Meta AI

See also: Terms and Artificial intelligence terms

Meta AI is the artificial intelligence assistant and AI division of Meta Platforms, reaching 1 billion monthly active users across WhatsApp, Instagram, Facebook, Messenger, Ray-Ban Meta smart glasses and a standalone app as of May 2025.^[5] The name covers two related things: the consumer AI assistant, and the broader research division that builds Meta's underlying models, frameworks and infrastructure, including the open-weight Llama family, which surpassed 1 billion downloads in March 2025.^[54] The assistant first launched as an integrated chatbot on Meta's social platforms on September 27, 2023, and became a standalone application on April 29, 2025.^[1][2] The division traces its roots to Facebook AI Research (FAIR), founded in December 2013 by Mark Zuckerberg and Yann LeCun, and was reorganized in 2025 into Meta Superintelligence Labs (MSL).

The assistant competes directly with ChatGPT, Google Gemini and other AI applications, offering social features and deep integration with Meta's ecosystem.^[2] The research division has produced influential open-weight models such as the Llama family, Segment Anything, DINO, ImageBind and AudioCraft, along with the PyTorch deep learning framework, all of which have shaped the modern AI landscape.

Overview

Meta AI is designed as a personal AI assistant powered by Meta's Llama family of large language models, specifically utilizing Llama 4 for the standalone app's conversational capabilities.^[3] The assistant can understand user preferences and provide personalized responses based on data from connected Meta accounts, with user consent.^[3]

The application serves multiple functions: as a standalone AI assistant, the companion app for Ray-Ban Meta smart glasses (replacing the previous Meta View app), and as an integrated feature across WhatsApp, Instagram, Facebook, and Messenger.^[4] As of May 2025, Meta AI reached one billion monthly active users across Meta's family of apps, with CEO Mark Zuckerberg announcing the milestone at the company's annual shareholder meeting on May 28, 2025.^[5][55] That figure had roughly doubled in eight months, up from nearly 500 million monthly active users that Zuckerberg reported at Meta Connect in September 2024.^[56] Describing the priorities for the assistant, Zuckerberg said the focus was on "deepening the experience and making Meta AI the leading personal AI with an emphasis on personalization, voice conversations and entertainment."^[55]

FAIR (Facebook AI Research)

Facebook AI Research, known as FAIR, was founded in December 2013 by Mark Zuckerberg and Yann LeCun. LeCun, a professor at New York University and a pioneer of convolutional neural networks, was appointed as FAIR's first director on December 9, 2013. The lab was established with a mission to "advance the state of the art in artificial intelligence through open research for the benefit of all."^[18]

FAIR initially operated from offices in New York City (Astor Place), Menlo Park, and Paris. From its earliest days, the lab adopted an open-science philosophy that was unusual for a corporate research lab. Researchers were encouraged to publish their work at top conferences and release code publicly. This approach attracted leading academics who might otherwise have stayed in university positions. Vladimir Vapnik, a pioneer of statistical learning theory, joined FAIR in 2014.^[36]

FAIR expanded its presence globally over the following years, opening research centers across North America, Europe and the Middle East:

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The Paris lab, opened in June 2015, was the first major U.S. tech AI center in continental Europe.^[37] FAIR Montreal began in September 2017 under Joelle Pineau, expanding Canadian federal AI initiatives.^[38] In 2016, FAIR partnered with Google, Amazon, IBM and Microsoft in creating the Partnership on Artificial Intelligence to Benefit People and Society. By 2018 the lab had approximately 200 staff members, with Jérôme Pesenti as president and Yann LeCun as chief AI scientist.

Following the rebranding of Facebook, Inc. to Meta Platforms in October 2021, FAIR was renamed to Meta AI, reflecting the company's broader focus on the metaverse and advanced AI technologies.

Over its first decade, FAIR grew into one of the most prolific AI research organizations in the world. By 2023, FAIR researchers had published thousands of papers, with many becoming among the most cited in the field. At the lab's 10-year anniversary in November 2023, Meta highlighted that the top three most cited AI papers of 2023 all came from FAIR: "LLaMA: Open and Efficient Foundation Language Models" (8,534 citations), "Llama 2: Open Foundation and Fine-Tuned Chat Models" (7,774 citations), and "Segment Anything" (5,293 citations). FAIR also won best paper awards at several major conferences in 2023, including ACL, ICRA, ICML, and ICCV.^[18]

Key research areas within FAIR have included self-supervised learning, computer vision, natural language processing, speech recognition, reinforcement learning, and robotics. The lab's contributions span foundational techniques such as self-distillation, contrastive learning at scale, and mixture-of-experts architectures, alongside applied systems like real-time translation and object segmentation.

