Based on 193 related nodes across 17 research explorations in the AI sector
Most companies competing in AI are trying to win the AI business. Meta is doing something stranger and, structurally speaking, more interesting: it is trying to make sure nobody wins the AI business — and it has the resources to do this indefinitely, because it does not need to win.
Understanding Meta in the AI era means understanding why this is rational, and what it risks.
The Basic Setup: A Company That Gives Away Its Best Work
Meta builds some of the most powerful AI systems in the world. Then it gives them away for free.
This seems like a mistake until you understand where Meta’s money actually comes from. Meta does not sell AI. It sells advertisements on Facebook, Instagram, and WhatsApp. In 2025, that business generated over $100 billion in revenue. Meta uses a portion of that money to fund AI research, then releases the results publicly — at no charge — under a product line called Llama.
Think of it like a supermarket that bakes artisan bread in-house, gives it away to anyone who walks past, and still turns a massive profit because the bread draws people into the store where they buy groceries. The bread is real and expensive to make, but it is not where the money comes from.
This is why Meta’s AI strategy cannot be copied by OpenAI or Anthropic. Those companies need to charge for their AI because AI is their entire business. If they give it away, they collapse. Meta giving away AI is, if anything, good for Meta’s core business — because it forces competitors to charge for something that now appears to be free, making Meta look more dominant in the AI ecosystem without Meta needing to win a single paying customer.
What Meta Is Actually Trying to Do
When you or a startup or a university downloads a Llama model for free, that is the point. Meta wants Llama to become the default AI foundation that the world builds on — the same way Google’s Android became the default smartphone operating system by being free.
This strategy has a name: commoditization. To commoditize something means to turn it from a premium product into a cheap or free ingredient. Meta is trying to commoditize the AI capability that its competitors sell, so that those competitors’ pricing power disappears.
It is working, at least partially. AI model pricing has fallen dramatically over the past two years. OpenAI and Anthropic have had to cut their prices repeatedly. Meta does not care, because Meta was never charging in the first place.
The most important single finding in this research is captured in one structural relationship: the better open-source AI gets, the more effective Meta’s strategy becomes. This is a self-reinforcing loop — and Meta is at its center.
The License Trick Nobody Talks About
Llama is often described as “open source,” but this is not quite accurate. Meta uses a license with a specific clause: if your product or service has more than 700 million monthly users, you cannot use Llama freely. You have to negotiate with Meta.
Seven hundred million users. Who does that describe? Google. ByteDance (TikTok’s parent company). Possibly Microsoft. These are exactly Meta’s largest competitors. Independent developers, startups, universities, and researchers are all below this threshold — they get Llama for free. The tech giants who could most benefit from a free frontier AI model have to ask Meta’s permission.
This is not generosity with a catch. It is a strategic weapon disguised as generosity. Small players get a free tool and build their livelihoods on the Llama ecosystem, which increases Meta’s ecosystem influence. Large competitors get blocked from the free tier entirely. The license appears open while functioning as exclusionary exactly where it counts most.
The Infrastructure Nobody Can Quickly Copy
Behind the open-source strategy is a physical foundation that took years and billions of dollars to build.
Meta has committed to 6.6 gigawatts of nuclear power capacity — enough electricity to power roughly five million homes, dedicated to running AI systems. It has also developed its own custom AI chips called MTIA, designed specifically to run AI workloads cheaply. And unlike companies that rent computing power from Amazon or Microsoft’s cloud, Meta owns its infrastructure outright.
What this means practically: Meta can run AI cheaper than almost anyone else, permanently. When AI inference costs fall toward zero industry-wide — which the research suggests they will, driven by the same competitive dynamics Meta is partly causing — Meta is structurally positioned to absorb that cost through its advertising cash flows and its infrastructure efficiency. Companies that rent compute from cloud providers face unit economics that get worse as they scale. Meta’s unit economics get better.
The Genuine Vulnerabilities
China Is Running the Same Play
The research identifies China as the most significant structural threat to Meta’s strategy — not as a technology competitor in the traditional sense, but as a mirror image. Alibaba and ByteDance operate social media and e-commerce platforms that generate advertising and transaction revenue similar to Meta’s. They are now releasing their own open-source AI models — Alibaba’s Qwen family, and DeepSeek — under far more permissive licenses than Llama. Qwen surpassed Llama as the most downloaded model on Hugging Face.
If developers start defaulting to Chinese open-source models instead of Llama, the entire gravity-well logic inverts. Meta’s influence over the AI ecosystem depends on Llama being the thing people build on. It is not the only candidate anymore.
The Llama license clause blocking 700M-user companies is also, practically speaking, difficult to enforce against entities operating primarily under Chinese law. The restriction may be real in Western jurisdictions and largely theoretical elsewhere.
