Category: Asides

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Data Overpayment

For the past 20 years or so, we have become too accustomed to the idea of a “free internet.” Search engines, social media, email, maps, translation services—all of it seemed free. Or at least, it felt that way.

But in reality, nothing was ever free.

We were paying not with money, but with data. Names, interests, location history, purchase records, sleep patterns, social connections, facial images—all of it was handed over as “payment” to sustain business models.

The problem is that we paid more than we needed to.

Did using a map really require disclosing our family structure? Did translation apps really need to track our location history? We never truly scrutinized how much information was being asked of us—or whether it was justifiable.

Worse, we no longer remember what we gave away.

This may, at some point, become a social issue we recognize as “data overpayment.”

Data overpayment is not a single event or sudden incident.
It is the slow accumulation of loss over years, even decades.
By the time we notice, we may no longer know what we’ve already lost.

But as we enter the age of AI, that structure is starting to shift. A new set of questions is emerging around our personal data—how it is used in model training, who has control over it, where it’s recorded, and how transparent that process can be.

What if we could know where our data is used? What if we could choose how it is used? What if we had the right to retract the data we shared in the past?
If that were possible, then the economic, legal, and ethical meaning of “data” itself would be dramatically redefined.

Data is not something to be sold off. It’s something to be licensed for use.
Data is owned—not traded, but governed through conditions.
If this perspective becomes more widely accepted, we might finally begin to correct the overpayment that has built up over the past two decades.

It’s time we start treating our data as something that truly belongs to us.

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Balancing Privacy and AI

The cloud is convenient. But more and more people are beginning to feel a quiet discomfort with entrusting everything to it.

Information is stored, utilized, linked, and predicted. Our behaviors, emotions, preferences, and relationships are being processed in places we can’t see. This unease is no longer limited to a tech-savvy minority.

So what can we do to protect privacy in a world like this?
One possible answer, I believe, is to bring AI down from the cloud.

As we can see in Apple’s recent strategic direction, AI is shifting from the cloud to the device itself. Inside the iPhone, inside the Mac, AI knows you, processes your data, and crucially—doesn’t send it out.

When computing power resides locally and your data stays in your hands, that convergence creates a new kind of architecture—one where security isn’t just about convenience, but trust. This is how a “safer-than-cloud” AI environment can emerge.

In this context, the question of “Where does AI run?” becomes more than just a technical choice. It evolves into a political and ethical question: “Who holds your data?” and equally important, “Who does not see it?”

This shift opens the door to new architectural possibilities.
When individuals hold their own data and run their own models locally, we create a form of AI that operates with a completely different risk structure and trust model than large-scale cloud systems.

In an era where the cloud itself is starting to feel “uncomfortable,” the real question becomes: Where is computation happening, and who is it working for?

Protecting privacy doesn’t mean restricting AI from using personal information.
It means enabling usage—without giving it away. That’s a design problem.

AI and privacy can coexist.
But that coexistence will not be found in the cloud.
It will be realized through a rediscovery of the local—through the edge.

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Digital Inbound

Until now, the word “inbound” has mostly been used in the context of tourism. People come from overseas. Products are sold. Culture is shared. Inbound meant creating systems that welcomed people, goods, and money into the country.

But today, a new kind of inbound is beginning to take shape.
Not people—but data—is coming.
In other words, we’re entering an era in which “information processing” crosses borders and comes to Japan.

Startups and research institutions from around the world are beginning to choose Japan as the place to train and deploy their AI models—not despite the regulations, but because of them. Because the legal frameworks are stable. Because the power supply is consistent. Because the local infrastructure is safe. And above all, because Japan is seen as a place where things can run in peace. There’s also the institutional integrity—data won’t leak even if someone attempts to subvert the system.

What’s happening here isn’t outsourcing or delegation.
What’s coming is not people, but computation, processing, information itself, and the use of infrastructure.
This is not tourism. It is the use of Japan’s physical infrastructure.

I believe this is a phenomenon we should call digital inbound.

Within this structure, Japan’s greatest value is in being a trustworthy foundation.
It’s not just about computing power, power grid reliability, or legal frameworks.
It’s about confidence that data won’t be extracted without permission.
Stability, knowing that rules won’t suddenly change.
Trust, that when something goes wrong, someone will be there to respond.
A proven track record of resilience in the face of disasters.
These intangible layers are beginning to define the value of Japan as a digital territory.

