The Trump administration released its National Policy Framework for Artificial Intelligence on March 20 — a four-page document that tells Congress what the White House wants federal AI law to look like, and signals clearly what it doesn't want: state-level regulation, a new federal AI agency, or courts deciding that AI training violates copyright law.

The framework isn't binding. It's a legislative wish list that still needs Congress to act. But it maps the administration's priorities across seven areas, and for publishers and media companies, two of them matter most.

On intellectual property, the White House punts to the courts while tipping its hand. The document states the administration "believes that training of AI models on copyrighted material does not violate copyright laws" — then says it supports judicial resolution. That's a tell. For publishers currently suing OpenAI, Google, and others over training data — including Encyclopedia Britannica's recent suit against OpenAI and News Corp's ongoing case against Perplexity — the administration has effectively signaled it's rooting against them. The framework does contemplate collective licensing frameworks and protections against unauthorized replicas of people's voices and likenesses, but the core fair use question is left to judges who now know where the White House stands. That's also bad news for publishers pushing for statutory licensing models as a structural solution.

On federal preemption, the framework pushes hard to override state AI laws that "impose undue burdens" on a national strategy for "global AI dominance." The immediate target is Colorado's AI Act — the first state law requiring impact assessments and transparency for high-risk AI deployments — which was already delayed from February to June 2026 under industry pressure. The framework would put federal law above a patchwork of state rules, effectively neutering the most aggressive state-level efforts to regulate AI behavior. It's the opposite direction from the EU AI Act, which the administration's framework implicitly positions itself against.

The rest covers child safety — age verification and deepfake protections via the Take It Down Act, which targets non-consensual intimate imagery — infrastructure buildout, workforce development, and a preference for regulatory sandboxes and industry-led standards over a new AI regulator. The deepfake protections are notable given Grok's ongoing global regulatory scrutiny over sexualized AI imagery, though the framework addresses individual harm rather than platform accountability.

The overall posture is: light-touch federal rules, no new agency, and existing sector-specific regulators handle the rest. The contrast with the EU is deliberate. The administration's framework is a bet that the US approach — let companies build, let courts sort out the edges — will outcompete Europe's more prescriptive compliance regime. For publishers, that bet means the most important AI policy battles are now happening in courtrooms, not legislatures — and the traffic consequences of losing those battles are already here.

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A federal judge has dismissed an antitrust lawsuit brought by two Arkansas news publishers against Google, ruling that they lack standing to sue over the general search market and that key claims are too old to pursue — even as the judge acknowledged Google’s monopoly power in blunt terms.

U.S. District Judge Amit P. Mehta, writing in a 41-page opinion, dismissed the case filed in 2023 by Helena World Chronicle and Emmerich Newspapers. The publishers argued Google had leveraged its search dominance to effectively become “America’s largest news publisher” — monopolizing the online news market, diverting their traffic, and depriving them of licensing revenue without paying for it.

Mehta didn’t dispute the underlying picture. “The acquisitions of Android, YouTube, and DeepMind may very well have been anticompetitive,” he wrote. “But Plaintiffs are 10 to 20 years too late; those claims are now stale.” The conduct that built Google’s dominance happened too long ago to sue over. The statute of limitations swallowed the core of the case.

On the market definition, Mehta found a structural problem: the publishers aren’t competitors in the general search market — they’re publishers of news content. Their alleged injuries (lost traffic, lost licensing fees, higher production costs) happen in the news publishing market, not the search market. That mismatch undercut their antitrust standing. The judge also dismissed the tying claim — that Google forced users into its search ecosystem as a condition of using Android — finding it inadequately pleaded.

The ruling is appealable, and the case remains on file with the U.S. District Court for the District of Columbia.

The outcome is discouraging but not surprising. Small regional publishers face enormous structural barriers in antitrust litigation against Google — the legal bar for market definition and monopoly power in adjacent markets is high, discovery is expensive, and the statute of limitations problem is real. The conduct that matters most happened years ago, and courts have been reluctant to retroactively reach it.

It’s worth noting that Judge Mehta is the same judge who ruled last year that Google illegally maintained its search monopoly in the DOJ case — so his skepticism of the publishers’ theory here isn’t a vindication of Google, just a finding that these particular plaintiffs, with this particular theory, didn’t clear the legal threshold. The broader question of what Google’s search dominance means for publishers is very much unsettled. The appeals court will have the final word.

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Most newsrooms that have adopted AI policies have done something admirable — and insufficient. A new briefing from the Center for News, Technology & Innovation synthesizes 30 peer-reviewed research papers on AI governance and finds that existing policies get the principles right but skip the operational details journalists actually need to follow them.

The CNTI report, released Feb. 17, is the third briefing from the organization’s AI and Journalism Research Working Group. Reviewing policies from 52 global news organizations across 12 countries, researchers found that newsrooms consistently prioritize transparency about AI use, human supervision of AI tools and human verification of outputs. But few policies define what “appropriate” or “proper” AI use actually means in practice.

The gap matters. As one example, AI translation tools can introduce gender biases — assuming doctors are men, nurses are women — that a journalist using a third-party tool may never catch. Existing policies focus on AI outputs, not the systems that produce them, making these subtle errors nearly invisible.

The procurement blind spot is arguably the bigger problem. Researchers found almost no AI policies that address how newsrooms should evaluate, contract with or monitor third-party AI vendors. A 2025 study of 16 AI tool contracts found that most gave developers the right to change terms of service without notice — a risk most individual journalists aren’t even aware of. Meanwhile, newsrooms’ growing reliance on tools built by Google, Microsoft and Amazon deepens their dependence on the same platform companies that already control much of their distribution.

The policy gap isn’t limited to the Global North. A Thomson Reuters Foundation survey of 221 journalists in the Global South found that roughly 80 percent said their newsrooms have no AI policy at all. That number has likely improved since the survey was conducted, but the structural barriers — no access to technical expertise, difficulty getting organizational buy-in, the pace of technological change — haven’t gone away.

The working group’s practical recommendation: treat AI policy development the way you treat coverage decisions. Include people with different job responsibilities and lived experiences. Draw on the lessons of earlier technology policy cycles — photo editing, social media — where the same tension between values and operational specifics played out. And start thinking seriously about procurement: what your AI contracts actually say, who can change them, and whether your organization has the leverage to push back.

For most newsrooms, the answer to that last question is no — but knowing that is the first step toward addressing it.

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