On July 23, 2025, the long-anticipated U.S. AI Action Plan was finally released. Unlike the copy-paste summaries circling online, I’ve read the full 90-point document and compiled the most critical insights here—not just as a policy breakdown, but as a reflection on its ideological, industrial, and geopolitical ambitions.
Because “Winning the AI Race” is not just a slogan—this plan positions itself as an ideological alignment blueprint, a tech-industrial manifesto, and a roadmap for asserting digital power in a multipolar AI world.
Between Risk and Speed
The structure of the plan rests on a quiet but clear rejection of the European approach. Where the EU insists on “risk-based regulation,” the U.S. now aligns around a different axis: “innovation-first.”
Ethical concerns are replaced by talk of ideological non-intervention. Safety standards are loosened in favor of deployment speed. Even the definition of neutrality is being rewritten—not to balance different value systems, but to avoid embedding any.
And while China’s strategy leans on state-directed infrastructure, the U.S. doubles down on private-sector-led exports—especially for what it calls “full-stack AI systems.”
Three main pillars hold the plan together:
Innovation Acceleration: Cutting red tape, prioritizing federal procurement, and favoring models that meet a vague standard of “ideological neutrality.”
Infrastructure and Energy Mobilization: Loosening environmental checks, opening federal land to datacenters, and securing power supply for AI compute.
International Diplomacy and Security: Positioning AI as a foreign policy instrument, with tools like CAISI and new diplomatic AI groups serving as digital extensions of influence.
But as we’ll see in the next sections, behind this structure lies something deeper: a quiet recalibration of which values matter, who sets them, and who gets to opt out.
Neutrality or Designed Silence?
One of the most telling features of the plan isn’t what it proposes—but what it deliberately leaves out.
References to climate change, diversity (DEI), and disinformation have been stripped from official documents and guidelines. These aren’t cosmetic edits. They are ideological signals. When you remove these terms from federal frameworks, you’re not just editing language—you’re reshaping what counts as a legitimate concern in technical systems.
The plan frames this move as a step toward “ideological neutrality.” But neutrality, in this case, doesn’t mean openness to all viewpoints. It means exclusion of some.
It’s a shift away from embedding values into technology, toward a refusal to define values at all—as if that, somehow, is more objective. But as we know, values don’t disappear just because you stop naming them. They become implicit, invisible, and often harder to challenge.
Some have called this a form of cultural erasure. Even technical bodies like NIST are reportedly being asked to revise their safety documentation—removing references that reflect social risk or equity. It’s a quiet, bureaucratic redrafting of what it means for a system to be “safe.”
Meanwhile, the EU is moving in the opposite direction: embedding anti-discrimination duties directly into law. If that model insists AI should be fair by design, the U.S. model seems to suggest fairness itself is too political to define.
What we’re seeing here isn’t just regulatory divergence—it’s a deeper, epistemic
break. A disagreement not over rules, but over whether values belong in code at all.
Infrastructure Is Power
Of all the areas the plan touches, infrastructure is where its intentions are most direct—and least ambiguous.
The logic is simple: If the U.S. wants to lead in AI, it needs more datacenters, more chips, and more energy. Fast.
That means federal land is being opened for AI infrastructure. Environmental checks like NEPA are being relaxed. Electricity grids are being reprioritized to serve compute-intensive systems. And public subsidies are lining up behind what the plan essentially frames as strategic infrastructure buildout.
But this isn’t just about technology. It’s about sovereignty.
Because in 2025, AI leadership isn’t only about better models. It’s about where the data is stored, where the compute happens, and who controls the hardware stack. That’s the new geography of digital power.
If compute is offshored, so is influence. If datacenters move abroad due to energy constraints or slow permitting, the U.S. loses not just innovation capacity—but geopolitical leverage.
That’s why the plan places physical infrastructure at the center of its AI vision. With the CHIPS Act expanding, classified datacenter projects growing, and grid modernization moving up the priority list, it’s clear: This is no longer just about algorithms. It’s about territory.
And yet, one piece is still missing: governance.
Who Watches the Buildout?
