Beyond the West: How National Values Shape AI’s Future
How Healthcare Models Reveal the AI Worlds to Come
More Than Code: AI Governance and the Soul of a Nation
When we talk about the future of artificial intelligence, we tend to talk about algorithms, regulations, and innovation. But underneath it all, AI governance is not just about what we build—it’s about who we are.
And right now, the loudest voices in this conversation come from one corner of the world: the Anglosphere, particularly the United States. The tone? Often anxious. The future? Cast in shadows. Will AI take our jobs? Will it erode our freedoms? Can we survive it?
But these questions—and the fears they reflect—are not universal. They’re shaped by deeply embedded cultural values, social contracts, and economic philosophies. And if we want to understand how AI will really unfold across the world, we need to widen the lens.
This essay argues that a powerful, underused way to do this is through a surprising proxy: healthcare systems.
Healthcare may seem far afield from AI, but it’s a mirror—reflecting each society’s beliefs about responsibility, solidarity, and the role of the state. It reveals how a nation treats its people in moments of vulnerability—and that, in turn, reveals how it may treat them in an AI-transformed world. Will AI be a tool of liberation, or another vector for precarity? Will it widen inequality or enable collective flourishing?
By comparing how different countries approach healthcare—from America’s market-driven model to the universalist frameworks of Europe, Japan, and Singapore—we begin to see not just policy differences, but diverging futures. Futures built on fear, or built on trust. On scarcity, or on solidarity.
What follows is not just a comparison of systems—it’s a meditation on values, and how those values will shape the AI paths we take.
The Healthcare System as a Cultural Mirror
What we do when people are sick says a lot about who we are. There is perhaps no moment more vulnerable in a person’s life than when their body fails them. In that moment, they don’t just meet doctors and nurses—they meet the values of their society. Do we treat illness as a shared human challenge, or a private economic burden? Do we rally around the unwell, or let them navigate the system alone?
This is where the hidden moral architecture of a nation reveals itself.
Healthcare systems crystallize the tension between individualism and collectivism, between market efficiency and universal dignity. Some nations prioritize personal responsibility above all. In these systems, healthcare is something you earn, not something you’re owed. Coverage is often tied to employment or wealth. In this view, falling ill is not just a medical event—it can be a financial one, or even a moral one. Did you save enough? Did you work hard enough to deserve care?
In contrast, other nations treat healthcare as a baseline human right. These systems are built on solidarity—on the belief that the health of each individual is intertwined with the well-being of the whole. You don’t need to “deserve” care; you get it because you’re human. Efficiency matters, yes—but not at the expense of dignity.
These different logics—individualist vs. collectivist, market-first vs. rights-first—are not just policy choices. They are cultural signatures. And they don’t disappear when the conversation shifts from healthcare to AI. They travel with us.
These same tensions will define how we respond to AI’s disruption. Because AI, like healthcare, deals in power and vulnerability. Who benefits when automation replaces human labor? Who is protected when industries are reshaped overnight? Will AI be used to lift people up, or to sort and exclude them? The answers to these questions won’t be determined solely by technology—they’ll be shaped by the same values that define our response to illness.
If we see people as lone economic actors, we’re likely to approach AI governance in ways that emphasize personal adaptation, competition, and “upskilling.” If we see people as interdependent citizens, we’re more likely to design AI systems that prioritize fairness, inclusion, and shared prosperity.
So if you want to understand a country’s likely path in an AI-transformed world, don’t just look at its tech companies. Look at its hospitals. Its insurance forms. Its waiting rooms. Look at how it treats people when they’re weak.
That’s the mirror. And it reflects more than you think.
And nowhere are these tensions more visible — and more consequential for the AI era — than in the United States.
Case Study – The American Model: Precarity as Policy
In the United States, illness is not only a physical condition—it is a financial and social reckoning. According to a KFF Health News investigation, 41% of U.S. adults—over 100 million people—are burdened with medical bills they cannot pay. Healthcare, far from being a shared social good, is often treated as a commodity: something to be earned, negotiated, or rationed based on employment, income, or sheer luck.
