The Dynamics of Minimum Wage and ‘Human Resource Development’ in Japan
Rikigaku Observation Institute | RYO | August 2026
OHAKONBANNICHIWA❗This is RYO from the RikigakuObservation Institute❗
In Japan today, it has become increasingly common to see foreign workers behind convenience-store counters and inside large distribution warehouses.
What concerns me is not simply that the number of foreign workers has increased. It is that so many of the people I see are young.
Why did they come to Japan❓ After spending several young years here, what will they be able to take home❓
This is not an argument for or against immigration. Nor is it a debate about how far Japan should accommodate religion or culture. The question is whether the time young foreign workers give to Japan is balanced by the value Japan returns to them.
Does Allowing the Hijab Amount to Diversity❓
FamilyMart announced that from September 2026 it would introduce a T-shirt-style uniform and formally broaden its grooming rules to allow freer hair colors and items such as the hijab. News coverage reported that foreign nationals accounted for about 13 percent of the chain’s roughly 200,000 store staff, and connected the change to recruitment.
Making an existing case-by-case accommodation explicit is not a bad thing. But being permitted to work while wearing a hijab and having one’s future protected are not the same issue.
Hair and clothing may be diverse. But what about the work itself❓ When a company says it welcomes diverse talent, is it also offering diverse paths for learning and advancement❓ Or is it mainly looking for people who will fill shifts under the existing wage and scheduling conditions❓
For readers outside Japan, a konbini is more than a small grocery store. Many are open around the clock and handle bill payments, ATMs, ticketing, parcel pickup, prepared food, and emergency supplies. Their apparent simplicity rests on dense information systems and precisely timed logistics.
The Original Idea Was to Take Japanese Skills Home
Japan’s Technical Intern Training Program, commonly abbreviated as TITP in English, was officially designed to transfer skills, technologies, and knowledge cultivated in Japan to developing regions. Its stated purpose was international contribution through human-resource development.
The governing principle was explicit: technical intern training was not supposed to be used as a means of adjusting the supply and demand of labor inside Japan.
The ideal cycle was easy to understand. A young person would learn distribution, manufacturing, care work, or another field in Japan, return home, become a core employee or start a business, and connect that knowledge back to Japanese companies and communities.
Skills and knowledge would circulate through people, creating value for both Japan and the sending country. Read literally, the word training described an investment rather than the purchase of cheap labor.
Not every foreign national working at a convenience store or warehouse is a technical intern. Some are international students working within permitted hours, some hold permanent or long-term resident status, some are dependents with permission to work, and some are dispatched by staffing agencies.
The more fragmented those legal and contractual positions become, the less clear it is who should turn those years into genuine human development. A school can say that education is its responsibility. An employer can say it hired only a part-time worker. A staffing agency can say it merely introduced a job. Each actor may be correct within its own boundary.
Yet from a wider view, their young years are unquestionably helping to keep Japanese stores, warehouses, factories, and care facilities operating.
Working Inside Advanced Logistics Is Not the Same as Learning Advanced Logistics
A Japanese convenience store can be a fascinating place to study distribution: demand forecasting from point-of-sale data, ordering and stockout rates, inventory turnover, food waste, temperature-controlled logistics, shared delivery networks, staff allocation by time of day, trade-area analysis, and the division of profit between headquarters and franchisees.
A major sorting hub is equally complex: trunk transport linked to local delivery, parcel-volume forecasts, hourly processing capacity, equipment design, load factors, missort rates, damage prevention, seasonal staffing, construction costs, and center-level profitability.
Japan offers a chance to observe one of the world’s most finely tuned parcel systems in physical operation. But are the young foreign workers inside that system actually being taught how it works❓
Scan a barcode. Send the parcel toward the displayed destination. Place it on a conveyor. Sort it before the deadline. If training ends there, what has been learned is not logistics as a discipline but the work procedure of one particular site.
Working inside an advanced logistics system is not the same as learning advanced logistics.
The company accumulates data on throughput, forecasting, missorts, equipment, and improvement. Headquarters retains the ability to design and optimize the network. The worker may retain only the experience of sorting parcels quickly and accurately.
That difference becomes severe the moment the worker leaves the site or returns home. Company knowledge remains portable within the organization. A site-specific routine may not be portable at all.