Right now, we’re seeing datacenters go up, energy capacity expand, and AI workloads surge. But the systems being built lack a shared governance framework—legal, ethical, or democratic.
What we have instead are voluntary transparency initiatives.
Yes, companies like Anthropic and Frontier AI Labs have released safety disclosures and model evaluation plans. These are useful steps. But they are self-designed, self-enforced, and non-binding.
Governance, in this model, becomes opt-in.
And that raises a risk we’ve seen before: transparency as branding. When accountability is defined by those being held accountable, it’s not really oversight. It’s reputation management.
The infrastructure may be material—but the governance, for now, remains entirely discretionary.
The Governance Gap: Why Is the FTC So Quiet?
One thing the plan doesn’t say speaks almost as loudly as what it does.
There is hardly any mention of the Federal Trade Commission (FTC)—a notable omission, especially given the Commission’s earlier investigations into AI-related risks, including algorithmic harm, deceptive practices, and market concentration. Some of those policy documents appear to have been removed or quietly archived. And within the plan itself, the FTC is reduced to vague references rather than a clear enforcement role.
That absence feels intentional. And it raises questions.
Because competition isn’t a peripheral concern in AI policy—it’s foundational.
If enforcement tools like antitrust are downplayed or deferred, the space quickly narrows. Innovation risks becoming exclusive to those with the scale to train large models, secure compute, and shape standards. Oversight, in turn, becomes less about public accountability and more about corporate disclosure.
In contrast, the European Commission is moving in a different direction. Under the AI Act, large developers are being designated as systemic actors—subject to specific obligations and increased scrutiny. The message is straightforward: if your models shape the market, you don’t just operate in it—you help define it.
The U.S. plan doesn’t articulate that view. If anything, it steps back from it.
That doesn’t mean competition is no longer a concern. But it does suggest that governance is being reframed—not through regulation, but through alignment with industrial growth. And that reframing may leave little room for more structural forms of oversight.
A Quiet Internal Conflict: Federal vs. State
There’s one part of the plan that doesn’t need to raise its voice to be understood. The message is already clear:
federal resources will now favor states that don’t slow things down.
States that pursue more cautious or rights-based AI regulations—such as California—may find themselves cut off from federal funding streams. The mechanism is indirect, but the incentive structure is unmistakable: if you attempt to introduce restrictions, you may lose access to infrastructure support.
In practice, this creates a new kind of friction within the U.S. regulatory landscape.
It’s also a quiet continuation of a legislative effort that didn’t succeed publicly. A few weeks before the Action Plan was released, a sweeping federal bill known informally as the One Big Beautiful Bill aimed to prevent individual states from setting their own AI rules. That bill didn’t pass. But parts of its intent seem to have found their way into this plan—just through different means.
The implications are structural.
In a system where each state begins to define its own thresholds for transparency, accountability, and safety, consistency becomes difficult. Developers face a fragmented compliance map. Users face varying levels of protection. And investors operate without a clear regulatory horizon.
The risk here isn’t just policy divergence—it’s coordination failure.
For a technology as foundational as AI, fragmentation at the regulatory level can undermine not just enforcement, but trust. Especially when the federal government signals that restraint will be penalized—not debated.
Exporting Norms: Toward an AI NATO?
There’s a clear shift in how the U.S. now talks about AI internationally. It’s no longer just a technology to be shared—it’s a system to be exported. Not only in the commercial sense, but as a framework of values, infrastructure, and governance assumptions.
The Action Plan positions AI as a tool of foreign policy. Full-stack AI systems—bundled with software, hardware, data infrastructure, and safety protocols—will be promoted to allies through diplomatic and trade channels. New offices and working groups are being formed to support this agenda. And the language around it is deliberate: these exports are meant to be trustworthy, interoperable, and secure.
But trust and interoperability are not neutral terms. They carry design choices. They reflect the value systems of the institutions that build and maintain them.
In this context, the U.S. is developing what could be described—at least in direction, if not in name—as an AI NATO: a digital alliance model where adopting American AI tools also means aligning with American frameworks for safety, transparency, and platform behavior.
The plan doesn’t frame it this way explicitly. But the structure is there.
