While most of the leading AI labs guard their models closely, Meta has taken the opposite path, giving its most powerful models away. That bet has made it the champion of open AI.
Meta, founded in 2004 by Mark Zuckerberg and still led by him, is best known for the social platforms used by billions: Facebook, Instagram, WhatsApp, and Messenger. But over the past few years it has become one of the most important forces in artificial intelligence, and it has done so with a strategy that sets it apart from nearly everyone else. Meta builds frontier-class models and then releases them as open weights, free for developers around the world to download, study, run, and build on.
That choice is more than generosity. It is a considered bet that open models accelerate innovation, build a vast ecosystem around Meta's technology, and ultimately serve the company's interests better than locking everything away. So far, the bet has produced one of the largest developer communities in AI and an assistant used by more than a billion people.
The dominant approach among leading labs is to keep the most capable models behind an interface, accessible only as a paid service. Meta argues that this is not the only way, and perhaps not the best one. By publishing its Llama models openly, Meta lets any developer, startup, researcher, or government build on top of state-of-the-art AI without asking permission or paying a toll. The argument is that openness drives faster progress, wider scrutiny, and more competition, and that a thriving open ecosystem lifts everyone, including Meta.
There is a strategic logic beneath the principle. If the open models that the world builds on are Meta's, then Meta shapes the standards, attracts the talent, and benefits from the improvements the community contributes. Open weights also give companies something the closed labs cannot: the ability to run powerful AI on their own infrastructure, with full control over their data. For many organizations, that control is decisive, and Meta is the company offering it at the frontier.
The Llama family of models is the heart of this strategy, and its reach is remarkable. Llama models have been downloaded well over a billion times, averaging on the order of a million downloads a day, and they have spawned tens of thousands of derivative and fine-tuned versions built by the community. That makes Llama the foundation of the open-model world, the default starting point for a huge share of developers who want capable AI they can shape themselves.
Meta has also turned its scale into a consumer advantage. Its Meta AI assistant, embedded across Facebook, Instagram, WhatsApp, and Messenger, has grown to more than a billion monthly users, making it one of the most widely used AI assistants in the world. Few companies could put an assistant in front of so many people so quickly, because few own the apps that billions already open every day. Meta does, and that distribution turns its AI investments into immediate, global reach.
Meta's AI work did not begin with the current boom. Its Fundamental AI Research group, known as FAIR, has been a serious research organization for over a decade, and its contributions extend well beyond Meta's own products. The most far-reaching is PyTorch, the open-source software framework that has become one of the most widely used tools in all of machine learning, the foundation on which a large share of the world's AI, including work at rival labs, is built.
That legacy matters because it reflects the same philosophy that drives the open-model strategy. Meta has repeatedly chosen to give foundational tools to the wider community, and the field has been shaped by those gifts. A company that produced PyTorch and then made frontier models open has, more than once, expanded what every other builder can do.
Meta is the counterweight in the AI landscape, the company making the case that the most powerful models should be open, not locked away. That stance has real consequences. It gives startups and researchers access they would otherwise lack, it lets businesses keep control of their data, and it keeps the closed labs honest by ensuring there is always a capable open alternative.
Combine that with an assistant used by a billion people and a research legacy that underpins much of modern AI, and Meta's importance is clear. Whether or not open or closed ultimately wins, the field is healthier for having a company of Meta's resources committed to the open path. For developers around the world, that commitment has put frontier AI within reach, and that may prove to be one of the most consequential decisions of this era.
The true measure of Meta's open strategy is not any single model but the ecosystem it has created. Because Llama is freely available, developers around the world have produced tens of thousands of customized and fine-tuned versions, adapting it for specific languages, industries, and tasks. Startups build products on it without paying a toll, researchers probe it to advance the science, and companies run it on their own hardware to keep full control of their data. Meta effectively seeded an entire economy of builders, and that economy now improves and extends its technology far beyond what any single company could do alone.