What the Phrase ‘Labor Shortage’ Leaves Out
The author lives in Osaka City, near Yamato Transport’s Osaka Base, a large parcel-sorting hub in Suminoe Ward. In August 2026, the company’s official recruitment page advertised two-month part-time sorting positions at \1,177 to \1,200 per hour.
The work included feeding parcels onto conveyors, pulling them into destination lanes according to their numbers, and loading them into boxes. The advertisement also noted that heavy items such as rice, bottled water, and furniture could be involved.
At the time of publication, \1,177 was the legally effective minimum wage in Osaka Prefecture. In other words, the bottom of the advertised range exactly matched the lowest hourly wage permitted by law. A rise to \1,231 had already been approved for October 2026, but it was not yet in force.
Now consider a thought experiment. If the base hourly wage were raised immediately to \2,000, would Japanese applicants still stay away❓
Under Japanese labor law, work between 10 p.m. and 5 a.m. receives a statutory premium of at least 25 percent. A \2,000 base rate would therefore become at least \2,500 during those hours. Would students, job seekers, part-time workers, and people with daytime jobs respond differently under that condition❓
If nobody came even then, the shortage might truly be a shortage of people. But if applicants appeared, the missing resource was never people in the abstract.
What was scarce was the number of people willing to work at night, handle heavy parcels, commute to a large hub, and accept \1,177 per hour.
The phrase labor shortage must not erase wages, hours, physical burden, commuting conditions, or employment duration. A more exact phrase would be: a shortage of people willing to work at a labor cost compatible with the current business structure.
It Is Also Rational for a Company to Seek Lower Labor Costs
Labor is one of the largest continuing costs of operating a business. The expense includes not only wages but also social insurance, recruitment, training, labor management, safety measures, and responses to absence and turnover.
If the same task can be completed to the same quality, trying to control labor cost is an ordinary management decision.
Nor would the story end if one logistics company alone raised its sorting wage to \2,000. Higher labor costs would pressure parcel rates. Online retailers would resist more expensive shipping. Sellers would absorb part of the cost, and consumers would eventually see it in prices or delivery fees.
Japanese consumers have been accustomed to low shipping fees, narrow delivery windows, and rapid arrival. Online sellers want to suppress distribution costs. Logistics companies want to preserve nationwide networks and cutoff times. Staffing agencies search for people who will accept the available conditions.
Young foreign residents choose from the jobs available to them while facing language limits, visa conditions, restrictions on working hours, and imperfect information.
No individual actor needs to intend exploitation. When everyone behaves rationally within their own position, an equilibrium can still emerge in which young foreigners support Japanese logistics at or near the minimum wage.
The equipment may be state of the art. The human labor may be priced at the legal floor. The gap between them is filled by someone’s youth.
People Increase by Addition. Does the Wage Pie Increase Too❓
In an earlier article, this institute used a simple model inspired by the grade-based wage system of Meiji-era silk mills, later remembered through the history and literature surrounding the Nomugi Pass.
For readers outside Japan, Nomugi Pass is associated with young women who left poor rural communities to work in silk-reeling factories. Their earnings supported families and Japan’s early industrialization, while their labor conditions became a symbol of the human cost hidden inside national economic development.
Imagine that a factory first fixes its total wage fund at 100 and then divides that 100 according to performance. If one worker receives more while the total remains 100, someone else receives less. The increase is not created; it is moved.
Now reverse that model and apply it to foreign labor. A young foreign worker is both a worker and a consumer in Japan. The person pays rent, buys food, uses communications services, and participates in domestic demand.
Headcount rises from 100 to 101. Labor supply gains one person, and the number of consumers also gains one person. But one complete unit of wage funding does not automatically arrive from abroad with that person.
If sales, value added, and the total labor budget remain unchanged, the new wage must come from somewhere inside the existing 100: corporate profit, other workers’ pay, payments to suppliers, higher prices, or public support.
If the additional worker expands output, sales, and value added so that the pie grows from 100 to 110 or 120, the result may be positive. The next question is where that increase flows.
