# Two Million Open AI Models, but Most of the Attention Goes to Just 200 > Source: > Published: 2026-08-20 14:24:37+00:00 Every time I open Hugging Face, the model ecosystem seems to have expanded again. There are new base models, fine-tunes, quantisations, adapters and experimental releases arriving constantly. At first glance, this looks like an incredibly diverse market. We have more models, more developers and more ways to run AI than ever before. But when I looked at where the downloads actually go, I found a much more concentrated ecosystem hiding beneath all that abundance. Hugging Face reported that its platform grew to 13 million users, more than two million public models and over 500,000 public datasets in 2025. That is a huge amount of activity. It also shows that developers are doing far more than downloading foundation models. They are building specialised versions, adapters, benchmarks and applications around them. The distribution of attention, however, is extremely uneven. Approximately half of the models on Hugging Face have received fewer than 200 downloads. Meanwhile, the 200 most-downloaded models—roughly 0.01% of the models available—account for 49.6% of all downloads. So we have millions of choices, but a surprisingly small group of models captures almost half the activity. I do not think this concentration is automatically bad. Developers tend to gather around models that work well, have clear documentation and are supported by popular inference tools. That creates useful network effects. Bugs are identified sooner, integrations improve and community knowledge becomes easier to find. The problem begins when we confuse repository count with meaningful choice. A model may technically be available but still be difficult to deploy because it lacks maintained serving templates, current dependencies or reliable quantisations. Another may perform well in a benchmark but have a restrictive licence. Some experimental fine-tunes may never receive another update. In other words, the usable ecosystem is much smaller than the visible catalogue. When I evaluate an open or open-weight model, I no longer look only at its benchmark scores and parameter count. I also want to know what exists around it. I look for signals such as: These signals do not prove that a model is production-ready. But they tell me whether I am selecting an isolated repository or joining an ecosystem that other developers are actively improving. That distinction matters when a model becomes part of a product rather than a weekend experiment. Download volume is one useful signal, not a final verdict. A highly specialised medical, legal or regional-language model may have modest downloads while still being extremely valuable to its intended users. Hugging Face itself describes open AI as a collection of overlapping sub-ecosystems rather than one uniform market. The opposite is also true. A widely downloaded model is not automatically the right choice for every workload. Popularity cannot tell me whether it meets my latency target, fits the GPUs available to me or behaves reliably with my application’s prompts. I normally begin with a manageable shortlist. A [practical overview of leading open model families](https://www.fluence.network/blog/best-open-source-llm-models/) can help narrow the field, after which I compare the candidates using the workload they will actually handle. That process gives me something a general leaderboard cannot: evidence that the model works inside my system. The concentration of downloads also creates a less obvious risk. Thousands of applications may depend on a relatively small number of base models, maintainers and supporting tools. If one licence changes, a repository becomes inactive or a critical component develops a vulnerability, the effects can spread well beyond the original project. That makes model selection resemble software supply-chain management. Teams need to understand where a model came from, which components it depends on, how actively it is maintained and whether they could migrate if necessary. Downloading the weights gives teams more control than relying exclusively on a closed API, but it does not eliminate dependency. We do not have a shortage of models. We have a shortage of dependable ways to determine which ones deserve long-term trust. The next phase of the open AI ecosystem will not be defined only by how many models get published. It will depend on how well developers can evaluate, maintain, secure and deploy them. Two million models sounds like unlimited choice. In practice, the models that matter are the ones surrounded by enough evidence, tooling and community support to survive contact with production.