Mozilla: The state of open source AI

V1.0 · Recurring · July 2026 In New Zealand's far north, a Māori broadcaster trains speech models for te reo — a language too small for any market — under a license that keeps the data with its people. PwC, one of the largest accounting firms in the world, fine-tuned an open model on the language of finance and runs it today for hundreds of clients, on its own hardware, with no per-token meter running. Researchers in Lausanne built an open medical model with the Red Cross, tuned to its humanitarian guidelines, and are preparing clinical trials at home and in Tanzania. In East Africa, farmers diagnose cassava disease with a model that runs on the phone itself, offline, in fields the cloud has never reached. In Switzerland, a public consortium trained a national model on public supercomputers and released all of it: weights, data, training code. None of them asked permission, and none of them could have rented this. They own it — that is the whole idea. We have been here before. Mozilla exists because one company tried to own the front door to the web, and an open community rose up to make sure it never could. Twenty-five years later, someone is running the same play. We bet on open the first time. Open won. Together, we can do it again. Our belief is simple: the path forward is competition and interoperability. We believe in a world of many models, standard ways to plug them together, and the freedom to walk away from any vendor at any time. Open has a record here. It grew the pie and let more people own a slice of it. Read what follows as a map: where open AI is winning — some numbers surprised even us — and where it is exposed. A case that hides its weak points is an advertisement.” Open weights are no longer a compromise. They are where the work happens: a majority of production tokens now route through them, and the five highest-volume models on OpenRouter are all open. Closed models still lead at the frontier, on reasoning and multimodality, but the frontier is not what most workloads need. Commodity inputs do not hold pricing power. Value moves up, to the agentic harness. Data from the Mozilla / SlashData 2026 developer survey. Open models lead in adoption: 79% of developers adding AI functionality use them, against 71% for closed, and the two are largely complementary, with half of developers using both. But production is where teams stall: only 51% of open-model teams reach production versus 63% for closed. The gap is operational tooling and trust, not model capability. | Challenge | W. Europe & Israel | N. America | Greater China | South Asia | East Asia ex GC | S. America | E. Europe & CIS | Oceania | All | |---|---|---|---|---|---|---|---|---|---| | High infrastructure or compute costs | 25% | 26% | 29% | 28% | 28% | 28% | 29% | 18% | 27% | | Security, privacy, or compliance concerns | 20% | 27% | 18% | 39% | 29% | 28% | 25% | 22% | 26% | | Ongoing maintenance and updates | 27% | 26% | 18% | 26% | 20% | 31% | 21% | 25% | 24% | | Complexity of deployment, hosting, or scaling | 27% | 24% | 19% | 24% | 11% | 30% | 26% | 25% | 23% | | Lack of specialised support | 17% | 16% | 21% | 31% | 24% | 23% | 23% | 32% | 22% | | Difficulty evaluating or comparing models | 14% | 17% | 14% | 23% | 16% | 26% | 25% | 18% | 18% | | Difficulty fine-tuning or customising | 22% | 18% | 18% | 20% | 11% | 22% | 18% | 12% | 18% | | Difficulty integrating into existing systems | 19% | 21% | 14% | 20% | 7% | 26% | 19% | 20% | 18% | | Insufficient documentation or learning resources | 18% | 15% | 15% | 17% | 15% | 20% | 24% | 15% | 17% | | Model performance is not good enough | 18% | 15% | 13% | 22% | 16% | 17% | 19% | 8% | 17% | | No major challenges | 9% | 21% | 16% | 5% | 14% | 4% | 8% | 12% | 12% | | Weighted sample size | 286 | 277 | 206 | 192 | 164 | 147 | 98 | 39 | 1411 | Nine layers and 48 components of the stack scored across 10 criteria (1–5). Click a layer to open its components: each carries its own criterion scores, maturity grade, open-vs-closed parity verdict, and surfaces some of its most-starred open-source projects. Hover any cell for detail. Open-weight AI is a commercial market at multi-hundred-billion-dollar scale, built by funded companies and run in production by global enterprises. Databricks crossed a $5.4B run-rate; Mistral scaled 20× to ~$400M ARR in twelve months; DeepSeek reached ~$220M ARR and recently raised $7.4B at a valuation over $50B. Five revenue models are proven at scale: hosted inference, enterprise platforms, on-prem licensing, fine-tuning services, and harness tooling. | Company | HQ | Layer | Disclosed funding | Valuation | Revenue signal | Leading investors | Stage | |---|---|---|---|---|---|---|---| | Databricks | USA | Enterprise platform | — | — | $5.4B run-rate | — | Pre-IPO | | DeepSeek | China | Frontier open weights | $7.4B | $50B+ | ~$220M ARR | Liang Wenfeng; Tencent; CATL; China National AI Fund | Private | | Mistral AI | France | Open weights + platform | $3.05B | ~$14B (talks at €20B) | ~$400M ARR, 20× YoY | ASML; a16z; Lightspeed; Nvidia | Private | | Moonshot AI | China | Open weights (Kimi) | $3.9B | — | — | Meituan/Long-Z; Alibaba; Tencent; HongShan | Private | | Zhipu AI | China | Open weights (GLM) | Undisclosed | Public | — | Public (HK IPO 2026); prior Alibaba, Tencent | HK IPO 2026 | | MiniMax | China | Open weights | Undisclosed | Public | — | Public (HK IPO 2026) | HK IPO 2026 | | Cohere | Canada | Enterprise / on-prem | $1.7B | — | Command A+ open-sourced May 2026 | Radical Ventures; Nvidia; AMD; Schwarz Group | Private | | Cerebras | USA | Compute | $2.1B | — | — | Fidelity; Atreides; G42; Tiger Global | Private | | Reflection AI | USA | Open weights | $2.13B | — | — | Nvidia; Disruptive; Sequoia; Lightspeed; DST Global | Private | | Together AI | USA | Inference cloud | $1.334B | — | — | Aramco Ventures; General Catalyst; Prosperity7; Nvidia | Private | | Hugging Face | USA | Hub | $400M | — | — | Salesforce; Google; Nvidia; IBM | Private | | LangChain | USA | Harness tooling | $260M | — | 126k+ stars, 60% dev share | IVP; Sequoia; Benchmark; CapitalG | Private | More than 70 national AI strategies are live. The strategic question has shifted from whether to have a national AI policy to which layer of the