
Japan Automation Technology was created under the theme of eliminating all shortages in the world by expanding supply capacity. But that is not its true purpose. The reason is simple: even if we did not exist, the flow of industrial automation would never stop, and that future will inevitably arrive one day. Moreover, such a transformation is something society as a whole—including consumers—brings about, not something a single company should hold up as its goal or mission. It is merely a theme, a guiding principle. It can never be the reason for our existence.

In essence, our company exists only so that we can enjoy our daily lives. For that reason, we chose automation as our theme. Company growth is something to pursue only when we grow bored of the present—it is not a goal in itself. The only real questions are: Are we enjoying what we are doing now? And will we be able to keep doing enjoyable things in the future? If every employee can continue to answer “yes” to those two questions, then everything is fine.

Missions and visions in business are nothing more than ceremonial platitudes born out of the separation between ownership and management. In truth, there is no company that genuinely operates for the sake of such lofty ideals. They always prioritize their own survival over their mission. If they truly worked for the good of the world, they would openly share all their knowledge with competitors and the industry as a whole—but they do not. Why? Because their real motivation is the pure ego of wanting themselves or their own company to be the one to bring about the future. That is not inherently bad. What is detestable is when companies hide behind noble words, while their actual actions are completely misaligned.

A company rots once it hands over its shares to boring investors. Because then, chasing meaningless numbers—sales, valuations, and the like—becomes the company’s purpose. That path leads inevitably to “EXIT,” which is in reality nothing more than the death of the company. Who is happy when profits or revenue go up? Not customers. Not employees. Nobody actually cares about those numbers. We are doing this because it is fun. That primal motivation must never be forgotten, and anyone who wavers from it should have nothing to do with the company.

When I say things like this, people sometimes criticize: “Nobody will support a company without social value” or “Do something that benefits others.” But that is completely wrong. Think of Einstein or Steve Jobs. They did not devote themselves to work for the sake of others. They were simply trying to bring forth the principles or products they themselves wanted to pursue, purely and relentlessly. They did not concern themselves with how it might change society. They just chased what they loved, and as a result, society changed a little. People later attached meaning to their work. Support is not necessary. What matters is having comrades who sweat and struggle alongside us, while enjoying the process. Meaning is always retroactively assigned. Everyone will find their own sense of our significance. And because what we love to do will inevitably benefit someone, someday people will end up supporting us without even realizing it.

So do not hold back. Do not do boring things. Do interesting things boldly, without hesitation. That is why this company exists. Enjoy it, and do not be swayed by empty pretenses.

Are we enjoying what we are doing now?

And will we continue doing enjoyable things in the future?


A Fun Company


## Introduction

In recent years, within the context of “full automation” driven by generative AI and large language models, U.S. Big Tech—able to concentrate capital, compute resources, and talent—and China—engaging in national-scale investment—have been the protagonists, leaving Japan structurally disadvantaged. If Japan directly challenges the economies of scale in GPU clusters and foundation model development, differences in fundraising capacity and hiring speed will directly determine competitive outcomes.

However, manufacturing competitiveness is not decided solely by owning a massive general-purpose model in‑house. We should instead focus on the overwhelming strengths Japan already possesses: high‑precision sensors and robots, decades of on‑site improvement know‑how, and vast quality datasets. In domains where these “assets owned by Japan” can be leveraged, the contest is not merely one of capital scale. Japanese companies can also easily collaborate with world‑class hardware firms such as FANUC and Keyence—another major advantage.

Japan offers an ideal environment for this “Delicate Automation.” Chronic labor shortages caused by population decline have turned factories nationwide into a “spontaneous testbed” racing to introduce robots and redesign processes. Moreover, structural factors such as rising numbers of vacant houses and closed schools and surplus land due to depopulation are increasing test environments for robots and new technologies. This creates soil for rapidly iterating verification and improvement of new technologies, offering overwhelmingly more proof‑of‑concept opportunities compared with other countries.

Furthermore, this domain is unlikely to become one where gigantic general foundation models—like LLMs—“winner‑take‑all” the market. Tasks are highly specialized for each process, material, and piece of equipment, with unique constraints such as real‑time control, strict safety requirements, and highly confidential factory data. Crucially, deep, site‑specific training is indispensable. It is structurally impossible for U.S. companies to access and train on data from such delicate manufacturing sites. Japanese companies, by contrast, can easily access this environment. This advantage contains the latent potential to produce an eventual “winner takes all” outcome.

