisition of AI agent startup Manus (announced December 2025) was blocked by China's National Development and Reform Commission in April 2026. Meta's Q1 2026 revenue reached $56.31 billion, up 33 percent year over year, giving the company the cash generation to self-fund its AI buildout without external investors. Meta stock fell roughly 15 to 29 percent during 2026 as investors weighed the scale of AI capex against unclear near-term returns. There is no IPO or acquisition scenario relevant here since Meta AI is a division of an already-public company, not a standalone entity that could be acquired or listed.
What products does Meta AI make?
Meta AI's core product line splits into two tracks: Llama, an open-weight model family available to download and self-host, and Muse Spark, a closed proprietary family accessible only through Meta's own surfaces and, since July 9, 2026, a paid API. Llama 4, released April 2025, uses a mixture-of-experts architecture and comes in Scout and Maverick variants; a larger Behemoth variant was delayed indefinitely after Meta faced allegations of gaming public benchmarks. Muse Spark, launched April 8, 2026, powers the Meta AI assistant across Facebook, Instagram, WhatsApp and Messenger, and is natively multimodal with a 262,000-token context window. Muse Spark 1.1, launched July 9, 2026, opened to outside developers through the Meta Model API at $1.25 per million input tokens and $4.25 per million output tokens, with a 1-million-token context window and native support for agentic, multi-step coding workflows; new developers get $20 in free credits before pay-as-you-go billing starts. Meta AI also ships hardware: Ray-Ban AI smart glasses, which sold 7 million units in 2025 and run Meta AI natively for voice queries, translation and visual search. On July 1, 2026, Meta announced Meta Compute, a new cloud business that resells excess AI compute capacity to outside customers, including reported deals worth $1.25 billion per month with Anthropic and $920 million per month with Google. Unlike the closed Muse Spark API, Llama weights remain broadly available through third-party hosts including Together AI, Fireworks AI, AWS Bedrock and Google Vertex AI.
Where is Meta AI headquartered and how big is the team?
Meta AI and Meta Superintelligence Labs are headquartered at Meta Platforms' campus in Menlo Park, California, with major research and engineering hubs also in Seattle, New York, London, Paris, Tel Aviv and Montreal. Meta Platforms employed 78,865 people companywide at the end of 2025, falling to about 77,986 by the end of March 2026. On May 20, 2026, Meta cut roughly 8,000 jobs, about 10 percent of its workforce, with the deepest cuts hitting integrity, cybersecurity and Reality Labs teams, bringing total headcount to an estimated 70,000. At the same time, Meta redirected about 7,000 existing employees into four newly formed AI units under Meta Superintelligence Labs, protecting AI infrastructure and foundation-model teams from the layoffs. Meta does not disclose a standalone Meta AI or MSL headcount; the division is best described as a multi-thousand-person organization embedded inside a roughly 70,000-person company. Engineering remains Meta's largest job function, accounting for an estimated 30 to 35 percent of total headcount, with research making up 2 to 4 percent. Meta continues remote and hybrid work policies for many roles, though AI infrastructure and research roles are concentrated at its US hubs.
What is Meta AI's mission or research focus?
Meta Superintelligence Labs states its mission as building personal superintelligence safely and making it available to everyone, a sharper and more explicit framing than FAIR's earlier academic-research mission of advancing human-level AI through open publication. FAIR, the original research arm founded in 2013, continues to publish open research spanning computer vision, natural language processing, speech and reasoning, and remains the origin of widely used tools including the PyTorch deep learning framework. Since April 8, 2026, Meta has governed frontier model releases under the Advanced AI Scaling Framework version 2, which gates deployment against three catastrophic risk categories: chemical and biological, cybersecurity, and loss of control. The framework introduced Safety and Preparedness Reports, published alongside qualifying model releases, detailing Meta's risk assessments, evaluation results and the reasoning behind deployment decisions. This differs from Meta's earlier, looser approach criticized after Llama 4's April 2025 launch, when independent researchers alleged Meta had gamed public leaderboard benchmarks. Founding FAIR director Yann LeCun left Meta in November 2025 arguing that large language models are a dead end for reaching superintelligence, a philosophical split from the LLM-centric direction Alexandr Wang's Meta Superintelligence Labs has since taken. Meta's research posture today sits between Anthropic and OpenAI's structured safety frameworks and the more permissive open-weight ethos that built Llama's developer ecosystem.