In the financial world, places like Manhattan, Hong Kong, and later Singapore once played similar roles.
They became “locations” where information and capital gathered—not because people were already there, but because the systems in place made it safe for people and information to arrive.

Now, the world no longer revolves around cities with growing populations.
AI doesn’t need crowds.
IoT doesn’t require human presence.
In fact, the very absence of people may make certain environments ideal for IoT.
Where there is land, energy, and social calm, AI and IoT will come to live.

In places once dismissed as “worthless because no one lives there,” we may soon see a new logic emerge—“valuable precisely because no one is there.”

Land that’s comfortable for AI.
Legal systems that are gentle on data.
Energy infrastructure with minimal friction.
Taken together, these factors are already starting to shift how Japan is being reevaluated by the world.

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What Kind of Literacy Is Required of Citizens in the Democratic Age of Computational Resources

Democracy, at its core, is built on the premise that sovereignty belongs to the people. But as we’ve passed through the information age and entered the age of AI, the very question of what sovereignty means is beginning to shift.

In today’s world—where computational resources, electricity, and data can influence the fate of nations and the direction of society—how can citizens, as sovereign actors, recognize and exercise their sovereignty?

In the information age, sovereignty meant choosing which sources to trust, which platforms to participate in, and which algorithms to entrust with our attention. But in the age of AI, that definition requires a deeper level of inquiry.

For example, we now have to ask: which computational resources processed the information that underpins our decisions?
Where were the models trained? Under what national legal frameworks and ethical principles were they built?
Where does the electricity come from, and who controls the compute processes?
All of these questions are directly linked to how and what we think.
It increasingly feels as if computational resources are becoming the new foundation of sovereignty.

In this era, having the right to vote may no longer be enough to be a true sovereign.
We also need to understand where our data is stored, under what nation’s rules our cloud operates, and which computational infrastructures are supporting our decision-making.
That ability to understand and choose is what I would call the literacy required of sovereign citizens in the era of computational resources.

If we entrust everything to Big Tech, we are, often without realizing it, relinquishing our sovereignty.
Which compute environments can we access?
To which computational infrastructures do we submit our data?
These may now be political rights in their own way.

So what kind of literacy do we need in this age?

Not just technical understanding, but literacy that spans systems, energy, ethics, and the meaning of decentralization.
Knowing which computational ecosystem we live upon may be one of the most important forms of awareness we can have.
That, I believe, will be a new prerequisite for democracy in the age of AI.

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Who Owns the Cloud

The cloud was once seen as belonging to no one—or at least, that’s how it felt.

Despite being built and operated by someone, we’ve long used it freely, entrusted our data to it, and become dependent on it, without treating it like “land” that can be owned. The cloud exists physically on some server somewhere, yet where it is has never seemed important.

In that sense, “cloud” was a triumph of branding.

But now that AI has become foundational to everything, and computational resources have emerged as the new currency of power, the cloud is once again under scrutiny.
Whose is it?
Who owns it?
Who has the right to use it?
Who controls access?

Just like land, water, or energy once did, the cloud now wavers between being public and private.

Today, decentralized data centers—what could be called distributed cloud infrastructures—are starting to appear in various regions. These are not provided by governments, nor should they be monopolized by any single corporation. Ideally, they should be owned by communities, used by schools and hospitals, and joined by citizens. These networks of computational resources could function as part of the societal infrastructure, much like waterworks or power grids once did.

Of course, this may be inefficient. It might be costly. Integration with existing infrastructure won’t be easy.
But between a future where everything is entrusted to one massive compute environment somewhere far away, and a society where small, reliable pockets of compute capacity exist across regions—surely the latter deserves more attention and discussion.

Beyond technical concerns, the cloud also needs diversity—politically and culturally.
This diversity means freedom of computation, freedom of thought, and freedom of choice.

So who owns the cloud?
I believe that should be decided by its users.
Perhaps it’s time to shift from a model where we’re merely “allowed to use” the cloud, to one where we “own it together.”

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Japan as a Choice

As information infrastructure becomes tied to national strategy, and both cloud and AI are increasingly framed within the context of geopolitics, nations are now faced with a decision: which information network to connect to, and on which compute infrastructure to build their society.

Many countries have effectively left that decision to Big Tech. The American cloud, or the Chinese cloud—not so much a matter of choosing, but of being absorbed into one or the other. In parts of Europe, there are now efforts to build “sovereign” systems, but even those often amount to little more than a reshuffling of dependencies.
This is something I felt directly, through discussions I had at CERN.

Beyond the startup

In this context, I’ve been thinking about the potential of a third option: Japan.