Leadership Changes at FAIR

Yann LeCun led FAIR from its founding through 2018, when he transitioned to the role of Chief AI Scientist for all of Facebook (later Meta). Joelle Pineau, a professor at McGill University and the Mila research institute in Montreal, joined Meta in 2017 and became the head of FAIR in 2023. Pineau oversaw the lab's operations during a period of rapid growth in generative AI and the release of the Llama model family.

In April 2025, Pineau announced her departure from Meta, with her last day on May 30, 2025. Her exit coincided with a broader organizational shift at Meta, as the company restructured its AI teams to focus more heavily on product delivery and pursuit of artificial general intelligence.^[19]

Yann LeCun departed Meta in November 2025, ending a twelve-year tenure as the company's chief AI scientist. He subsequently founded AMI Labs, a startup focused on building "world models" that can understand the physical world, in contrast to the prevailing large language model paradigm. In March 2026, AMI Labs announced that it had raised $1.03 billion in funding at a $3.5 billion pre-money valuation, with investors including Cathay Innovation, Bezos Expeditions, and others.^[20]

GenAI Team and Organizational Structure

In February 2023, Meta CEO Mark Zuckerberg announced the creation of a new top-level product group focused on generative AI. This group was tasked with building generative AI tools and experiences across Meta's family of apps, including AI chat in WhatsApp and Messenger, AI image generation in Instagram, and AI-powered ad formats.

In January 2024, Zuckerberg merged FAIR and the GenAI product team into a unified organization to accelerate development and reduce duplication of effort. The combined team worked on both foundational research and the consumer-facing Meta AI assistant.

Meta Superintelligence Labs (2025)

In June 2025, following concerns about Meta's competitiveness in artificial general intelligence, Mark Zuckerberg announced the formation of Meta Superintelligence Labs (MSL), a new division focused on superintelligence research. The formation came after an aggressive acquisition and hiring strategy, including a $14.3 billion investment in Scale AI for a 49 percent non-voting stake, with founder Alexandr Wang joining Meta as its first Chief AI Officer. Meta also pursued failed acquisitions of Safe Superintelligence (then valued at roughly $32 billion) and Perplexity AI, and recruited Daniel Gross (CEO of Safe Superintelligence) and Nat Friedman (former GitHub CEO) to lead parts of the new organization.^[21][39]

On August 19, 2025, Meta announced a restructuring that split MSL into four distinct teams:

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As part of this restructuring, Meta's AGI Foundations team was dissolved and absorbed into the MSL divisions. In October 2025, Meta announced the elimination of approximately 600 roles across the FAIR, Products and Applied Research, and MSL Infra teams.^[22]

Features

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Voice Features

The Meta AI app emphasizes voice-based interaction as its primary interface:

• Standard Voice Mode: Allows users to have conversations with the AI assistant using natural speech

• Full-Duplex Speech Demo: An experimental feature using real-time voice generation technology that creates more natural, conversational responses without relying on text-to-speech conversion

• Ready to Talk: An optional setting that enables voice interaction by default when opening the app^[3]

Key Model Families and Research Releases

Meta's AI division has produced a wide range of models spanning language, vision, audio, and multimodal domains. The sections below cover the most significant model families.

Llama (Large Language Model Meta AI)

The Llama family is Meta's flagship series of large language models. It has become one of the most widely adopted open-weight model families in the industry, surpassing 1 billion cumulative downloads on March 18, 2025 and reaching 1.2 billion by late April 2025, up from 650 million in December 2024.^[54][57]

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The Llama 4 family, announced at Meta's inaugural LlamaCon developer conference on April 29, 2025, represented a significant architectural shift: Scout and Maverick were the first Llama models to use a mixture-of-experts design and the first to be natively multimodal (processing both text and images in a single model).^[23]

Llama Guard

Llama Guard is a family of safety classification models designed to filter harmful content in AI interactions. Released alongside major Llama versions, these models are built to detect unsafe prompts and responses according to a standardized hazard taxonomy.