stack a country can own. Click a marker or a country below. The browser was the user agent of the open web: code on the user's side, negotiating with servers on their behalf. That role is being recreated one layer up. Above the model now sits the agentic harness — the orchestration loop, tools, memory, sandboxes, and permission model. It is where production difficulty concentrates, and where the open-vs-closed, owner-vs-renter contest restarts. Reversible and low-consequence. Fetching a document, querying a database, listing a calendar. These can largely be permitted by default; a bad read costs little and can be repeated safely. Side effects that are costly or irreversible. Sending a message, spending against a budget, modifying a record, executing a transaction. This is where confirmation, approval thresholds, cost caps and revocation must concentrate. They require owning the layers above it — the harness, the memory, the permission model — while those layers are still open. The 3.3% gap (at parity on coding, behind on reasoning and agentic), and open's OpenRouter token share, especially in agentic coding. Reverses if: token share stalls while the reasoning gap widens. The Terminal-Bench spread between lab-owned and independent scaffolds; MCP/A2A governance under the AAIF; the portable permission spec that still doesn't exist. Reverses if: the lab-harness lead widens, or a closed platform sets the permission standard first. Open-lab economics (ARR, raises, the Zhipu/MiniMax IPOs) against metered-pricing breakpoints (~2027–28), with sovereign capacity as counterweight. Reverses if: sovereign funding lapses or open-lab economics fail to scale. Tracked, not settled: misuse capability and how easily safety tuning strips from open weights; hard-friction zones, above all synthetic CSAM and NCII; whether NTIA's “monitor, don't restrict” holds. Reverses if: a major misuse event, or a shift from monitoring to restriction. There is a test you can run for the rest of this. Look at who is seated in the rooms where AI gets decided, and with what status. The day they seat the people who keep AI open, portable, and widely deployed on equal footing, the shift from renting to owning will have happened. The window is open now. It is closing slowly enough that we can pretend it isn't, and the lease is shorter than it looks. Build with us. In New Zealand's far north, a Māori broadcaster trains speech models for te reo — a language too small for any market — under a license that keeps the data with its people. PwC, one of the largest accounting firms in the world, fine-tuned an open model on the language of finance and runs it today for hundreds of clients, on its own hardware, with no per-token meter running. Researchers in Lausanne built an open medical model with the Red Cross, tuned to its humanitarian guidelines, and are preparing clinical trials at home and in Tanzania. In East Africa, farmers diagnose cassava disease with a model that runs on the phone itself, offline, in fields the cloud has never reached. In Switzerland, a public consortium trained a national model on public supercomputers and released all of it: weights, data, training code. None of them asked permission, and none of them could have rented this. They own it — that is the whole idea. Open-source and open-weight AI now anchor one of the fastest-growing builder ecosystems in the history of software. Hugging Face alone hosts 2.5 million public models and 13 million users. A third of the Fortune 500 are among them. On OpenRouter, where developers route real production traffic, open-weight models went from a sliver of usage to roughly a third by late 2025. Just six months later, the platform moves 25 trillion tokens a week — five times as much — and the largest single source of that traffic is an open model. Developers are responding to what the models can do and what they cost. And on both counts open has become the practical choice. This spring, the strongest closed model scored 60 and the strongest open model 54. A year earlier, the leading open model managed 22. Closed systems still lead on the hardest problems. But for what most builders actually ship — where price, control, and deployability matter — open models have crossed from promising to ready. Anyone still waiting for open source AI to grow up can stop waiting. It already has. Governments are moving, too. The European Commission has proposed an “open source first” rule for how public institutions buy AI, and Canada has set a national target to lift business adoption from 12 percent to 60. When communities, markets, and governments converge on the same thing at once, they are telling you where this is heading: toward more intelligence, in more hands, and owned by more people. None of this is inevitable, and the other future on offer is seductive. Picture a handful of validation machines reading the world back to you, smooth and confident and sourced to nothing you can check. The bazaar of a billion arguing voices is muffled by a polished concierge that answers to its owner. We got a preview this June, on a Friday afternoon, when one of the most advanced models went dark everywhere because a government sent a letter. Every business renting that model discovered an off switch that belonged to someone else. We have been here before. Mozilla exists because one company tried to own the front door to the web, and an open community rose up to make sure it never could. Twenty-five years later, someone is running the same play. We bet on open the first time. Open won. Together, we can do it again. Our belief is simple: the path forward is competition and interoperability. We believe in a world of many models, standard ways to plug them together, and the freedom to walk away from any vendor at any time. Open has a record here. It grew the pie and let more people own a slice of it. Read what follows as a map: where open AI is winning — some numbers surprised even us — and where it is exposed. A case that hides its weak points is an advertisement. The builders are already building. A rented future has deeper pockets; an owned one has more hands — millions more — and this story ends the same way every time it is told: the many, building in the open, outbuild the few behind walls. Build with us.