And this brings it into contrast with two other global approaches:
China is offering vertically integrated, state-backed AI infrastructure—complete with financing and turnkey deployment, particularly across the Global South.
The EU is embedding human rights and democratic accountability into its AI governance model, emphasizing digital sovereignty and enforceable obligations.
The U.S. sits somewhere in between—but with a strong leaning toward industrial alignment over normative coherence. And while the plan speaks often about openness, it remains unclear whether access to U.S. models will be conditioned on certain policy alignments—such as removing terms like disinformation or climate risk from national strategies, as we’ve seen domestically.
If that logic is extended outward, ideological alignment becomes part of the export package. That’s not just diplomacy—it’s norm transfer.
And that raises questions not only for geopolitics, but for global digital rights. If the AI systems shaping economies also carry invisible values, then the battle isn’t over whose technology leads—it’s over whose assumptions about fairness, agency, and truth are embedded at scale.
Workforce, but Not the World
One of the plan’s more pragmatic sections focuses on domestic workforce transformation. Through initiatives like AI Workforce Labs, the government aims to reskill workers for an AI-driven economy—especially in areas like procurement, federal services, and technical operations.
There’s an emphasis on public–private partnerships, education programs, and labor upskilling. In this sense, the plan treats AI not just as a technology challenge but also as a labor market transition.
But here too, something feels incomplete.
For a country that has long positioned itself as a magnet for global talent, there’s almost nothing about international recruitment. No mention of AI visas. No incentives for cross-border talent pipelines. No acknowledgment of the competitive landscape in which other countries are actively designing immigration strategies to attract AI researchers, engineers, and founders.
Canada’s AI Talent Stream and France’s startup-friendly immigration policies stand out as contrasts. Both countries are building frameworks that understand talent as infrastructure—something to be planned for, supported, and integrated into national growth strategies.
The U.S. plan doesn’t ignore labor. But it narrows the scope of that conversation to domestic reskilling. That may reflect political caution. Or it may be a strategic blind spot.
Either way, the result is the same: a global AI race with a national labor lens.
And that lens may not be enough—especially as research labs, startups, and even large-scale development teams become increasingly transnational in how they operate.
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Türkiye’s Question: Between Two Poles, Is a Third Way Possible?
The U.S. Action Plan makes one thing clear: the global AI landscape is no longer just about regulation. It’s about alignment—industrial, ideological, and strategic.
This creates pressure not only for developers and regulators, but also for countries that are navigating both partnerships and autonomy. For Türkiye, the question is not simply whether to align with one bloc over another. It’s whether a third path—between compliance and imitation—is still possible.
On one side, there is the U.S. ecosystem. Open-weight models, commercially led infrastructure, and a growing export strategy that encourages interoperability—on U.S. terms. For startups and public agencies working with open models, this can mean faster integration into global supply chains. But it can also bring unspoken dependencies, especially around values and standards.
On the other, the EU offers a more legally defined model. Through the AI Act and related instruments, there is growing regulatory gravity toward rights-based governance—transparency, fairness, non-discrimination. Given Türkiye’s Customs Union and ongoing technical harmonization with the EU, these standards are already beginning to shape procurement, funding eligibility, and public digital services.
But alignment isn’t the only option. It’s also possible to design with intent.
For Türkiye, this could mean building a hybrid framework—one that doesn’t treat innovation and governance as trade-offs, but designs both in parallel. A framework that keeps space open for model experimentation while building durable mechanisms for audit, accountability, and risk oversight.
It also means preparing now for the questions that will define the next phase:
Who will govern public AI infrastructure?
How will cross-border data flows be structured?
What mechanisms exist for independent model review?
And what is Türkiye’s long-term strategy for talent—both domestic and international?
Global examples offer reference points: Canada’s visa model, France’s AI startup ecosystem, the EU’s regulatory sandboxes. But ultimately, Türkiye’s position will depend on whether it frames AI policy as reactive alignment—or as strategic authorship.
Because what’s being shaped right now isn’t just code or infrastructure.
It’s the conditions for trust.
And the architecture of digital sovereignty.
Disclaimer
This is a personal analysis and does not reflect the views of any organization I am affiliated with.