Thus, even though Japan lags in capital strength, by leveraging its domestic assets and social structure as a fulcrum, there exists a realistic path to establishing a global standard. This paper defines the domain as a “next‑generation architecture for high‑mix low‑volume production centered on hardware precision and human knowledge,” and argues that establishing a standard here is Japan’s most reliable route to creating a “world‑class company.”

## Chapter 1: What Is Delicate Automation?

Delicate Automation combines micrometer‑level high‑precision sensors, lightweight domain‑specific AI control, *jidoka* (abnormality detection and automatic stop incorporating human judgment), modular cell design, and a two‑tier edge/cloud architecture to operate high‑mix low‑volume production with low defects and short changeover times. The goal is not mass throughput, but the simultaneous maximization of precision, flexibility, and in‑process quality assurance.

The key point is that each site differs significantly in equipment configuration, materials, temperature/humidity, worker skill, maintenance history, and other conditions—rendering optimization impossible through a single giant model or uniform automation logic. Each cell maintains a “small model” continuously fine‑tuned on its own data; when it detects an anomaly, it stops, a human makes a judgment, and the result is fed back into retraining. This accumulation of “site‑specific tuning” elevates performance, making it unlikely for a single foundation model, like a general LLM, to take the entire world.

Through modularization and digital twins, product changeovers can be validated in virtual space before implementation on the physical line, simultaneously reducing setup time and defect risk. The edge layer handles low‑latency inference and control; the cloud layer aggregates historical data and performs federated learning, gradually improving overall accuracy. The result is lower defect rates, mitigation of labor shortages, and improved capital efficiency of equipment.

Japan is densely packed with factories possessing precision sensors, robots, and long‑accumulated quality/process data—already providing the data sources required for this “site‑specific learning.” Therefore, rather than competing in general‑purpose AI battles that demand massive capital, Japan can target a world standard in this distributed, specialized domain.

## Chapter 2: Why We Cannot Win with “Massive Automation”

The reasons Japan cannot defeat the U.S. by engaging head‑on in full automation or general LLM competition ultimately converge into three points.

### 1. Overwhelming disparity in the capital game

Backed by giant tech firms and deep capital markets, the U.S. has virtually no ceiling on data‑center construction and GPU procurement. It can execute trillions‑of‑yen‑scale upfront investment and continually secure the latest GPUs. In frontier‑class model development and “lights‑out” large‑scale robotization, pure capital accumulation of compute resources and infrastructure determines throughput. Given Japan’s structural disadvantages in fundraising size (stock market depth, VC scale, corporate risk tolerance), it falls behind by design. In domains where capital translates almost linearly into performance, this gap cannot be closed.

### 2. Differences in talent pipelines

The U.S. absorbs PhD talent in foundational fields—AI, control engineering, semiconductor architecture—through domestic universities and immigration, forming a “research cluster” that spans research to commercialization. Authors of top‑tier conference papers, core open‑source contributors, and GPU architecture designers circulate within one ecosystem, generating extraordinarily rapid model improvement cycles. While Japan has excellent individuals, it lacks scale and density, making it difficult for startups and firms to iterate at the same speed. This “difference in improvement cycle rotation” expands the performance gap over time.

### 3. Simplicity of data collection structures

For general LLMs, crawling the public internet yields the text necessary for initial training, and additional data flows continuously from English‑language platforms. In other “scale‑type” domains such as large‑scale agriculture, logistics, and retail automation, vast territory, unified supply‑chain standards, and massive capital investment allow sensors and drones to be deployed widely to accumulate huge, homogeneous datasets. Here, a simple equation holds—“area × capital = data volume = model performance”—and U.S. territory and investment capacity translate directly into advantage. Japan’s land scale and industrial concentration differ; adopting the same collection approach yields little scale benefit.

### Redefining the conditions for victory

Given the three points above, it is unrealistic for Japan to overturn the U.S. in domains where “invest capital and performance rises straightforwardly.” Japan can only dominate in areas where data is complex and highly heterogeneous across sites, where collection and standardization are themselves difficult, and thus simple capital and land scale cannot replicate it. Delicate Automation precisely meets these conditions. Each factory differs in equipment, materials, setup procedures, and workforce skills, with data tied to local tacit knowledge—invalidating the U.S. model of “crawl and bulk‑train.”

