I keep noticing something that is becoming harder and harder to ignore.
The more people use AI, the more writing begins to sound the same.
The tone smooths out. The edges soften. The structure becomes familiar. A certain style of “clarity” takes over. Arguments get cleaned up into managerial prose. Critique gets translated into balance. Conflict becomes “trade-offs.” Politics becomes “polarization.” Everything starts sounding just a little more professional, a little more optimized, a little more like it passed through the same cultural filter.
And at first, that can feel helpful. AI helps you organize your thoughts. It helps you move faster. It helps you write around fatigue, distraction, uncertainty, overload. I use it myself all the time. But that is exactly why this matters. Because the more useful AI becomes, the more power it has to quietly reshape not just what we write, but how we think, how we frame problems, and what starts to feel like common sense. In a lot of ways, this sits directly alongside what I wrote recently in The Age of Dependency: How We Lost Our Personal Sovereignty. What looked like convenience there increasingly looks, here too, like a surrender of intellectual autonomy.
That is where the deeper issue emerges.
AI bias is not only about whether a model leans “left” or “right.” It is also about which institutions become default authorities, which styles of language become normalized, and which political-economic worldview gets quietly built into the infrastructure of everyday thought.
From bias to infrastructure
Recently I asked AI a basic health question about hand, foot, and mouth disease. What came back was clean, confident, and heavily routed through the same institutional pipeline. CDC. CDC. CDC. That is the U.S. Centers for Disease Control and Prevention. But I am not in the United States. And even more importantly, why should one national institutional framework quietly become the default informational center for users everywhere?
This is not a minor technical issue. It reveals a larger pattern. Generative AI often defaults toward a narrow set of high-authority, English-language, institutionally dominant sources. Those sources are then laundered back to users as neutral knowledge. But they are not neutral. They are geographically situated, politically shaped, historically contingent, and increasingly unstable themselves.
So the problem is not simply that AI sometimes gets things wrong. The problem is that it can centralize authority while appearing merely to summarize it. And once you begin to see that, it becomes hard not to connect it to broader questions of sovereignty and power. Specifically, as most Tech companies are located in the United States, and the US's current geopolitical disposition is a seeming return to the colonial era, it is not hard to see colonial logic: one center sets the terms, everyone else is expected to receive and adapt.
That is already dangerous enough. But then there is the second layer: style.
The neoliberalization of language
A lot of generative AI does not just answer in a certain way. It writes in a certain way. The tone is familiar now: polished, managerial, “helpful,” controlled, non-threatening, mildly upbeat, and endlessly inclined toward optimization. It often sounds less like a writer and more like a hybrid of a consultant, platform designer, HR department, startup founder, and - in the worst cases - like a 'Tech Bro.'
That style is not politically innocent, as it carries a worldview. Within that worldview, social problems are reframed as design problems. Structural violence becomes a coordination issue. Inequality becomes inefficiency. Human beings become users. Institutions become platforms. Democracy becomes governance. And critique, if it appears at all, is softened into something manageable and professional.
Over time, that style begins to work on us. I can feel it in my own writing. The punctuation changes. The rhythm changes. The structure changes. I want narrative; AI pushes bullets. I want friction; AI pushes readability. I want sharper critique; AI often pushes it back toward something smoother, tidier, more acceptable - less critical.
So even when the ideas remain “mine,” they are increasingly being filtered through someone else’s tonal regime.
That should alarm us. Because this is not just a matter of technology. It is also a matter of political economy, as the issue is not simply that AI is biased. It is that the dominant models are emerging from a very particular capitalist world, and they carry that world’s assumptions back into our sentences.
When different AIs produce different worlds
And this becomes even clearer the moment you step outside the U.S.-centered AI model ecosystem.
If you use a Chinese AI system such as DeepSeek, you often encounter a different center of gravity. It is not the same Silicon Valley voice. It is not the same institutional defaulting. It is not the same moral vocabulary. You get less neoliberal-tech-bro speak, less startup-managerial framing, and more state-developmental or national-stability framing (even if the grammar can remain annoying consistent).