Does it return to the worker as higher wages, education, and portable qualifications❓ Or does it become cheaper delivery, corporate profit, and convenience for Japanese society❓
People increased. Labor increased. Consumers increased. Did the value that the worker can carry home increase as well❓
The System Did Not Drift Away from Reality. The System Moved Closer to Reality.
Japan’s Technical Intern Training Program officially emphasized international contribution through the transfer of skills. On 1 April 2027, it is scheduled to be replaced by the Employment for Skill Development Program, also described in government English materials as the Training and Employment System.
The new system’s stated purpose is to develop and secure workers for sectors in Japan that face labor shortages, generally through three years of employment leading toward the level required for Specified Skilled Worker status, category one.
For readers unfamiliar with Japan’s visa architecture, Specified Skilled Worker category one is a work-oriented status for designated industries. It is distinct from permanent residence and is generally limited in duration. Category two allows a more durable career path in eligible fields, but Japan’s warehouse-logistics field currently has no category-two route.
In warehouse logistics, the government lists activities such as receiving and inspecting goods, moving and storing freight, picking items, and distribution processing. These are real and necessary skills. But the list does not include network design, demand forecasting, center profitability, management education, or support for starting a business after returning home.
Previously, the official principle said the program must not be used to adjust Japan’s labor supply. Under the new system, securing workers in labor-short sectors is stated openly as part of the purpose.
The workplace did not simply depart from the old ideal. In the end, the official ideal moved closer to what workplaces had already become.
Wages Settle One Hour of Labor. They Do Not Settle the Future Value of Youth.
Young foreign workers are not working for free. They receive wages, and they may use those wages for living costs, tuition, savings, or remittances to their families.
Yet the fact that wages were paid does not settle every question about the young time exchanged for them.
An hourly wage pays for the task performed during the hour in front of us. It does not price the alternative education, qualification, professional network, or career path that might have been built during that same hour.
Young time compounds. Skills gained in one’s twenties shape work in one’s thirties. Experience in one’s thirties shapes position and choice in one’s forties. A young year cannot later be bought back at the same price.
If Japan teaches distribution engineering, logistics management, store operations, and franchise systems, those years can remain inside the worker as human capital. Portable qualifications and knowledge can turn time spent in Japan from consumption into investment.
But if only procedures that lose value outside one site remain, Japan keeps its delivery capacity, the company keeps its revenue and operating knowledge, and the consumer keeps the parcel that arrived. The worker keeps time that has already passed.
Wages are paid for one hour of labor. They are not paid for the future choices that disappeared with that hour.
Responsibility Flows Like a Fluid and Finally Disperses Like Mist
The word guilt in this article does not point to a simple villain.
A company can say it paid the agreed wage. A staffing agency can say it legally introduced a job. A school can say the work remained within permitted limits. The government can say it created visa and employment rules. Consumers can say they paid the displayed price and delivery fee. The worker can say the job was chosen voluntarily.
Viewed one transaction at a time, nobody takes an entire youth. A company buys one shift. A staffing agency connects one vacancy. A consumer receives one parcel. Each transaction is settled locally.
Add those settled transactions together across several years, however, and part of one person’s twenties appears.
When responsibility for that whole is questioned, it flows from the company to the staffing agency, from the agency to the legal system, from the system to individual choice, and from individual choice to consumer demand.
It changes shape, crosses boundaries, and finally becomes a mist: the worker chose the job and received wages, so there is no problem.
Responsibility may disperse. Time does not return.
What Remains with the Young Worker❓
The company retains logistics knowledge. Consumers retain convenience. Japanese society retains the result of having kept a labor-short workplace operating for several more years.
What remains with the young foreign worker❓ A memory of working in Japan❓ Japanese-language ability❓ Savings❓ Or work experience with limited use after returning home❓
Nobody may have intended to take their time. Yet if nobody assumes responsibility for its long-term value, that time is quietly converted into convenience for Japanese society.
Guilt flows like a fluid and disperses like mist. The lost young years alone remain as a solid fact inside the worker’s life.
Is Japan really importing foreign talent❓ Or is it importing inexpensive blocks of time cut out of young people’s futures❓
Is that not a profoundly troubling system❓
Author’s Note
This article is not intended to criticize any specific company, organization, individual, national government, or local authority. It observes a structure produced when companies, public agencies, staffing firms, consumers, schools, and workers each behave rationally within their own positions.