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These models are part of Meta's broader Purple Llama project, which provides tools for responsible AI deployment, including CyberSecEval for evaluating cybersecurity risks and Code Shield for filtering unsafe code.^[24]

Segment Anything (SAM)

The Segment Anything Model (SAM) is a foundation model for image segmentation released in April 2023. SAM can segment any object in any image based on user prompts such as points, bounding boxes, or text descriptions. The model was trained on SA-1B, a dataset of over 1 billion masks across 11 million images.

SAM comes in three sizes: ViT-B (91 million parameters), ViT-L (308 million parameters), and ViT-H (636 million parameters). It was released under an Apache 2.0 license.^[25]

In July 2024, Meta released SAM 2, extending the model's capabilities to video segmentation. SAM 2 uses a transformer architecture with streaming memory, allowing it to track objects across video frames even when they temporarily leave the field of view. Compared to the original SAM, SAM 2 is six times faster on images while achieving better accuracy. It was trained on the SA-V dataset, which contains over 50,000 videos and 35.5 million segmentation masks. An updated SAM 2.1 followed in the fall of 2024 with improved performance for visually similar objects.^[26]

DINO, DINOv2 and DINOv3

DINO (self-DIstillation with NO labels) is a self-supervised learning method for training vision transformers without labeled data. Published in 2021, DINO demonstrated that self-supervised vision transformers can learn features that contain explicit information about semantic segmentation, enabling object detection without any supervision.

DINOv2, released in April 2023, scaled up this approach to produce general-purpose visual features that perform well across a variety of computer vision tasks including classification, segmentation, and depth estimation. DINOv2 models can be used with simple linear classifiers and still achieve strong results, eliminating the need for task-specific fine-tuning in many cases. Meta later relicensed DINOv2 under an Apache 2.0 license for commercial use.^[27]

DINOv3, released in August 2025, scaled self-supervised learning further to produce universal vision backbones that transfer across a wide range of downstream tasks without task-specific adaptation. The release continued FAIR's line of work on label-free representation learning for images.^[40]

Meta has also released several widely used computer vision libraries, most notably Detectron2, an open-source object detection and segmentation toolkit, and FAISS, a library for efficient similarity search and clustering of dense vectors that is widely used in retrieval-augmented generation pipelines.

ImageBind

ImageBind is a multimodal embedding model released in May 2023. It was the first AI model capable of learning a joint embedding space across six modalities: images, text, audio, depth (3D), thermal (infrared), and inertial measurement unit (IMU) data. The key innovation is that images serve as a bridge between different modalities, allowing the model to learn cross-modal relationships without requiring training data covering every possible combination of modalities.

ImageBind enables applications such as cross-modal retrieval (searching for audio clips using images), modality arithmetic (combining embeddings from different modalities), and cross-modal generation. It was presented as a highlighted paper at CVPR 2023 and released as open source on GitHub.^[28]

AudioCraft

AudioCraft is a framework for audio generation released in August 2023. It consists of three components:

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All AudioCraft model weights and code were released as open source, allowing researchers to train their own models on custom datasets.^[29]

Emu (Image and Video Generation)

Emu is a family of generative models for images and video, first announced in September 2023. The foundation Emu model is a latent diffusion model pre-trained on over 1 billion image-text pairs, then fine-tuned on a curated set of high-quality images.

In November 2023, Meta released two extensions:

• Emu Video: A text-to-video model that uses a factorized two-step approach, first generating an image from a text prompt, then animating it into a 4-second video at 512x512 resolution and 16 frames per second. It was trained on 34 million video-text pairs.
• Emu Edit: An instruction-based image editor that can perform local editing, background removal, color transformations, and more while leaving unrelated pixels untouched. It was trained on 10 million synthesized editing samples.

The Emu models power image generation features within the Meta AI assistant across WhatsApp, Instagram, Facebook, and Messenger.^[30]

Seamless Communication Models

Meta has released a series of translation and speech models aimed at breaking language barriers:

• No Language Left Behind (NLLB): A text-to-text machine translation model supporting 200 languages, released in 2022. NLLB has since been integrated into Wikipedia as one of its translation providers.
• SeamlessM4T: Released in August 2023, this was the first unified multimodal model supporting speech-to-text, speech-to-speech, text-to-speech, and text-to-text translations for up to 100 languages. Built using 1 million hours of open speech data and the SeamlessAlign dataset (406,000 hours of aligned speech translations), it improved speech-to-text translation accuracy by 20% and speech-to-speech by 58% compared to previous approaches.
• Seamless Communication Suite: An expanded set of models released in late 2023 that added features like real-time streaming translation and expressive, style-preserving speech translation.^[31]

Game-Playing and Scientific AI

Beyond language and vision, Meta AI has produced a number of research systems that pushed the state of the art in agents, mathematics and the sciences.