The consequences of this model go far beyond the emergency room. They shape the American psyche. In a system where one bad diagnosis can derail a family’s financial stability, health becomes a form of personal accountability. Are you covered? Did you make the right choices? Did you hustle hard enough?
This is the American model in action: a system where instability isn’t accidental—it’s engineered.
This same ethos permeates how many Americans now talk about artificial intelligence. AI is not simply a tool—it’s a looming threat. A destroyer of jobs. A consolidator of wealth. A black box operated by billion-dollar tech firms. The narrative isn’t just dystopian—it’s deeply personal. Because in a society where basic well-being hinges on economic participation, the idea of mass automation reads less like progress and more like a slow, systemic disqualification from society.
In the American model, you work or you don’t eat. You earn or you’re erased.
So when AI begins replacing white-collar tasks, or when platforms begin profiting from synthetic labor, it sparks fear—not just because change is scary, but because there is no safety net. No guarantee of care. No robust fallback plan. Automation doesn’t just threaten income; it threatens access to healthcare, to housing, to dignity itself.
Contrast this with countries where healthcare is untethered from employment, and the fear calculus shifts. In those systems, losing a job may be destabilizing—but it’s not catastrophic. In the U.S., it’s existential.
This precarity drives a distinctly anxious AI discourse. It’s why conversations about Universal Basic Income are often framed not as social innovation, but as “handouts”—an admission of failure or dependency. It’s why AI governance debates frequently focus on harm prevention and catastrophic misuse rather than shared benefit and public empowerment. It’s why so many people talk about being left behind—because in America, being left behind doesn’t mean discomfort. It means ruin.
This anxiety is reinforced by America’s dominant narrative of individualism. There is a deep cultural resistance to collective solutions. The mythos of the self-made person, the cowboy innovator, the bootstrap success story—these stories fuel policy paralysis. If everyone is supposed to fend for themselves, then social safety nets appear not as acts of care, but as threats to autonomy.
And so, as AI threatens to reshape the labor market, the prevailing response is not collective preparation—it’s individualized scrambling. Retraining programs. Productivity hacks. Side hustles. The burden, once again, falls on the individual. Just as with healthcare, if the system won’t catch you, the onus is on you to never fall.
This is why the American imagination around AI often feels like a survival manual, not a blueprint for thriving. It’s not because Americans are less capable of optimism. It’s because the scaffolding for collective optimism has been stripped away. And just as an uninsured patient might approach illness with dread rather than hope, many Americans approach AI with a sense of looming inevitability rather than possibility.
But this worldview isn’t global. It’s not even inevitable. It’s a product of specific choices, ideologies, and policies. And when we examine how other nations have structured their care for citizens, a different kind of AI future starts to emerge.
Case Study – Solidarity in Practice: Canada, Japan, and Beyond
In countries where healthcare is treated as a right rather than a privilege, a different kind of social contract takes shape—one rooted in trust, mutual care, and the belief that society has a role in cushioning life’s uncertainties. Illness, in these places, is not a moral failing or a financial collapse. It is something expected, prepared for, and collectively managed.
Take Canada, where healthcare is funded through general taxation and delivered as a publicly guaranteed service. No one has to worry whether their job comes with medical benefits. There are flaws, of course—wait times, regional disparities—but the foundational promise is intact: if you get sick, you’ll be cared for. This assurance, quiet and often taken for granted, shapes how Canadians approach technological disruption. Change may still be disorienting—but it’s not synonymous with personal ruin.
Or look at Japan, where universal health coverage has been in place for decades. The system blends public regulation with private service provision, offering care at low cost while maintaining high standards. More than 70% of the population expresses trust in its healthcare institutions. That trust bleeds outward—into how the Japanese public approaches automation, robotics, and AI. Japan has some of the highest rates of industrial automation in the world, with 419 robots per 10,000 employees in 2023. Yet, unlike in more precarious contexts, this level of automation isn’t widely seen as a threat. Instead, AI and robotics are often discussed in terms of how they can assist an aging population, relieve overworked caregivers, or extend the reach of rural health services.
The difference is not just policy—it’s philosophy.