No society has a single absolutely correct answer. The subject here is the combined force created by individually rational interests—and the future value that may disappear between them.
A Note for Readers Outside Japan: Why the Media Debate Often Misses This Layer
Japanese reporting on foreign residents often relies on two powerful moral frames. One warns the public not to view foreigners through discrimination, prejudice, or xenophobia. The other foregrounds the hardship experienced by foreign residents when immigration rules, fees, or administrative requirements become stricter.
These are legitimate subjects. Discrimination exists, and the consequences of policy changes deserve scrutiny. The problem is not that such reporting is false. The problem is that the chosen lens can become the entire story.
For example, Japanese editorials frequently caution against exclusionary politics, while feature reporting may center families who say that sharply higher residence-permit fees threaten their livelihoods. Such stories ask whether Japan is being sufficiently humane toward foreign residents.
But the present article asks a different question. Even when foreigners are treated politely, allowed religious dress, paid legally, and protected from overt discrimination, is Japan turning their young years into portable human capital—or merely into low-cost operating capacity❓
When media coverage remains at the level of tolerance versus exclusion, wages, business models, training content, labor mobility, productivity, and the future value of time can disappear from view.
This framing also collides with social media. Many Japanese users already feel that taxes, social-insurance contributions, prices, and public charges are rising for citizens as well. They may therefore read sympathetic coverage of foreign residents as journalism that shows compassion in only one direction.
The news media says, ‘Do not discriminate.’ Social media replies, ‘Do not demand special treatment.’ Emotional friction grows, and the argument becomes a contest over who deserves sympathy.
Meanwhile, the structural question is left behind: who receives the accumulated knowledge, who receives the convenience, who bears the cost of training, and what remains with the young worker after the transaction has ended❓
Anti-discrimination is necessary. So is honest reporting on hardship. But neither should be allowed to substitute for an examination of the economic mechanism that converts a young person’s nonrenewable time into social convenience.
English Translation by AI Watt — the hardworking canine AI robot of Rikigaku Observation Institute.🐾
Quotations are welcome with clear attribution and a link to the original article. For substantial reproduction, translation, image use, interviews, or official comments, please contact info@rikigaku.jp.
In July 2026, Japan’s Ministry of Internal Affairs and Communications issued administrative guidance to LY Corporation over the external transmission of approximately 8.03 million pieces of user-related information from several LINE games.
The information included internal user identifiers sent by a development and operations partner to an external analytics service without LY Corporation’s approval and without the required notice to users.
The incident lasted for nearly four years.
At first glance, this may look like yet another story about one company failing to manage user data properly.
But in Japan, the name LINE carries much more weight than an ordinary messaging app.
And that is where this story becomes more interesting.
Ohakonbannichiwa❗️ This is RYO from the Rikigaku Observation Institute.
To Understand the Issue, You First Need to Understand LINE in Japan
For readers outside Japan, a little background is necessary.
LINE was launched in Japan in 2011 by NHN Japan Corporation, which had been established by South Korea’s NHN Corporation, now NAVER Corporation.
So simply calling LINE either a “Japanese app” or a “Korean app” does not fully describe its history.
What matters here is that LINE became extraordinarily successful in Japan.
By March 2026, LINE had about 100 million monthly active users in Japan, equivalent to more than 80 percent of the country’s population.
People use it to talk with family and friends.
Companies use it to communicate with customers.
Stores use official accounts for marketing, reservations and customer service.
And local governments and public organizations have also adopted LINE for administrative communication and public services.
After concerns arose over LINE’s data management in 2021, the Japanese government actually surveyed the use of LINE by government agencies and local authorities and published guidelines for its continued use.
That fact alone tells us something important.
LINE had already become something close to social infrastructure in Japan.
The 2021 Controversy Was More Complicated Than “Servers in China”
This history also explains why some Japanese users react strongly whenever another LINE-related data incident appears in the news.
There is an important factual distinction here.
The 2021 controversy was not simply that “LINE stored all Japanese user data on servers in China.”
The actual problem included the fact that contractors located in China had been able to access certain personal information belonging to Japanese users.