• Cicero: Published in Science in December 2022, Cicero was the first AI agent to reach human-level play in the strategy game Diplomacy. It combined a large language model for natural-language negotiation with planning and reinforcement-learning components, and roughly doubled the average score of human players in online tournaments.^[41]
• HyperTree Proof Search (HTPS): A neural theorem-proving system that, in 2022, proved 10 International Mathematical Olympiad problems in the Lean proof assistant, demonstrating that learned search policies can solve competition-level mathematics.^[42]
• Galactica: A large language model for scientific text released in November 2022. The public demo was withdrawn within three days after researchers showed the model would confidently generate fluent but fabricated citations and inaccurate scientific content. The episode became an early warning case study for hallucinations in domain-specialized LLMs.^[43]
• Protein structure prediction: In 2022, Meta AI used a protein language model approach (the ESM family) to predict the structures of roughly 600 million metagenomic proteins in about two weeks, complementing DeepMind's AlphaFold work on a different slice of the protein universe.^[1]

PyTorch

PyTorch is an open-source deep learning framework originally developed by FAIR. Led by Soumith Chintala, the PyTorch team designed the framework around dynamic computational graphs, offering a more intuitive and flexible alternative to the static-graph approach used by TensorFlow at the time.

PyTorch was released publicly in January 2017 and quickly gained traction in the research community for its ease of use and Pythonic design. It became the dominant framework for AI research and is now widely used in production systems as well.

In September 2022, Meta transitioned the governance of PyTorch to the newly established PyTorch Foundation under the Linux Foundation. The foundation's governing board includes representatives from Meta, Microsoft, Amazon, Google, and other major technology companies. This move was intended to ensure PyTorch's long-term neutrality and community-driven development.^[32]

PyTorch is widely considered one of FAIR's most consequential contributions to the AI field. As of 2025, it underpins the training and deployment of the majority of state-of-the-art AI models across industry and academia.

Open-Source Philosophy and Strategy

Meta has been the most prominent large technology company to embrace an open-weight approach to AI model releases. Beginning with Llama 2 in July 2023, Meta released its major language models under permissive licenses that allow commercial use, a strategy that stood in contrast to the closed approaches of OpenAI, Google, and Anthropic. In announcing the 1 billion Llama download milestone, Meta said that "open sourcing AI models is essential to ensuring people everywhere have access to the benefits of AI."^[54]

The rationale behind this strategy has several components:

1. Ecosystem building: Every company or developer that deploys a Llama-based model strengthens Meta's position in the AI ecosystem. Meta's frameworks, optimization tools, and developer APIs gain relevance with each deployment, creating a network effect.
2. Talent attraction: An open research culture has helped Meta recruit and retain top AI researchers who value the ability to publish and share their work.
3. Commoditizing complements: By making powerful foundation models freely available, Meta reduces the competitive advantage that rivals derive from their proprietary models, while Meta itself benefits from AI through its advertising and social media businesses.
4. Safety through transparency: Meta has argued that open models allow the broader community to identify and fix safety issues more effectively than closed development.

Is Meta AI open source?

Meta's open-source approach began evolving in 2025. In July 2025, Zuckerberg signaled that Meta would likely not open-source all of its most advanced "superintelligence" AI models. By late 2025, reports emerged that Meta was developing a model codenamed "Avocado" that would be released as a closed model, one that Meta could sell access to. This would represent the biggest departure from the open-weight strategy that Meta had championed for years.

The shift was driven by pressure to monetize AI investments directly, as Meta poured billions into researcher salaries, data center construction, and the development of increasingly powerful models. Despite having one of the top AI research labs in the world, Meta faced challenges in commercializing its AI work compared to rivals like OpenAI and Google.^[33] The pattern was confirmed in April 2026 when Meta released Muse Spark, its first major MSL model, as a proprietary system rather than an open-weight one (see Recent developments below).

AI Infrastructure

Meta has made substantial investments in the compute infrastructure required to train and serve large AI models. The company's approach combines custom silicon development with massive deployments of commercial GPUs.