This is the strategic genius of openness. Every developer who builds on Llama has a reason to want it to succeed, every improvement the community discovers can flow back into the ecosystem, and the standards that emerge tend to be Meta's standards. In a field where closed providers must do all the innovating themselves, Meta has recruited the world as its collaborators. The result is reach and momentum that would be enormously expensive to buy any other way.
For organizations wary of depending on a closed service they cannot inspect or control, the open option is not a compromise but a requirement, and Meta is the company offering it at the frontier. That makes Llama the natural foundation for a huge share of serious independent AI work, and it gives Meta influence over the direction of the field that extends well past its own products.
Meta's commitment to open technology did not begin with Llama. Years earlier, its researchers created PyTorch, a software framework for building machine learning systems that has since become one of the most widely used tools in all of AI. A great deal of the world's AI research and development, including work at companies that compete directly with Meta, is built on PyTorch. It is foundational infrastructure, quietly underpinning the field.
That contribution reflects the same philosophy that drives the open-model strategy. Meta has repeatedly chosen to give important tools to the wider community rather than hoard them, and the entire field has been shaped by those gifts. A company that produced PyTorch and then made frontier models open has, more than once, expanded what every other builder in AI is able to do.
Behind these contributions sits a long-standing research organization that has pursued fundamental advances in machine learning for over a decade. That depth gives Meta the ability to keep producing genuinely new science, not just incremental products, and it anchors the company's credibility as a serious AI lab rather than merely a large company that adopted AI.
Meta also has something most AI labs lack: direct access to billions of people through apps they already use every day. Its Meta AI assistant is built into Facebook, Instagram, WhatsApp, and Messenger, which is how it reached a billion users so quickly. But AI runs deeper than the visible assistant. The recommendation systems that decide what people see across Meta's platforms are themselves sophisticated AI, and improvements there translate directly into a more engaging experience and a stronger business.
The company is also betting on new form factors for AI. Its smart glasses, developed with a leading eyewear maker, point toward a future in which an AI assistant can see what you see and help in the moment, hands free. It is an early but genuinely interesting vision of how people might interact with AI beyond typing into a screen, and Meta's willingness to invest in the hardware as well as the models reflects the scale of its ambition.
None of this would be possible without a massive commitment to infrastructure. Meta has invested heavily in the computing power needed to train ever more capable models, signaling that its leadership sees AI not as a feature but as central to the company's future. That long-horizon commitment, paired with open models, a foundational research legacy, and unmatched consumer distribution, makes Meta one of the most consequential and distinctive forces in the entire field.
It is worth dwelling on why Meta's openness matters beyond the company itself. When frontier models are available freely, a researcher in a university with a modest budget, a startup in a country far from Silicon Valley, or a government building tools for its citizens can all work with capability that would otherwise be locked behind the doors of a few wealthy firms. That access is a genuine democratizing force, and it changes who gets to shape and benefit from the technology.
For organizations and nations concerned about depending on foreign, closed services, open models offer a path to self-reliance. They can run the technology on their own infrastructure, inspect how it works, and adapt it to their own needs and values. Meta, by making capable models open, has given the world a credible alternative to a future in which advanced AI is available only as a rented service from a handful of providers.
There is also a healthy competitive effect. The existence of strong open models keeps the closed labs honest, ensuring there is always a capable alternative that anyone can use. That pressure benefits everyone, pushing quality up and helping keep the technology from being controlled too narrowly. In that sense Meta plays a balancing role in the entire ecosystem, a counterweight that makes the field more open and more competitive than it would otherwise be.
All of this rests on a serious, sustained commitment. Meta has poured resources into research, models, infrastructure, and new hardware, and its leadership has been explicit that AI is central to the company's future. The combination of that commitment with a genuine philosophy of openness is what makes Meta distinctive. It is not just a large company that adopted AI. It is the standard-bearer for a particular, hopeful vision of how the technology should be built and shared, and that vision has already shaped the field in lasting ways.
Jason Kumpf follows the AI industry for what it means to business. He is Head of US Revenue at Razorpay, a board advisor, angel investor, and speaker. More about Jason.