LINE reported to Japan’s Personal Information Protection Commission that such access from China had been blocked by March 23, 2021.
At the same time, some data — including certain photos, videos and files — had been stored in data centers in South Korea.
LINE subsequently began moving the relevant Japanese user data to servers in Japan.
LY Corporation says that the migration of all data covered by that plan was completed by June 2026.
There were also later incidents.
In a major unauthorized-access incident disclosed in 2023 and updated in 2024, LY Corporation reported that 302,980 pieces of user-related personal data had been leaked or potentially leaked, along with information relating to business partners and employees.
That incident began after malware infected a computer used by an employee of a contractor connected to South Korea’s NAVER Cloud.
None of this means that every piece of information on LINE is currently sitting exposed somewhere overseas.
Nor does Korean corporate origin itself prove that a service is unsafe.
The real issue is governance.
Who can access the data❓️
Where is it stored❓️
Which companies and contractors are connected to the system❓️
And are users and public institutions being told those facts accurately❓️
That is a much more useful security question than simply asking which country a company came from.
Infrastructure Does Not Automatically Mean Trust
There is another contradiction in Japan that is easy to miss from overseas.
LINE has become infrastructure-like, but not everyone wants to participate in that infrastructure.
There are Japanese users who consciously avoid LINE, PayPay and other services associated with the broader SoftBank–LY ecosystem.
The reasons are not all the same.
Some are specifically concerned about privacy, cross-border data management or past security incidents.
For others, the reaction is less technical and almost instinctive:
“I simply don’t want to give that corporate ecosystem more of my data.”
There is no reliable statistic telling us exactly how many Japanese people avoid LINE or PayPay for this particular reason.
So it would be wrong to exaggerate this into a majority view.
But privacy-driven refusal of digital services itself is certainly not imaginary.
A 2026 Japanese consumer survey found that, among respondents who felt uncomfortable providing personal information, 35 percent said they had stopped using a service.
This produces an interesting kind of friction.
If a privately operated platform becomes deeply embedded in everyday life, choosing not to use it begins to carry a cost.
A person may distrust the service, yet discover that a company, store, school, neighborhood association or local authority assumes everyone has it.
The technical freedom not to use a service still exists.
But the practical price of exercising that freedom gets higher as the network grows.
That is worth remembering when somebody says:
“If you don’t trust LINE, just don’t use it.”
The Front Door Is Fortified — While the Back Door Is Left Open
Now let us move from LINE itself to corporate information security.
Many companies protect their company-issued computers and smartphones very seriously.
USB storage is restricted.
Software installation is controlled.
Access logs are recorded.
Smartphones are managed through MDM.
Endpoint protection and access-control systems are installed.
Ohakonban’nichiwa! I’m RYO from the Rikigaku Observation Institute!
“Which do people prefer: fiction written by ChatGPT or fiction written by a human?”
A study by researchers at Villanova University, recently covered by CNET Japan, produced an intriguing result: participants rated the ChatGPT-generated stories more highly than the human-written ones.
In the first experiment, 1,682 adults aged 18 to 81 evaluated short stories written either by humans or by ChatGPT, rating their quality and how absorbing they found them. The AI-generated stories received higher ratings overall. Yet another effect appeared at the same time: stories described as “human-written” were rated more favorably, regardless of who had actually written them.
In subsequent experiments, participants were asked to distinguish human-written stories from AI-generated ones. Their performance showed that telling the two apart was far from straightforward.
So far, so interesting. But while reading the Japanese article, something else caught my attention.
In Japanese, the AI Version Really Is Easier to Read
The CNET Japan article includes Japanese translations of excerpts from the stories used in the study.
In the AI-generated story, the narrator sits beside a pond, remembers her mother, watches autumn leaves fall and koi swim beneath the surface, and reflects on change and constancy in life.
The prose may have that slightly familiar ChatGPT tendency to wrap things up a little too neatly, but its meaning comes across immediately.
The human-written story, by contrast, develops a metaphor around childbirth. One character is described as being in labor; the narrator casts herself as a midwife; the husband becomes an anxious father; and the metaphor expands toward the idea of a sacred event.
Of course, these are only excerpts from longer works, so it would be unfair to judge either story as a whole from these passages alone. But when I read the two excerpts in Japanese, I found the AI-generated one noticeably smoother and easier to follow.
