Tag: AI

Looking back over the past few months, I realize just how deeply I’ve fused my daily life with AI. Most of my routine tasks are already handled alongside it. Research, small repetitive work, even writing code can now be delegated. The most striking change is that tools built solely for my own efficiency are now fully automated by AI.

What’s especially fascinating is that even complex tasks—such as online banking operations—can now be automated in ways tailored specifically to an individual’s needs. For example, importing bank statements, categorizing them based on personal rules, and restructuring them as accounting data. What once required compliance with the frameworks imposed by financial institutions or accounting software can now be achieved simply by giving natural language instructions to AI.

The key here is that, unlike commercial products, there’s no need to satisfy “universality” for everyone. Personal quirks, rules that only I understand, exceptions that would never justify engineering resources for a mass-market service—these can all be captured and executed by AI. What used to be dismissed as “too niche” is now fully realizable at the individual level. Being freed from the constraints of general-purpose design has enormous value.

Even more revolutionary is the fact that APIs are no longer necessary. Traditionally, automation was possible only when a service explicitly exposed external connections. Now, AI can interact with data the same way a human would—through a browser or app interface. This means services don’t need to be designed to “export data.” AI can naturally capture it and fold it into personal workflows. From the user’s perspective, this allows data to flow freely, regardless of the provider’s intentions.

As I noted in my piece about Tesla Optimus, replacing parts of society without changing the interface itself will become a defining trend. AI exemplifies this by liberating usage from the design logic of providers and putting it back into the hands of users.

This structure leads to a reversal of power. Until now, providers dictated how services could be used. With AI as the intermediary, users can decide how they want to handle data. Whether or not a provider offers an API becomes irrelevant—users can route data into their own automation circuits regardless. At that moment, control shifts fully to the user side.

And this isn’t limited to banking. Any workflow once constrained by a provider’s convenience can now be redesigned by individuals. Subtle personal needs can be incorporated, complexity erased, external restrictions bypassed. The balance of power over data—long held by providers—is starting to wobble.

Of course, AI itself is still transitional. “AI” is not one thing but many, each with distinct characteristics. At present, people must choose and balance among them: cloud-hosted AI, private local AI, or in-house AI running on proprietary data centers. Each has strengths and weaknesses, and from the perspective of data sovereignty, careful selection is essential.

Still, living with multiple AIs simultaneously brings a sense of parallelization to daily work. Different tasks with different contexts can be run side by side, allowing me to stay focused on the most important decisions. Yet at the same time, because AI increasingly performs the research feeding those decisions, the line between my own will and AI’s influence grows blurred. That ambiguity is part of what makes this fusion fascinating—and also why the health of AI systems and the handling of personal data have become more critical than ever before.

The Emergence of a New Trend

Cloudflare’s recently announced “Pay per Crawl” is a system that enables content providers to charge AI crawlers on a per-request basis. Until now, site administrators only had two options when dealing with AI crawlers: fully block them or allow unrestricted access. This model introduces a third option — conditional access with payment.

Data has value. It should not be exploited unilaterally. A technical solution was needed to enable ownership and appropriate compensation. This move may upend how companies like Google handle information and monetize the web. It also presents an intriguing use case for micropayments.

How the Crawl Control API Works

At the heart of this model is HTTP status code 402 Payment Required. When an AI crawler accesses a web page, Cloudflare first checks whether the request includes payment intent. If it does, the page is returned as usual with HTTP 200. If not, a 402 response is returned along with pricing information. If the crawler agrees to the terms, it re-sends the request with a payment header and receives the content.

Cloudflare mediates the entire transaction, including payment processing and crawler authentication. The system essentially functions as an HTTP access API with built-in payment. It’s a well-designed solution.

The key differences from existing robots.txt or meta tag-based controls lie in enforceability and economic exchange. Since the control is enforced at the network level, access can be physically denied when requested. And with micropayments, permission becomes conditional — shifting the model from a courtesy-based norm to a contract-based economy.

In some ways, this reflects the type of society blockchain and smart contracts aspired to create. Yet again, private innovation is leading the charge toward real-world implementation.

Rebuilding the Data Economy and Its Reach in Japan

In the traditional web, value was derived from human readership. Monetization — through ads or subscriptions — depended on people visiting your content.

But in the age of generative AI, information is being used without ever being read by a human. AI models crawl and learn from massive amounts of data, yet the content creators receive nothing in return. Pay per Crawl introduces a mechanism to monetize this “unread but used” data — laying the foundation for a new information economy.

In Japan, local newspapers, niche media, and expert blogs have struggled to monetize via ads. Now, AI crawlers represent a new type of “reader.” As long as AI systems require niche data, those who provide it will hold value. Going forward, the strategy will shift from merely increasing readership to optimizing content for AI consumption.

For AI developers, this introduces a shift in cost structure. Whereas they previously harvested public information for free, they will now incur costs per data unit. This marks a shift: data, like electricity or compute resources, will be treated as a resource that must be paid for.

The role of data centers will also grow more significant. Companies like Cloudflare — which control both the network and the payment rails — will become central hubs of information flow. As with energy distribution in the “Watt–Bit” framework, control over information infrastructure will once again become a source of economic power.

Addressing Data Overpayment and Establishing Information Sovereignty

The greatest societal significance of Pay per Crawl lies in correcting the imbalance of data overpayment. Many websites, public institutions, educational bodies, and individuals have provided content for years — often without knowing that AI systems were using it freely.