The Benchmark Scandal
To compete for developer adoption, AI labs publish benchmark scores — standardized tests that let developers compare models. The research documents that Meta tested 27 private variants of Llama 4 before public release and submitted only the top performer to benchmarks. Independent researchers from Cohere, Stanford, MIT, and AI2 published a paper explicitly naming this practice.
This matters because the Llama ecosystem gravity well depends on trust. Developers adopt Llama because they believe it performs as advertised. If benchmark manipulation becomes the accepted narrative around Llama releases, the ecosystem adoption that makes the strategy work begins to erode. This is a fixable problem — third-party evaluation infrastructure exists — but it requires Meta to voluntarily constrain its benchmark optimization behavior.
The Accounting Question
Meta extended the useful life of its GPU hardware from three to four years on its books to six years. This accounting change increased Meta’s reported operating profit by an estimated 20 to 27 percent. The problem: NVIDIA releases new GPU generations rapidly, making older hardware economically obsolete faster, not slower. The six-year depreciation schedule is increasingly disconnected from how quickly the hardware actually loses its competitive value. This does not affect Meta’s actual operations, but it flatters the financial figures that justify continued AI investment to shareholders.
The Fashion-Advertising Dependency
This is the least obvious structural finding in the research. Meta’s advertising revenue — the engine that funds everything — has historically depended substantially on direct-to-consumer fashion brands and fast-fashion retailers as major advertising buyers. The research documents that companies like ASOS and Boohoo are in severe structural decline, with valuations down 90 percent or more from their peaks, caught between rising customer acquisition costs, Shein’s logistics advantages, and shifting consumer behavior.
These companies were large Meta advertising purchasers. Their structural contraction represents a headwind to the advertising flywheel that funds Meta’s entire AI strategy. The exact size of this exposure is not precisely quantified, but the directional signal is consistent across the research.
The Non-Obvious Strategic Situation
Most people watching the AI industry focus on who has the best model or who is spending the most on data centers. The structural finding that deserves more attention is this: Meta is the only major AI company whose strategic interests are served by AI infrastructure remaining unprofitable for everyone else.
OpenAI wants AI to be a profitable business. So does Anthropic. Google and Microsoft want cloud AI revenue to justify their capex. Meta wants none of these companies to have stable AI revenue, because stable AI revenue funds capability that eventually makes Meta’s advertising platform less dominant.
Meta giving away Llama is not charity. It is the same logic as a monopolist selling a product below cost to prevent a competitor from gaining the foothold they need to survive long-term. The difference is that Meta does not have a monopoly on AI — it has a monopoly on its own advertising audience, and it is using the profits from that to prevent anyone from building a stable AI business that could eventually threaten it.
The Things We Don’t Know
The research raises several important open questions that the data cannot answer.
First: what is Meta Superintelligence Labs actually trying to build? Meta paid some researchers $100 million signing bonuses in 2025. Is this team trying to build artificial general intelligence — a fundamental capability breakthrough — or are they focused on engineering optimization to run existing models more cheaply? These have very different implications for Meta’s regulatory exposure and competitive positioning.
Second: what happens to Meta’s advertising business when AI agents start shopping for people? The research documents a growing trend of AI assistants executing purchases directly, bypassing the ad-click-to-purchase funnel that Meta’s advertising model depends on. If people increasingly let AI agents handle their shopping decisions, the human attention that Meta monetizes becomes less valuable. Meta’s consumer AI products — Meta AI integrated into WhatsApp and Instagram — could either accelerate this problem or become the platform that captures the new transaction layer. The research cannot determine which.
Third: does the EU close the open-source carve-out? The EU AI Act contains a partial exemption for open-source model releases. If frontier-scale models like Llama are classified as requiring full regulatory compliance regardless of open-source status, Meta faces compliance obligations that do not currently exist and that would slow or constrain its release cadence.
Bottom Line
Meta is not winning the AI race. It is redesigning the track.
Its strategy is structurally coherent: use advertising profits to fund AI research, release the results freely to prevent competitors from building profitable AI businesses, build energy and silicon infrastructure to run inference at costs no competitor can sustain, and let the advertising flywheel continue funding the cycle.
The strategy is genuinely durable against most competitive threats — except one: a competitor that operates the same advertising-funded open-source strategy from outside Western regulatory jurisdiction. DeepSeek and Alibaba are not just better AI models. They are evidence that Meta’s structural playbook can be run by entities that do not face the same licensing constraints, regulatory obligations, or governance expectations.
The benchmark credibility problem is manageable. The accounting question is a medium-term risk. The advertising dependency on distressed sectors is a real headwind. But the China mirror risk — a parallel AI ecosystem funded by social commerce revenues, releasing permissive open weights without governance constraints — is the structural variable that the research weights as most capable of disrupting Meta’s position, because it is the one threat that cannot be countered by spending more money.
Meta’s bet is that the Llama ecosystem becomes too embedded to displace before that parallel ecosystem matures. Whether that bet lands depends on a race that is already underway.