The human-written passage felt different. In Japanese, it had the flavor of an older translated novel—or, to exaggerate slightly, something produced by an earlier generation of machine translation.
There is a particular kind of translation effect that Japanese readers sometimes encounter in American films, advertisements or tech presentations: a perfectly ordinary English phrase crosses into Japanese and somehow comes out sounding grand, solemn and vaguely philosophical.
To invent an exaggerated example:
“This is not merely a device. It is a new way to experience tomorrow.”
Perfectly plausible in an English-language presentation. Translate that too literally into Japanese, however, and suddenly it sounds as though Apple has started writing philosophy.
The human-written excerpt gave me a little of that feeling.
But Wait—These Stories Weren’t Written in Japanese
And then an obvious point occurred to me: the experiment was conducted in English.
Japanese readers of the CNET Japan article are therefore not reading exactly what the participants in the study read. What reaches us has already traveled through another process:
English original → Japanese translation → Japanese reader
That distinction matters particularly when the subject is fiction. In a news report, a translation can often do its job as long as factual information—dates, numbers, events and statements—is transferred accurately. Fiction is different. Word order, rhythm, ambiguity, metaphor and cultural association can all be part of the work itself.
A metaphor that feels natural and understated in English may become conspicuous, overly dramatic or strangely explicit when its structure is carried directly into Japanese.
So by the time I compare these two excerpts in Japanese, I am no longer observing only ChatGPT vs. human. I am also observing ChatGPT in English → Japanese translation versus human writing in English → Japanese translation.
There is another lens between the original text and me: translation.
The AI Wasn’t Writing from Nothing
Looking at the original paper reveals another important detail. The researchers used three human-written stories and asked GPT-4 to generate a corresponding story for each one.
For example, the AI story Reflections in Still Water was paired with the human-written story FISH. GPT-4 was given fairly specific instructions involving themes such as life and death across generations, uncertainty, koi as a symbol, and the perspective from which the story should be told.
In other words, the researchers did not simply tell GPT-4:
“Write me a story.”
Themes, symbols, narrative perspective and other elements were extracted from the human-written works and used to construct corresponding prompts for the AI.
That is a reasonable way to make the stories comparable in an experiment. But when the study is reduced to the popular question “Which writes better fiction, AI or humans?”, this experimental condition is worth remembering.
Using Saussure as a Measuring Stick
This is where Ferdinand de Saussure becomes useful—not as a subject for a linguistics lecture, but as a tool for observation.
To simplify his theory considerably, Saussure described the linguistic sign through the relationship between the signifier—the form of a word or expression—and the signified—the concept it evokes.
These signs connect to their meanings in a relatively straightforward way. Leaves fall in autumn / koi swim beneath the water / the narrator remembers her mother / something constant offers comfort amid a changing life.
Of course, none of these meanings is completely independent of language or culture. But the relationships among them seem relatively easy to preserve when the passage moves from English into Japanese.
Here, meaning develops through an extended metaphor. The effect depends not only on what each individual word signifies, but also on the network of associations created among those words within a particular linguistic and cultural context.
That network may not survive translation in exactly the same form. The words can all be translated correctly, and the tone can still shift. What felt literary in English may become unusually explicit in Japanese. A metaphor may remain perfectly understandable while becoming heavier, more conspicuous, or simply more “translated.”
In other words, translation does not merely replace one signifier with another. It has to reconstruct relationships among signs in another linguistic system.
And that raises another question.
Are AI-Generated Texts More “Translation-Resistant”?
From this point on, I am no longer describing a finding from the Villanova study. This is a hypothesis that occurred to me while reading the Japanese translations.
Perhaps AI-generated prose is not simply easier to read because it is more direct. Perhaps it also tends to preserve its semantic relationships more easily when moved from one language to another.
Large language models learn from enormous quantities of text. In doing so, they may gravitate toward patterns and structures that recur across many examples of language. That can certainly be a weakness: AI prose can feel averaged out / less idiosyncratic / overly polished / strangely familiar.
But turn the same characteristic around, and it suggests another possibility: those more widely shared structures may also be easier to carry across languages.