Is Meta AI compliant with SOC 2, GDPR, HIPAA?
Meta Platforms publishes a general privacy policy and terms of service covering Meta AI products, and complies with GDPR and CCPA for data collected through its consumer AI surfaces, including opt-out mechanisms in the EU and UK for users whose public Facebook and Instagram posts may otherwise be used to train models. Meta has not published a dedicated SOC 2 Type II or ISO 27001 trust center specific to Meta AI or the Muse Spark API the way Anthropic, OpenAI or enterprise-focused API vendors have; Meta's public security documentation is oriented toward its broader consumer platforms and Meta for Work / Horizon enterprise offerings rather than a standalone AI trust center. There is no publicly confirmed HIPAA-eligible tier or business associate agreement offering for Muse Spark as of its July 2026 public API preview. Under the EU AI Act, Llama and Muse Spark are classified as general-purpose AI models subject to transparency obligations ahead of the August 2026 compliance deadline, with Muse Spark's capability tier under review for potential systemic-risk classification given its scale. Meta's training data policy for Muse Spark states that API inputs are not used to train future models unless a customer explicitly opts in, differing from how Meta trains its consumer-facing Llama and Muse Spark assistant products on public platform content. Enterprises evaluating Meta AI for regulated workloads should treat its compliance posture as less mature than dedicated enterprise AI vendors, pending clearer public certification disclosures.
Who are Meta AI's main competitors?
Meta AI's primary competitors are OpenAI, Google DeepMind and Anthropic, each pursuing a different structural advantage. OpenAI, valued at roughly $850 billion as of March 2026, leads on ChatGPT's consumer distribution, having crossed 1 billion monthly active users in June 2026, a milestone Meta AI first reached in Q1 2025 but has not since updated with fresh figures. Google DeepMind holds the deepest infrastructure advantage through custom TPU silicon and native distribution across Search, Android and Workspace, areas where Meta has no direct equivalent. Anthropic, valued around $380 billion, leads on enterprise trust and compliance depth, an area where Meta AI's documentation remains comparatively thin. Meta's structural counter-advantage is free reach: the Meta AI assistant is embedded across Facebook, Instagram, WhatsApp and Messenger, plus 7 million Ray-Ban AI smart glasses sold in 2025, giving it a distribution footprint none of its closed-model rivals can match without a consumer platform of their own. On raw capability, Muse Spark's Artificial Analysis Intelligence Index score of 52 trails Gemini 3.1 Pro Preview (57), GPT-5.4 (57) and Claude Opus 4.6 (53), though Muse Spark leads specific benchmarks like figure understanding and HealthBench Hard. Meta's Muse Spark 1.1 API, priced at $1.25 per million input tokens and $4.25 per million output tokens, undercuts Anthropic's and OpenAI's comparable pricing by roughly 75 percent, giving Meta a cost-based wedge even where it trails on raw benchmark scores. Emerging competitors not on the radar 12 months ago include DeepSeek and Mistral AI, both open-weight labs increasingly competing with Llama for the developer mindshare Meta considers its core moat.
Top Alternatives
- OpenAI: Pick OpenAI if you need the strongest frontier reasoning scores; pick Meta AI if you want to self-host open weights for free.
- Google DeepMind: Pick Google DeepMind if you need native Search/Android/Workspace integration; pick Meta AI if you want a permissive open-weight license.
- Anthropic: Pick Anthropic if enterprise compliance depth matters most; pick Meta AI's Muse Spark API for roughly 75% lower per-token pricing.
- DeepSeek: Pick DeepSeek if you want the cheapest open-weight alternative; pick Meta AI (Llama) for the larger fine-tuning and hosting ecosystem.