In these countries, care is not conditional. Worth is not transactional. This fosters a baseline emotional security that’s easy to underestimate until you’ve lived without it. And when that emotional security is in place, society can take risks. It can experiment. It can look at the rise of AI and ask: How can this serve the common good?
This outlook extends beyond healthcare into wider policy circles. In many European nations, where universalism is the norm, proposals like Universal Basic Income (UBI) are not viewed with the same suspicion they often face in the U.S. UBI isn’t necessarily seen as charity, or as compensation for obsolescence—it’s discussed as an elegant continuation of long-standing social support principles. An update, not an overhaul.
In these contexts, AI is more likely to be framed as a partner to public institutions, not a replacement for them. Policymakers are more inclined to ask how AI can be used to improve healthcare outcomes, streamline public services, or enhance education access—because the foundational assumption is that these services should exist in the first place.
Solidarity creates space for imagination. When people don’t live in fear of destitution, they are better able to envision futures worth building. When systems don’t punish vulnerability, technological change becomes less threatening—and more inspiring.
Of course, none of these nations are utopias. They each face their own demographic, economic, and political pressures. But the key difference is the starting point: a presumption of care, not competition. A belief that society has a role in ensuring that no one is left behind—not just rhetorically, but structurally.
This belief doesn’t erase the challenges of AI—it just reframes them. Instead of asking, “How do we prevent AI from destroying livelihoods?” the question becomes, “How can AI help us care for one another better?”
And that question leads to a very different future.
Against the Myth of a Monolithic AI Future
For all the talk of artificial intelligence as a universal force—one that will “transform humanity,” “reshape the workforce,” or “redefine society”—we rarely stop to ask which humanity we’re talking about. Which workforce. Whose society.
The truth is: there will be no singular AI future.
Just as there is no single way to organize healthcare, there is no culturally neutral way to govern, implement, or interpret AI. Technology doesn’t exist in a vacuum—it moves through the world wearing the clothes of the culture that shaped it. It inherits our assumptions. Our anxieties. Our ambitions.
What works in San Francisco—with its libertarian undertones, venture-capital speed, and “move fast” mentality—may falter in Helsinki, where public trust, civic design, and slow, consensus-based policymaking are cultural cornerstones. An AI model designed for a hyper-competitive, gig-based economy might feel alien or even predatory in a country where labor rights and collective bargaining are deeply ingrained.
Similarly, an AI governance framework built around centralized federal authority might clash with societies that prioritize decentralized or subsidiarity-based decision-making. What looks like “progress” in one country may look like “overreach” in another. And what some call “handouts,” others will recognize as overdue infrastructure for dignity.
This is why pluralism isn’t a luxury in global AI governance—it’s a necessity. Without it, AI policy becomes brittle: a top-down export of one cultural vision, doomed to fracture the moment it touches a society with different values. We’ve seen this before with international development initiatives, global trade policy, even climate frameworks. One-size-fits-all prescriptions often lead to resistance—not because the technology is bad, but because the implementation ignores the fabric of local life.
It’s tempting, especially in tech circles, to believe in “the optimal solution”—a clean, elegant blueprint that can be replicated worldwide. But humans don’t live in blueprints. They live in histories. In languages, in religions, in colonial legacies, in family structures, in trust or mistrust of institutions. They live in the cumulative emotional weight of how they’ve been governed, who they’ve been failed by, and what they believe society owes them.
So when we imagine the future of AI, we must resist the urge to flatten. We must make space for a plurality of AI futures, shaped by local values, driven by cultural nuance, and grounded in lived realities.
This doesn’t mean surrendering to chaos. It means designing systems that can flex and adapt. It means inviting voices to the table that don’t sound like Silicon Valley. It means recognizing that building AI for humanity means building for many human experiences—not just one dominant narrative amplified by wealth, media, or English-language discourse.
Because if AI is to serve us all, it must be able to belong to us all.
The Global Consequences of American Pessimism
America’s influence over global discourse is vast—and artificial intelligence is no exception. Through its tech giants, media channels, think tanks, and academic institutions, the U.S. effectively sets the tone for how the world talks about AI. The language of disruption, obsolescence, existential risk, and job loss is not just domestic commentary—it’s a broadcast, translated and amplified across borders.