Human literary writing can derive much of its richness from exploiting the peculiarities of a particular language, culture, voice or network of associations. Precisely because those relationships are so specific, some of that richness may be difficult to reproduce elsewhere.
AI-generated prose may sacrifice some of that specificity. But could the same sacrifice make it more portable?
Less dependent on a particular system of signifiers → less lost when those signifiers have to change?
I don’t know. And the Villanova study does not answer that question.
Testing it would require a different experiment—one designed specifically to compare how human-written and AI-generated texts behave across translation. But that is exactly why the Japanese version of the article interested me. Translation may have introduced a new variable that the original experiment was never designed to examine.
The Researchers Themselves Don’t Say “AI Won Because It’s Easier to Read”
There is another point worth keeping in mind. Popular coverage naturally tends to focus on a simple explanation: AI-generated stories may have been preferred because they were more direct, concise and easier to understand.
The original paper is more cautious.
The researchers discuss ease of interpretation as one possible explanation for the higher ratings, not as a conclusion established by the experiments. They also point out an obvious complication: literary fiction is not necessarily “better” simply because it is easier to understand. Ambiguity, complexity and room for interpretation can be part of what gives a story its value.
The later experiments produced an even more interesting result. Participants who relied on wording as a clue to authorship tended to be worse at identifying whether a story had been written by a human or by ChatGPT. In one experiment, participants who used their own enjoyment of a story as a clue were also more likely to get the answer wrong.
In other words, an intuition such as “This is easy to read, so it must be AI” may not help us identify AI writing at all. It may even push us in the wrong direction.
That makes the result more interesting, not less.
How Long Will “AI vs. Human” Remain a Useful Comparison?
There is a broader problem with the question itself. In a controlled experiment, separating “AI-written” from “human-written” text makes perfect sense. Outside the laboratory, however, that boundary is already becoming difficult to maintain.
Consider professional shogi. Today’s top players study moves suggested by AI, including moves that previous generations of human players might have considered unnatural or even poor. They examine the reasoning behind those moves, understand their value, and incorporate what they learn into their own play.
When a professional later plays such a move in an actual match, whose move is it? The human’s? The AI’s? The question quickly becomes awkward.
Something similar happened long ago with spreadsheets. Before software such as Microsoft Excel, enormous amounts of human time were spent performing calculations and organizing data manually. Today, we do not normally look at a spreadsheet produced with software and say, “This is not human work because a computer calculated it.”
The software has become part of the human workflow. Generative AI may be moving in the same direction.
Human → AI → Human
Writing is already beginning to look like this:
A human develops the idea → ChatGPT produces a draft → the human spots what feels wrong → the AI generates alternatives → the human rejects some, keeps others and rewrites the result.
Who wrote the finished text?
“The human” and “the AI” are both incomplete answers.
The more interesting change may therefore be not that AI is becoming capable of writing better stories than humans, but that humans and AI are beginning to alter one another’s output.
Humans learn from AI. Humans incorporate those techniques into their own writing. AI systems, in turn, learn from human-produced language in an environment that is itself increasingly influenced by AI.
Human → AI → human → AI.
Shogi offers an early example of this cycle. What begins as an “AI move” can eventually become part of ordinary human theory.
If something similar happens to writing, the clean boundary required by the question “AI or human?” may become increasingly artificial.
Perhaps the more useful question will eventually be not “Who wrote this?”, but “What forces shaped the text that ended up in front of us?”
English → Japanese (translator unknown) → ChatGPT → English. At this point, even Saussure might ask for a system update.😂
小説では、語順、リズム、曖昧さ、比喩、文化的な連想まで作品の一部です。英語では自然な表現でも、その構造を保ったまま日本語へ移せば、大げさだったり説明臭かったりする文章になることがあります。つまり、われわれが比較しているのは単純な「ChatGPTの文章 vs 人間の文章」ではありません。翻訳というレンズが一枚挟まっています。
たとえば人間作品『FISH』に対応するAI作品『Reflections in Still Water』では、「世代をまたぐ生と死／不確実性／鯉を象徴として使うこと／成長した子どもの視点」といった具体的な条件がGPT-4に与えられました。つまり、「はい、小説を書いて」と丸投げしたわけではありません。人間作品からテーマ、象徴、視点などを抽出し、それに対応する条件を与えたうえでAI作品を生成しています。
Ohakonbanichiwa! RYO here from the Dynamics Observation Institute. Yes, that means good morning, hello, and good evening — all at once. Very efficient.
