The government’s brief declares that it would be “significantly more difficult to develop a robust AI industry” if frontier labs need to enter into licensing agreements. But dozens of agreements have already been announced between publishers and those frontier labs, most notably OpenAI. In fact, the government concedes, in a footnote, that it “takes no position on whether a licensing regime would be financially or logistically feasible” and that “both mainstream and independent publishers could enter (and have entered) into licensing agreements.” So, where exactly is the difficulty? What onerous obligation are we placing on frontier labs that would slow their progress?

In the past 18 months, a number of services have been launched to make licensing even more turnkey, streamlined, and efficient. Cloudflare, Tollbit, Really Simple Licensing, and the News Media Alliance, to name a few, have created platforms that make royalty payments simple. This isn’t surprising because the history of media contains numerous examples of effective licensing frameworks, like ASCAP and BMI, being created to address this very need. These systems have proven to be rewarding for both large companies and individual creators, addressing the government’s fear that licensing would “disproportionately benefit legacy media outlets.” The publishing industry just needs willing counterparties. This has been done before, can be done today, and is not difficult.

Another point the government makes is that frontier labs based in countries that are foreign adversaries would not be “encumbered” by licensing requirements. Likely true. But do we want to abandon our respect for intellectual property just because our adversaries have? And, again, the “encumbrance” here is not logistical – we ought to reject the notion that a licensing regime is some kind of complex speed bump for American AI. This is simply about costs.

And, there’s the government’s most sweeping statement about the market dynamics here: “only the largest technology companies might have the capital necessary to pay licensing fees.” There’s no effort to estimate the capital required for licensing or compare it to what’s being spent on data centers, chips, and power. OpenAI alone has told investors it expects to spend $750 billion on compute by 2030. It’s why there are only a handful of large-language model companies in the world with access to the capital required to create frontier models. The barrier to entry here has nothing to do with publisher licenses, and if the government were interested in stimulating LLM competition, it might look at the costs of compute and energy. It’s why startups in the space are building on top of these models, not trying to compete with them.

For context, royalties and licensing payments in the U.S. music industry are under $20 billion per year. If similar amounts were paid to news publishers, it would represent a rounding error to LLM companies’ overall expenses, but be meaningful for content producers of all sizes. A sensible fee structure creates a flywheel of quality inputs and quality outputs, benefiting the AI consumer and the public good.

Here’s a final market reality: LLM companies routinely pay for data they can’t scrape. There are companies like Scale AI raising billions of dollars to acquire and sell that training data exclusively to frontier labs. Without a licensing framework, publishers will increasingly erect barriers and paywalls to restrict their own content and extract those fees. This imposes substantial costs to consumers, limits access to free information and content on the open web, and reduces pathways to earnings for writers and content producers. All because we allow the false assumption that licensing is hard and expensive to go unchallenged.

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