Not because Japan is technologically superior. In terms of compute resources, latent energy reserves, software competitiveness—Japan may in fact be at a relative disadvantage.

Even so, Japan holds a unique kind of value: neutrality, transparency, and trust—layers that aren’t easily quantified.

It’s a rule-of-law nation, with high disaster resilience, cautious about global-scale data usage, and with a strong social security layer. These form the foundation of what might be called national-level “assurance.”

In training AI models, it’s no longer just about how much you can compute. Where the data is processed, and under what ethical standards, now directly impacts long-term value. Ethics itself has become part of the infrastructure.

That’s why I believe that choosing Japan—specifically, the combination of its compute infrastructure and its legal framework—may increasingly hold structural significance. As companies, organizations, and even individual developers begin to consider “where to run” their projects, Japan may come to be seen as a politically and culturally “acceptable” nation.

Just as, in the world of finance, Switzerland, New York, Hong Kong, and Singapore once played such roles—Japan, or more precisely, Japan’s regional cities, could become a new center.
Perhaps the world is already beginning to seek out this option called Japan.

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The Age of a Compute-Backed Economy Where Semiconductors Anchor Trust

In the past, the foundation of the economy was gold.
Under the gold standard, currency was backed by physical assets.
The rarity of gold itself directly reflected the credibility of a nation and the value of its currency. That was the world we once lived in.

After a long phase of economic expansion unmoored from tangible assets, we are now entering a world where computational capacity is beginning to take the place once held by gold.

AI has become the foundation of all economic activity.
Industries are run by models. Decisions are made by computation.
In such a society, value is no longer created by labor—but by computational resources themselves.

And what are computational resources?
They are, in the physical sense, electricity, compute devices, cooling infrastructure, access as a matter of policy, and above all, semiconductors.

In the coming world, a nation’s credibility will be determined by how much it can compute.
National power will increasingly reflect the total computational capacity it controls.
A country that possesses semiconductor design and fabrication capabilities—and the energy and infrastructure to operate them—will be able to anchor its currency with computational resources.

This represents a transition into a compute-backed economic system.

Where once nations signaled their monetary credibility with gold reserves, they may soon point to the total number of GPGPUs they own, the strength of their AI training infrastructure, or the volume of high-quality data they control.
We may enter a world where it is reasonable to say, “Our currency is stable because we possess sufficient compute.”
It’s possible that compute has already rewritten the very concept of military power.

Computational resources are invisible. And their value is fluid.
Electricity prices, cooling efficiency, software optimization, algorithmic efficiency, and data quality—all of these dynamically affect the credibility of a currency.
This is a real-time economic foundation, so dynamic that humans alone may be unable to grasp it.
It presupposes communication between AIs.
And for any nation without computational resources to participate in that communication, the end may already be near.

Until now, the economy has been driven by “intangible trust.”
But in the age of AI, it is “the total executable compute” that becomes the final form of trust.
And at the core of that trust lies the hard fact of how much compute a nation possesses—and governs—within its borders.

Semiconductors, electricity, and data are no longer merely parts of industrial structure.
They underpin currency and sovereignty.
And the nation that supports them will be the one that holds the next global reserve currency.

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How to Turn Forgotten Resources into Infrastructure

There are resources in society that are no longer in use. They once had value, but over time, as structures changed, they were forgotten, left behind, and left untouched. Vast tracts of land abandoned due to natural disasters or depopulation. Decommissioned power infrastructure. Obsolete telecom stations. Remote plots of land and tunnels no one visits anymore. These are resources left behind by shifting industrial structures—forgotten, but not gone.

If the structure changes again, these resources may take on new meaning. Especially in an era driven by computational power, these physical infrastructures can function as “foundations for computation.” There’s electricity. Land that can dissipate heat. Environments with high tolerance for noise. Cheap land and municipalities open to collaboration. Water sources and climates ideal for cooling. From a different perspective, these may have always been “ideal infrastructures.” It’s not that they lacked value—they simply hadn’t been redefined yet.

When urban depopulation accelerates and rural populations decline, we tend to assume that the value of those regions is lost. But I believe that’s a human-centered—and deeply arrogant—assumption. For AI and IoT, the presence of people is not essential. What matters is whether data can be collected, electricity is available, and there’s access to the internet. For them, the optimal environment isn’t necessarily the city. In fact, rural areas—less interference, more available power and space, and infrastructure that can be redesigned from scratch—might be “natural” habitats for AI and IoT. Just as wildflowers grow where human hands do not reach, it is in these quiet places that the information infrastructure of the future may take root.