Custom Silicon: MTIA

The Meta Training and Inference Accelerator (MTIA) is Meta's family of custom AI chips, designed specifically for the deep learning workloads that power Meta's apps and services.

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MTIA v1 delivers 102.4 TOPS for INT8 operations and 51.2 TFLOPS for FP16 operations. The next-generation chip achieved 6x model serving throughput at the platform level (with 2x the number of devices and a more powerful CPU host) along with a 1.5x improvement in performance per watt.

In October 2025, Meta publicly confirmed its acquisition of chip startup Rivos, further strengthening its in-house silicon capabilities. Meta has stated that custom chips allow it to achieve better price-performance across its data center fleet than relying solely on third-party vendors.^[34]

GPU Clusters and Data Centers

Alongside custom silicon, Meta operates some of the largest GPU clusters in the world. Key milestones in Meta's infrastructure build-out include:

• By the end of 2024, Meta targeted an infrastructure equivalent to 600,000 NVIDIA H100 GPUs, with 340,000 physical H100 chips deployed across its data centers.
• In 2025, Meta committed between $60 billion and $65 billion in capital expenditure for AI infrastructure, and ultimately spent roughly $72 billion on capital expenditures for the year.^[58]
• For 2026, spending is projected to rise to between $115 billion and $135 billion, with plans to bring over 1 gigawatt of AI computing power online and purchase more than 1.3 million GPUs.
• A multi-billion-dollar, multi-year deal with NVIDIA announced in early 2026 covers Blackwell and Rubin GPUs, Grace and Vera CPUs, and Spectrum-X Ethernet networking fabric.
• Meta's total data center capacity is expected to exceed 10 gigawatts by late 2026, with active projects in at least nine countries. Many new facilities are being engineered for liquid immersion and direct-to-chip cooling.^[35]

Platform Integration and Availability

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Language Support

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Development History

When was Meta AI launched?

Meta unveiled Meta AI during the Meta Connect keynote on September 27, 2023, initially powered by a custom model based on Llama 2.^[1] The assistant gained real-time information access through a search partnership with Bing and was rolled out in English to users in the United States and 13 other markets.^[1]

Platform Expansion (2024)

In April 2024, Meta upgraded the assistant to use Llama 3, adding faster image generation and inline web results.^[9] The assistant was progressively integrated across all major Meta platforms throughout 2024, with multilingual support added in July 2024.^[10] By September 2024, Zuckerberg reported that Meta AI had nearly 500 million monthly active users and was on track to become the most-used AI assistant in the world.^[56]

Standalone App Launch (2025)

Reports of a standalone Meta AI app first emerged in February 2025.^[11] The app was officially announced and launched at Meta's inaugural LlamaCon developer conference on April 29, 2025, powered by an early Llama 4 checkpoint.^[2][12]

Key People

The following table lists individuals who have played significant roles in Meta's AI efforts.

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Technical Specifications

• AI Model: Powered by Llama 4 large language models (standalone app); previously Llama 2 and Llama 3^[3]

• Voice Technology: Full-duplex speech technology for natural voice conversations^[3]

• Knowledge Sources: Integration with Google and Microsoft Bing for real-time information^[8]

• Hardware Integration: MTIA v1 AI accelerator (7nm chip delivering 102.4 TOPS for INT8 and 51.2 TFLOPS for FP16) and Nvidia GPUs for compute power

• Platform Requirements: Compatible with iOS 15.2+, Android, and modern web browsers^[4]

Business Model

While the basic Meta AI app is free, Meta has indicated plans for monetization. At the May 2025 shareholder meeting, Zuckerberg said "there will be opportunities to either insert paid recommendations" or offer "a subscription service so that people can pay to use more compute."^[55]

• Premium Subscription: Testing of paid subscription tiers for advanced features planned for Q2 2025^[12]

• Advertising Integration: Potential for "paid recommendations" within the AI responses^[5]

• Enhanced Compute Access: Subscription users may access more computational resources for complex queries^[5]

Reception and Controversies

Privacy Concerns

The Discover Feed feature has raised significant privacy concerns:

• Unintentional Sharing: Reports of users unknowingly sharing personal conversations publicly, including medical queries, personal data, and work-related information^[13]

• UI/UX Issues: Criticism that the app interface doesn't clearly indicate when content will be shared publicly^[14]

• Data Usage: Meta has stated that public posts may be used to train AI models in regions like the European Union

