AI writer Ethan Mollick recently pointed out something easy to miss in the headlines about the biggest American models: a second tier of AI systems is improving just as fast, and it works very differently.

"But there is a second set of near-frontier AI models that typically lag 6-12 months behind the frontier, all of which are from China. These are open weights models, which means that anyone can use or modify them after release (as opposed to the frontier models which are proprietary). That makes them quite cheap to operate. They, too, are climbing up an exponential improvement curve, though lagging the American closed models."

Two terms in there are worth unpacking. "Frontier" means the best proprietary systems — the ones you pay a subscription or per-use fee to access, controlled entirely by the companies that built them. "Open weights" means the model's underlying numbers — the thing that makes it work — are published for anyone to download, run, and modify. You are not renting access; you are getting the thing itself.

The practical consequence is the one Mollick names: open weights models are cheap to operate. If a model that is roughly a year behind the frontier is good enough for your task, and it costs a fraction of the price, the economics of using AI change — for individuals, but even more for organizations running it at scale. A school, a small business, or a government office that balks at frontier pricing may find a near-frontier open model entirely adequate.

The improvement curve is the second half of the claim. These models are not standing still at "good enough." They are climbing on their own exponential trajectory, which means the gap between what is free or cheap and what is expensive keeps narrowing in capability terms even as it persists in time.

Who this is for. If you are a regular user of an AI assistant, the honest answer is: this mostly matters indirectly, at least for now. You probably will not download and run a model yourself — that still takes technical work and decent hardware. Where it touches your life is downstream: the apps, services, and workplaces around you get access to capable AI at lower cost, which tends to mean more AI features in more places, at lower prices. If your employer has been hesitant to roll out AI tools because of cost or data-privacy concerns — an open model can be run on your own machines, so data never leaves the building — this trend is the reason that calculation is shifting.

The people this matters to most directly are developers and IT teams, and it is worth saying so plainly: they are the ones who can actually grab an open weights model and put it to work today. For everyone else, this is a "know it is coming" development, not a "go do this" one.

Is it usable today? Yes and no. The models exist and are being released now — this is not speculative. But Mollick's framing is a preview of where things are heading, not a product you can pick up. He does not name specific models, cite benchmarks, or say what "cheap" means in dollar terms, so the 6–12 month lag and the cost advantage are his characterization, not measured figures.

One more honest caveat: "open" here means open weights, not fully open. You can use and modify these models, but how they were trained — on what data, at what cost — is generally not public. And a lagging model is still a lagging model; for the hardest tasks, the frontier keeps moving too.