Of course, capital and talent are still needed here, but the requirement is not a large quantity of generalist PhDs; it is “domain integration capability” that can interpret long‑term logs hidden within precision component companies and HMLV sites and link them with lightweight AI. Japan already possesses corporate groups and factory networks that embed this capability, enabling it to trace a unique learning curve starting from complex data. Therefore, it is precisely in this domain that Japan can compete—and realistically aim for world leadership.

## Chapter 3: Why the Largest Rival Is Not the U.S. or China, but Germany

Japan’s strengths, in short, are the domestic concentration of precision sensor and robot component supply; on‑site improvement know‑how for running HMLV (high‑mix low‑volume) production; and long‑accumulated quality/process data. Population decline creates continuous automation pressure producing demand for proof‑of‑concepts. This accelerates the circulation of “complex data” training small models for each site, forming a learning curve that other countries struggle to imitate. How do major competitors compare?

The U.S. is overwhelmingly strong in foundational AI, talent, and cloud infrastructure—but these are primarily optimized for generalization and software‑led scale. In Delicate Automation, on‑site integration involving physical equipment and safety requirements becomes the bottleneck, exposing a hollowing out of disassembled manufacturing process knowledge accumulated over decades. While acquisition or systems integration can partly fill gaps, the “muddy” learning cycle of repeated fine‑tuning per equipment difference cannot be replicated overnight.

China’s strengths are deployment speed and capital infusion, which have built advantages in standardized automation for mass production. Yet in delicate domains, dependency on imported upstream technologies—nano‑level precision sensors and processing equipment—and geopolitical risks destabilize supply. Moreover, cultural focus on optimizing volume and cost weakens incentives to redirect management resources toward the minute quality optimization characteristic of HMLV, leaving variability in the “quality” of data accumulated domestically.

The largest rival is Germany. Germany also possesses high‑precision machinery and process management culture—an asset structure almost mirroring Japan’s—and has demonstrated international standard‑setting power through Industry 4.0. Whereas Japan tends to focus inward on domestic closed improvements, Germany has, from early on, advanced data collaboration across Europe via common languages like OPC UA, honing outward standardization capabilities as a platform. This “extensional effect of standardization” may, over time, create divergence—Japan’s greatest risk.

In sum, the U.S. presses via software and capital, China via scale, and Germany via standardization, yet all face high hurdles in simultaneously reproducing within a single country the triad of “precision components + diverse HMLV sites + long‑term quality data.” To maintain and expand this advantage, Japan must establish domestic data specifications and API standards ahead of Germany and publish them in internationally compatible form. If achieved, Japan can lock in top global positioning through a dual advantage of trained models and standards compliance.

## Chapter 4: Becoming No.1 in Japan Means Becoming No.1 in the World

Delicate Automation, because conditions differ per site and deployment/adjustment requires human labor, is unlikely to form a structure where a giant platform sweeps the world at once. Labor intensity and local adaptation become bottlenecks, discouraging excessive foreign over‑investment and creating a “comfortable battlefield” with low entry barriers for Japan. Why, then, can Japanese firms become world No.1 in such a market?

The key is to separate physical integration from data/software by layers. The lower layer of deployment and maintenance is regionally distributed and hard to oligopolize, but if a “manufacturing OS” layer that provides common APIs, data formats, and model updates is established above it, this layer alone can produce de facto oligopoly through network externalities and learning curves. The player that first gathers wide‑area data from Japan’s diverse factories and spins a high‑frequency update cycle can accelerate accuracy faster than others and thereby raise switching costs. The result is a position of “not complete monopoly, yet largest global share and profitability.”

This mechanism is a general principle repeatedly observed in other industries. Taiwan’s semiconductor sector, with early policy support and TSMC at its core, aggregated equipment suppliers, design customers, and talent, compounding yield‑learning to fix itself as the world’s manufacturing base. Saudi Arabia used low‑cost oilfields and state capital to gain price adjustment power and restrain others’ entry. U.S. internet companies, through regulatory environment, huge domestic demand, universities, and VC, crossed the network externality threshold early and captured global standards. In every case the “country where it started” initial conditions determined victory; latecomers could not reproduce the same structure.