Mozilla Foundation launched a petition demanding Meta improve the app's design to ensure users understand when they're sharing content publicly.^[13]

In May 2024, the Meta AI chatbot drew criticism for summarizing news articles inside chats without linking back to original sources, which raised legal and ethical questions in markets such as Canada where news links are blocked on Meta's platforms.^[44]

Copyright, Training Data and Safety Reviews

• Reports in 2025 alleged that Meta used pirated books in training corpora for some of its language models, prompting copyright complaints from authors and publishers.^[45]
• In May 2025, Meta announced plans to replace human reviewers with AI systems for parts of its privacy and societal-risk assessment process, a move that current and former employees argued would weaken internal safety review.^[46]

Talent Wars and Internal Tensions

Meta competed for top AI researchers with reportedly very large packages, including signing bonuses said to reach roughly $100 million for the most senior hires. OpenAI CEO Sam Altman publicly confirmed that Meta had approached OpenAI staff with such offers, and Andrew Bosworth (Meta's CTO) called the market for AI talent "unprecedented."^[47]

Internally, the rapid expansion produced tensions between FAIR's traditional fundamental-research culture and the product-focused GenAI organization. Several long-time researchers departed in 2024 and 2025, and some insiders described FAIR as "dying a slow death" as resources shifted toward shipping. The 2025 reorganization into MSL was an explicit attempt to restructure those tradeoffs.^[33]

Llama 4 Reception

The April 2025 release of Llama 4 received mixed reactions from the open-weight community:

• Allegations that some leaderboard scores reflected a fine-tuned chat variant rather than the same checkpoint released to the public.
• Reports of a rushed release schedule.
• Concerns about transparency around training data and evaluation.
• Pressure from rapidly improving Chinese open-weight labs such as DeepSeek.^[48]

Meta denied gaming benchmarks and attributed mixed performance reports to early-release bugs that were addressed in subsequent updates.^[48]

User Experience Issues

Users have reported several technical challenges:

• Android users experiencing significant battery drain (1% every 2 minutes in background) and device overheating^[15]

• Sign-up difficulties for users without existing Facebook or Instagram accounts

• Glitches when importing media from Ray-Ban Meta glasses^[15]

User Growth

Despite concerns, the app has shown rapid adoption:

• 500 million monthly active users reported at Meta Connect in September 2024^[56]

• 600 million monthly active users reported in December 2024^[16]

• 700 million users by January 2025^[2]

• 1 billion users across all Meta platforms by May 2025^[5][55]

Competition

Meta AI competes in the AI assistant market with:

• ChatGPT by OpenAI - Known for advanced conversational abilities and a large third-party plugin ecosystem

• Gemini by Google DeepMind - Offers deep web integration and multimodal capabilities across Google products

• Claude by Anthropic - Emphasizes safety, interpretability, and long-context reasoning

• Grok by xAI - Integrated with the X (formerly Twitter) platform; focuses on real-time information^[2]

How does Meta AI differ from ChatGPT?

Meta's competitive advantages include its integration with the social media ecosystem (reaching billions of existing users) and the unique social Discover Feed feature. Unlike ChatGPT, which is a destination product that users seek out, Meta AI is embedded directly inside apps that already have billions of users, which helped it reach 1 billion monthly active users within about 18 months of launch.^[55] However, its voice mode capabilities have been noted as lagging behind ChatGPT's advanced voice features.^[17]

In the broader AI model market, Meta competes through its open-weight Llama models against proprietary offerings from OpenAI (GPT-4, GPT-4o), Google (Gemini), and Anthropic (Claude), as well as open-weight competitors like Mistral AI, DeepSeek, and Qwen from Alibaba.

Table of Major AI Releases

The following table provides a chronological overview of Meta's significant AI model and tool releases.