In finance, Manhattan once served as a hub, and later, Singapore did too—each backed by policy, tax regimes, and geopolitical positioning. If “geographic advantage” takes on new meaning, then Japan’s rural regions still have a chance. Japan is a rule-of-law country with stable power infrastructure and a high degree of safety. From the standpoint of human-centric life, it may appear resource-poor, but if we look across the country with localized renewable energy in mind, these “low-value” areas could become ideal foundations for the next generation of infrastructure.

Modern computational infrastructure no longer needs to be concentrated in urban centers. In fact, to avoid the shortages of electricity, space, and cooling found in cities, it will spread outward—to the periphery, to rural regions. As this trend continues, the logic that “unused means worthless” will flip. Places once dismissed may now be rediscovered as the foundational base for computational resources—valuable precisely because no one else is using them.

And it’s not just resources being redefined. Entire regions can reclaim purpose by changing the scale of evaluation. Turning forgotten resources into assets is not simply about buildings or machinery—it marks a quiet update to the structure of society itself.

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A Society Made of META

Before we realized it, the world became saturated with “META.” Finance, the internet, information, the economy—every structure in society has been elevated into increasingly abstract layers.

Take the internet as an example. There is a product, and a website to promote that product. Then a portal site that aggregates and compares such websites. Then a meta-search engine that cross-searches multiple such portals. The meta-search engine’s ads appear in search results, and when you click through, you land on a page that again displays the same meta-search ads. The structure folds in on itself, loops back, and begins to self-reference.

In this dense informational world, it becomes increasingly difficult to tell what is real and what is merely a reference. Unless we step back and observe with distance, we can no longer distinguish truth from echo, origin from surface.

The same phenomenon is happening in finance. The collapse of subprime mortgages was a symbolic example. Home loans became products, then were repackaged as financial instruments. These were bundled into CDOs, which in turn spawned more derivative products. Credit ratings were assigned, and in the end, everything became “an investment product.” People were trading abstracted assets, disconnected from the original substance—houses, borrowers, and local economies.

This meta-ization will only accelerate. In every field, structures that are distant from primary information will be mass-produced. Some will stack meta-layers on top of meta-layers and attempt to dominate from above. Competition will occur at these higher layers. GDP might appear to grow, but what does that “growth” even represent? We’ll gradually lose the means to measure whether it reflects actual value.

In such a world, what must be reexamined is the “lower layer.” The lowest layers may appear vulnerable, exploitable, powerless. But all meta-structures depend on them. The more meta we become, the more power returns to the foundation.

Those who control electricity. Those who physically store data. Those who own land and access to natural resources.

These “real assets” begin to function as the final gravity that anchors the meta elite. Running a meta-structure still requires real-world energy. And that energy resides in the lower layers.

This is why, in the future, true power won’t belong to those who control the surface. It will belong to those who control the base. At the end of meta, physics regains its force.

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The Concept of Distributed National Infrastructure

Until now, national infrastructure was something centrally managed and deployed across an entire country. Power plants, communication networks, water systems, roads, and data centers—all followed a model of “build in one place, use everywhere.” It was the nation that built, protected, and supplied these systems.

But that structure is slowly starting to change.

As portions of information infrastructure and computational resources come to be operated by specific tech giants, the infrastructure that once sat beneath the authority of the state is beginning to form a structure parallel to it. And what’s coming next is a shift away from centralization—toward a physical and logical model of “distribution.”

Distribution doesn’t simply mean breaking things into smaller parts. It means separating locations, ownership, control rights, power sources, and networks. It means running each independently, while allowing them to function together as a single system. That, to me, is the core of what “distributed national infrastructure” means.

This kind of structure is often discussed in terms of redundancy in disasters or risk dispersion in geopolitics. But more importantly, I believe it becomes critical when we begin asking, “Under whose sovereignty does this infrastructure operate?”

Entities not belonging to any central authority, but possessing social functions equal to or greater than national infrastructure. Cloud services, blockchain networks, local compute clusters, off-grid energy systems—when combined, these create a new kind of infrastructure that transcends borders and legal systems.

Whether this becomes something that replaces the nation-state, or something that complements it, remains to be seen. But what’s clear is that infrastructure is no longer something exclusive to states.

Perhaps we are entering an era where infrastructure is not something built by the state, but something into which the state must now merge—beyond the constraints of geography and the linear flow of time.

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