Pay per Crawl introduces a negotiable structure: “If you want to use it, pay for it.” This represents a reclaiming of informational self-determination — a step toward what could be called “information sovereignty.”

With micropayments on a per-request basis, the monetization model will also diversify. Previously, revenue depended on going viral. Now, simply having high-quality niche information may generate revenue. This marks a shift from volume-based value to quality-based value.

As the ecosystem expands to include universities, municipalities, and individual bloggers, we’ll see a new era where overlooked information can be formally traded and fairly compensated.

Pay per Crawl is not just traffic control technology. It is an attempt to create a new rulebook for how information is controlled and monetized in the generative AI era.

The system is still in its infancy, but there is no doubt that it will influence Japan’s media industry and data governance. Establishing a healthy economic relationship between creators and users of information — that is the kind of infrastructure we need in the age of AI.

The Paradoxical Future Depicted by Gundam

The evolution of war and technological development has often followed parallel trajectories. From the era of samurai wielding swords and bows, to machine guns and weapons of mass destruction. From one-on-one close combat to one-versus-many long-range warfare. Modern war has been dominated by the logic of remote control and overwhelming firepower.

Against this trend, the anime Mobile Suit Gundam presented a provocative reversal. In a future dominated by high-speed, long-distance battles, it imagined a world where individual skill and close-range duels once again determined the outcome of war. Encased in machines of armor, samurai reappeared on the battlefield. Gundam envisioned a future where war regressed to a more personal, primitive form.

The Return of “Direct Combat” in Cyberspace

This structure is now reemerging in the real world. For decades, software scalability and information dominance ruled warfare and industry. But today, nations are shifting their strategies—targeting the physical layers. Network decoupling, hardware embargoes, infrastructure sabotage. Some states now attack the foundations that cloud computing and AI rely on.

By making software unusable, they strike at the bottom: electricity, semiconductors, supply chains. This pushes us back toward physical “direct combat.” To gain strategic advantage, players now optimize OS, middleware, and programming languages for hardware—maximizing computational efficiency and security. A new arms race is underway in cyberspace: the race to forge the blade and shield of digital sovereignty.

Even in AI Warfare, We Need the Forgotten Samurai

AI development follows the same logic. While attention focuses on clouds, APIs, and LLMs, true strength lies in hardware-software integration. Distributed systems, cooling solutions, energy optimization, secure physical design. Those who understand and master the lower layers are the modern samurai—resilient, grounded, and decisive.

Yet this mode of battle is not passed down to the “Silicon Valley generation.” Engineering education prioritizes app interfaces and abstraction, but neglects core OS skills or low-level circuit design. Investment pours into user experience, while the foundations are forgotten.

But in the real world, only those who can descend to the physical layer can confront the essence of AI warfare or cyber conflict.

The age of the samurai is not over.
It is being reborn—beneath the software, deep in the substrate of our digital world.

The First 99% Was the Stone Age

It is often said that 99% of human history was spent in the Stone Age. This is not a metaphor—it is, for the most part, true.

Even if we define humanity strictly as Homo sapiens, around 290,000 of our 300,000-year history was spent in the Paleolithic era, making up about 97% of our existence. If we trace our lineage further back to early hominins, the ratio increases to between 99.6% and 99.9%.

In other words, agriculture, cities, nations, and even AI—all emerged within the final sliver, less than 1% of our history.

Revolutions Are Accelerating

The Agricultural Revolution began roughly 10,000 years ago. When humanity chose to settle and discovered the concept of “production,” society began to transform. After 4 million years of a hunter-gatherer lifestyle, that paradigm ended in just a few generations.

Since then, humanity has repeatedly undergone transformative leaps—what we now call “revolutions.”

From agriculture to the Industrial Revolution took about 10,000 years.

From there to the Information Revolution: roughly 200 years.

And from that to the AI Revolution: just 30 years.

The intervals between revolutions have been shrinking exponentially.

As revolutions become more frequent, they are no longer “exceptions” but the new “norm.” What once defined an entire era for millennia now gets overturned within decades.

Generative AI became a starting point for the next upheaval the moment it arrived. As it penetrates society, it actively influences the trajectories of AGI, robotics, brain-machine interfaces, and other concurrent revolutions.

We now live in a time when we can no longer afford the luxury of recognizing that a revolution even happened.

Revolutions Always Destroy What’s Most Primitive

The Agricultural Revolution dismantled humanity’s coexistence with nature.

The Industrial Revolution redefined labor and the meaning of time.

The Information Revolution shattered physical limitations.

And now, the AI Revolution threatens to redefine what it means to be human.

Information flow, the reassembly of knowledge, behavioral optimization, externalized consciousness—all of these have unfolded within the final 1% of human history.

The idea that revolutions are accelerating is itself an indication of a singularity. Whether or not Kurzweil’s prediction of 2045 comes true, we are already living in something resembling a singularity.

We are no longer in an age between revolutions—we are living within an unbroken state of revolution itself.

The Sense of Living in the Final 1%

If 99% of human history was the Stone Age, then we are living in that final 1%—right now.

Farming, nations, economies, energy, networks, and AI—all these revolutionary changes occurred in less than 1% of our past. And it is likely that in the next 0.1%, everything will be rewritten again.

That next revolution may not even be expressible in human language.