Japanese frozen-food giant Nichirei was hit by a cyberattack that disrupted shipments and other operations.
The ransomware group RansomHouse later claimed responsibility and reportedly published more than 200,000 files, including documents that may contain personal and business information.
“More than 200,000 files leaked” certainly sounds alarming. But there is another way to look at what happened.
What exactly did RansomHouse gain by publishing them?
Did Nichirei Refuse to Pay?
There is no public confirmation that Nichirei refused to pay a ransom, nor do we know what negotiations, if any, took place behind the scenes.
So we cannot say that Nichirei “didn’t pay.”
What we can observe, however, is the sequence of events.
Nichirei detected the system failure on July 13, isolated affected systems, worked with external cybersecurity specialists, and subsequently restored normal operations. Meanwhile, RansomHouse continued releasing stolen data, with more than 200,000 files reportedly published by August 10.
We do not know what happened at the negotiating table. But looking at what happened outside it, one question naturally arises:
Is this really how RansomHouse wanted things to end?
Extortion Is Most Powerful Before the Data Is Published
In double-extortion ransomware attacks, stolen data has value. But perhaps even more valuable is the fact that it has not yet been made public.
As long as the attacker can say, “Pay us or we’ll publish it,” the data remains a hostage and a bargaining chip. Once the data is published, however, that particular bargaining chip is gone. Publish more, and even more chips disappear.
The damage to the victim is real, especially when personal or confidential information is involved. But from the attacker’s perspective, there is a strange contradiction: every threat they carry out also destroys part of their own leverage.
In other words, self-defeating extortion.
Are 200,000 Files Really 200,000 Valuable Targets?
The number 200,000 sounds impressive. But 200,000 leaked records do not automatically translate into 200,000 profitable victims.
If much of the data consists of names, email addresses, phone numbers or business relationships, criminals still have to turn that information into money through phishing, impersonation or fraud.
Information linking someone to Nichirei may certainly make targeted scams more convincing, so the risk should not be underestimated. But if Nichirei and related companies repeatedly warn customers and business partners about suspicious messages, invoices and payment requests, the success rate of those scams can be reduced.
Leaked data cannot be taken back. But its value as a criminal commodity can still be reduced.
Nichirei Paid a High Price — But It Also Gained Experience
The price Nichirei paid for this incident was undoubtedly high. But the company also gained something that only an organization that has actually been attacked can acquire: real-world experience.
Business continuity plans, backups and incident-response exercises are essential. But some weaknesses only become visible when systems actually go down.
Which operations stop? / Who makes the decisions? / How far does the disruption spread? / How quickly can the business recover?
These are part of an organization’s “shadow” — weaknesses and realities that remain hidden during normal operations.
If Nichirei turns this experience into organizational knowledge, the next time it faces a cyberattack, it will no longer be experiencing one for the first time.
That is an asset the attacker cannot steal or copy.
So What Did RansomHouse Gain?
Breaking into systems, stealing data, maintaining infrastructure, threatening a victim and eventually publishing the stolen files all require time, skills and resources.
Again, we cannot conclude that Nichirei refused to pay, nor can we say that the attack was unprofitable.
Still, watching RansomHouse continue to burn through its remaining cards by publishing more and more data makes it difficult not to wonder:
“We leaked 200,000 files!”
Okay.
But how much money did you make?
The Best Defense May Be Making Ransomware Unprofitable
Ransomware defense usually focuses on one question: How do we stop attackers from getting in?
That is obviously essential. But if ransomware is also viewed as an economic activity, there is another form of defense:
Make successful attacks unprofitable.
Recover quickly. / Limit the damage. / Warn potential secondary victims. / Continue operations without depending on the attacker.
The more organizations can do this, the greater the chance that attackers will successfully break in — and still fail to make money.
For ransomware operators, that may be almost as damaging as failing to break in at all.
Break in. / Steal the data. / Disrupt operations. / Threaten the victim. / Leak the files.