And yet, this narrative is deeply shaped by America’s own internal contradictions: a fragile social safety net, privatized healthcare, polarized politics, and a culture of hyper-individualism. It makes perfect sense that many Americans look at the coming wave of AI technologies and feel dread. But the danger arises when that national anxiety is mistaken for a global truth.
Because here’s the thing: not every society shares the same fears.
In countries with stronger welfare systems, higher trust in public institutions, and a more collective approach to risk, the dominant questions about AI sound different. They’re not just, “How do we survive this?” They’re also, “How do we shape this to serve us all?”
But these perspectives often struggle to reach the center of the conversation. The platforms through which AI futures are imagined—major conferences, policy papers, Silicon Valley product launches, media op-eds—are overwhelmingly Anglophone, and disproportionately American. Even well-meaning international collaborations often adopt U.S. frameworks as their default, subtly aligning global regulation with American assumptions about markets, innovation, and individual responsibility.
The result? A feedback loop. The U.S. projects its fears. The world absorbs them. And slowly, other visions fade from view—not because they’re flawed, but because they’re quiet. Because they don’t come with billion-dollar branding budgets or viral TED Talks.
This isn’t just an imbalance—it’s a missed opportunity.
When we allow one country’s pessimism to set the tone, we narrow the field of imagination. We stop asking what AI could do for public services, for climate action, for shared prosperity—because we’re too busy preparing for collapse. We ignore the models of cautious optimism that exist in places like Finland, Estonia, or South Korea, where AI is being explored not as a threat to be contained, but as a tool to be governed wisely and integrated thoughtfully into the commons.
We also risk exporting the wrong battles. If UBI is framed globally as a defensive measure against job loss—rather than as a dignified foundation for human flourishing—we lose the chance to discuss it in proactive, emancipatory terms. If AI regulation is solely about preventing harm, rather than designing public value, we end up policing risk instead of cultivating benefit.
In short: when American pessimism becomes the default narrative, pluralism suffers. And so does our ability to build AI futures that reflect the rich tapestry of global values, needs, and dreams.
The answer isn’t to silence American voices. It’s to invite more voices in. It’s to build a genuinely international conversation—where fears can be held alongside hopes, and where different cultural frameworks aren’t just tolerated, but deeply respected as essential inputs to global AI governance.
Because this is too big for one country to define. And too human for one narrative to own.
Plurality of Futures
AI is not destiny. It doesn’t descend from above like a force of nature. It emerges from us—from our priorities, our systems, our values. And so, more than anything, AI is a reflection: of what we protect, what we ignore, what we believe people deserve.
To see the future clearly, we must look beyond the Anglosphere. The narratives that dominate English-language media and American tech circles are not universal truths—they’re situated perspectives, shaped by specific histories, cultural beliefs, and institutional architectures. And while they have reach, they do not—and should not—have the final word.
If we want to build AI systems that serve humanity, we have to begin by acknowledging that humanity is not homogenous. Healthcare systems already show us this. They embody vastly different ideas about what societies owe to individuals, how resources should be distributed, and who gets to feel safe. These ideas don’t vanish when new technologies arrive. They evolve alongside them.
In some places, AI may be harnessed to enhance public services and expand collective well-being. In others, it may deepen inequality and entrench precarity—not because of the tech itself, but because of the context it enters. That context matters. Culture matters. History matters.
So there will not be one AI future. There will be many. And that’s not something to fear. It’s something to embrace.
Because plurality is not chaos—it’s resilience. It’s the recognition that different people, with different values, will need different tools, different safeguards, and different visions. It’s the understanding that the richest futures will be those shaped by many hands, not a single blueprint.
The goal, then, is not to find the answer—but to hold space for many answers. To listen across borders. To let solidarity, not scarcity, guide our choices. And to ensure that AI becomes not just a mirror of the powerful, but a canvas for the collective.
We don’t need a singular future.
We need futures worth choosing.
- Iarmhar
October 18, 2025