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Future Development

Meta has outlined several areas for future development:

• Expansion of voice features to additional countries and real-time web access integration

• Enhanced personalization capabilities leveraging Meta's ecosystem data

• Integration with upcoming Meta hardware products, including a more expensive Ray-Ban Meta model with heads-up display planned for late 2025

• Development of subscription tiers with advanced features

• Expansion to reach 1 billion standalone app users by end of 2025^[5]

• Continued scaling of AI infrastructure, with total data center capacity expected to exceed 10 gigawatts by late 2026

• Development of next-generation closed models (codenamed "Avocado") as part of a potential shift toward monetizing advanced AI capabilities directly

CEO Mark Zuckerberg stated that 2025 would be "the year when a highly intelligent and personalized AI assistant reaches more than 1 billion people," positioning Meta AI as that leading assistant.^[2]

Images

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See Also

• WizardLM

• Deep Cogito

• Deep Cogito

• Llama

• Meta Platforms

• Meta Superintelligence Labs

• Yann LeCun

• Scale AI

• PyTorch

• Segment Anything

• DINO

• ImageBind

• AudioCraft

• ChatGPT

• Gemini

• Claude

• Open Source AI

• Artificial intelligence

• Ray-Ban Meta

Recent developments (2026)

On April 8, 2026, Meta Superintelligence Labs released Muse Spark, the first model in a new Muse family and Meta's first major model since the formation of MSL. Muse Spark is a natively multimodal reasoning model with support for tool use, visual chain-of-thought, and multi-agent orchestration. Meta said it reaches equivalent performance to the earlier Llama 4 Maverick model while using over an order of magnitude less compute. The model ships with a "Contemplating" mode that runs multiple reasoning agents in parallel, which Meta reported scored 58 percent on Humanity's Last Exam and 38 percent on FrontierScience Research. Muse Spark immediately became the engine behind the consumer Meta AI assistant on the standalone app and meta.ai website, with rollout to WhatsApp, Instagram, Facebook, Messenger, and the Ray-Ban, Oakley, and Display AI glasses following over subsequent weeks, alongside a private API preview for select partners.^[49][50] Unlike the open-weight Llama releases, Muse Spark is proprietary; Meta said only that it hoped to open-source future versions, confirming the strategic shift toward closed frontier models that the company had signaled in 2025.^[49]

Meta's AI organization was restructured again in early 2026. On March 4, 2026, an internal memo described a new Applied AI Engineering division led by Maher Saba, a Reality Labs vice president, reporting to CTO Andrew Bosworth rather than to Chief AI Officer Alexandr Wang. The unit is split into two teams, one for interfaces and tools and one for tasks, data collection, and evaluations, and is tasked with building the "data engine" of coding and agentic tools to speed up Meta's model development. The arrangement created a leadership track parallel to Wang's MSL.^[51] On April 14, 2026, Aparna Ramani, the vice president of engineering who had led MSL Infra, announced her departure after nearly a decade at the company; no successor was named, and her teams absorbed her infrastructure responsibilities while Meta's large cloud commitments continued on fixed schedules.^[52]

The company also raised its infrastructure spending. In its first-quarter 2026 earnings report on April 29, 2026, Meta lifted full-year 2026 capital expenditure guidance to between $125 billion and $135 billion at the low end and as much as $145 billion, up from the prior $115 billion to $135 billion range, citing higher component prices and additional data center costs. Quarterly revenue rose 33 percent year over year to $56.3 billion. Meta's share price fell more than 6 percent after hours as investors reacted to the higher spending.^[53] Much of that capital is going toward two named "titan" superclusters: Prometheus, a gigawatt-scale campus in New Albany, Ohio, slated to come online in 2026, and Hyperion in Richland Parish, Louisiana, a multi-gigawatt build that Meta expects to scale toward roughly 5 gigawatts over several years.^[53]

References

1. Meta Connect 2023 keynote, September 27, 2023. ↩
2. "Meta AI app launches at LlamaCon." TechCrunch, April 29, 2025. ↩
3. Meta AI app product page, meta.ai, 2025. ↩
4. Meta AI platform documentation, 2025. ↩
5. Mark Zuckerberg, Meta annual shareholder meeting, May 2025. ↩
6. "Meta AI Discover Feed launches with social sharing features." The Verge, 2025. ↩
7. "Meta AI personalization and data usage." Meta Privacy Center, 2025. ↩
8. "Meta AI integrates Google and Bing search." Reuters, 2024. ↩
9. "Meta upgrades AI assistant to Llama 3." The Verge, April 2024. ↩
10. "Meta AI expands multilingual support." Meta Blog, July 2024. ↩
11. "Reports emerge of standalone Meta AI app." Bloomberg, February 2025. ↩
12. "LlamaCon: Meta's first developer conference for Llama." Meta Blog, April 2025. ↩
13. "Mozilla petitions Meta over Discover Feed privacy." Mozilla Foundation, 2025. ↩
14. "Meta AI app privacy concerns." Wired, 2025. ↩
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