Of course, that comes with its own constraints. Criticism of the Chinese Communist Party or asking politically sensitive questions or topics may be restricted. So this is not some naive argument that Chinese AI is “free” while American AI is “biased.” Both are biased. Both are shaped. Both are governed. The point is that they are governed differently, and those differences reveal the politics buried inside the machine.
Once you see that, a bigger realization follows:
Why would every country, region, or culture not eventually want its own AI?
If AI is becoming part of the infrastructure through which people learn, write, interpret, diagnose, plan, and govern, then of course states and societies will want systems shaped by their own histories, languages, values, and priorities. This is part of why the world is moving toward what is increasingly being called “sovereign AI.” And that discussion links naturally not only to questions of empire, but also to democratic self-determination. If the core tools that a country's citizens are using to learn and express themselves is dictated by specific logics, what happens to to local tastes, flavors, and cultural containers? Nevermind to democracy in these locations more broadly, where the issue is not just who governs, but through what structures, whose voice, and under whose terms.
But isn’t this just another form of colonization?
I would argue yes.
Not colonialism in the old territorial sense. But colonialism as extraction, dependency, and epistemic rule.
Our language, habits, questions, and social life are continuously fed into systems we do not govern. Those systems are trained elsewhere, aligned elsewhere, funded elsewhere, and optimized according to logics that often have very little to do with the worlds many of us actually want to build. Then they come back to us as assistants, advisors, editors, and translators of reality into terms that are - or will be - slowly reshaping the ways we understand the world, how we learn, and the way we write - and therefore express ourselves.
That is a colonial relation.
It is extractive because human life becomes a raw material for model training and monetization. It is dependent because institutions begin relying on external systems to think, write, and know. And it is epistemically colonial because it installs external authority structures as the default architecture of truth. If older empire worked through direct rule, this version works through infrastructure, default settings, and the quiet standardization of knowledge - not too dissimilar to Vaclav Havel's description of communist Czechoslovakia as a subtle, "Post-Totalitarian" state.
So yes: this can absolutely become another form of colonization. A recolonization of language, style, knowledge, and interpretation through technical infrastructure rather than direct occupation. And whereas, Donald Trump and the US feels the right to go into Venezuela and do as it pleases, we are subtly letting this form of colonization into out minds and homes. Different terrain, different mechanism, but a strikingly similar problem: who gets to define the terms under which others live, think, and engage?
The danger of one empire and the danger of many
That does not mean the answer is simply “let every nation build its own AI” and call it solved.
There are two possibilities here. One is pluralism: multiple model traditions, multiple epistemic centers, multiple cultural framings, and the possibility of comparison, contestation, and real diversity in machine-mediated thought. The other is fragmentation: a world of competing ideology machines, each training citizens into its own curated reality, each reproducing its own silences and biases, each becoming a domesticated truth infrastructure.
So the goal cannot be neutrality. There is no neutrality here. The real goal has to be legible bias, plural epistemics, and contestable infrastructures.
What would a better AI ecosystem look like?
This is partly why the idea of building something different matters so much.
Not just another chatbot. Not just another assistant. But an AI shaped by another intellectual and ethical center of gravity. One that does not treat critique as an exception. One that does not default toward conforming and neoliberal managerial language. One that does not pretend the U.S. institutional universe is the natural center of knowledge. One that can compare frameworks rather than hide them. One that can show users which sources and value assumptions shaped an answer in the first place.
That would not eliminate bias. But it would make bias visible. And once bias becomes visible, it becomes arguable. Once it becomes arguable, it becomes political again. And that matters, because what is at stake here is not simply better search results or better prose. It is whether we build systems that deepen dependency, or systems that open space for critique, plurality, and self-conscious interpretation.
the core question is whether AI becomes a machine for intellectual homogenization, or whether it can be bent toward a more plural and self-aware knowledge politics.
Right now, too much of generative AI is moving in the first direction. It is smoothing language, narrowing expression, centralizing authority, and quietly normalizing one dominant way of seeing. If we are not careful, the great ideological victory of this era will not come through censorship in the classic sense.
It will come through "default settings."
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