Japan has similar success stories. In manga and games, domestic concentration of editorial systems, creators, and consumers honed content and operational know‑how, which was exported globally to set standards. Delicate Automation is the industrial version; because it targets manufacturing infrastructure, its economic impact surpasses that of manga or games. With initial conditions—precision sensor and robot firms, HMLV factories, automation demand from labor shortages, long‑term quality data—already in place domestically, if Japan first establishes a national standard here, then like Taiwan’s semiconductors or U.S. internet firms, it can cross borders already “pre‑trained,” carrying an embedded entry barrier. The fact that domestic leadership = largest data holder itself becomes a weapon for global expansion, creating room for a Japan‑origin world leader even in a market “hard to fully monopolize.”

## Chapter 5: Scenario Where a Startup Overtakes Existing Giants

Conventional wisdom suggests existing giants will first build an advantage in this domain. Companies such as FANUC, Yaskawa Electric, Mitsubishi Electric, Omron, and Keyence already possess precision hardware development and manufacturing capabilities, global sales networks, brand trust, and service personnel, and can supply the major components composing Delicate Automation in‑house. In typical incremental innovation, this asset gap would determine the outcome.

Even so, in the early formation of a new industry, startups more easily seize the “global standard.” Large firms are bound by enclosure models centered on their own products and by department‑specific KPIs, making it difficult to justify building cross‑vendor data standards or a neutral cell OS themselves. Startups face no risk of cannibalizing existing revenues and can iterate rapidly with a multi‑vendor, software‑centric design from the outset. Factory users, motivated to avoid single‑vendor lock‑in, are also more willing to accept a neutral mediator. This incentive structure opens a window for acquiring the common foundation.

The strategy proceeds in stages. First, specialize in an ultra‑niche such as injection molding or cutting of a specific material, and within a short term visualize improvements in defect rate and changeover time via lightweight AI and simple digital twins to build a “neutral nucleus.” Next, abstract the data extraction and normalization pipeline, and—through a technical committee involving multiple giants—define common schemas and APIs, change procedures, and IP rules. Data remains inside the factory; only training results are exchanged through a distributed update method that protects confidentiality while raising overall accuracy. Simultaneously, institutionalize neutrality via dispersed shareholding and partial code openness.

On this foundation, accelerate domestic horizontal expansion via the cell OS; as connected cells and update frequency increase, learning curve effects arise. Accumulation of historical templates raises switching costs, strengthening platform stickiness. After reaching critical mass, export “domestically validated templates” to high‑mix low‑volume factories in Germany and ASEAN, incorporating data from different regulations and environments to enhance generalization and widen the moat.

Ultimately, large firms face a binary choice: continue on their own path while suffering accuracy lag, or participate in the neutral foundation to maximize hardware sales. As participation expands, the startup self‑reinforces data advantage and switching costs, completing de facto oligopoly over the upper layer while hardware remains distributed. The manufacturing OS formed domestically then scales into the global standard, establishing a world‑leading position for a Japan‑origin startup that orchestrates existing giants.

## Conclusion: Here, Japan Can Build a World‑Leading Company

Delicate Automation is a “Japan‑origin next‑generation manufacturing OS” that reconstructs long‑accumulated precision sensors, robots, on‑site improvement know‑how, and quality data using lightweight AI. Unlike capital battles in large‑scale automation or general LLMs, site‑specific diversity and labor intensity block giant players from sweeping the field, making it easier to establish a standard starting from the domestic cluster. Thus, domestic victory = international standardization = quickest path to a top global position—an exceptionally rare battlefield.

The structure to maximize this opportunity is clear. Existing firms provide precision hardware and customer bases; startups build a neutral data standard and model distribution platform. Cooperation between the two forms a dual moat of “precision hardware + neutral software,” compounding learning curves and switching costs. By the time the “manufacturing OS” polished domestically is exported abroad, it already possesses data, track record, and development speed that other countries struggle to replicate.

The remaining task is to unambiguously choose this battlefield and make decisions that outpace Germany, the U.S., and China in execution speed. If Japan seizes the standard now, a world‑leading company originating from Japan is fully achievable. Delicate Automation is the most reproducible stage for Japan to once again prove that “No.1 domestically = No.1 globally.”


Delicate Automation


## Prologue – When Short‑Term “Flash” Fades and Long‑Term “Glow” Emerges

We humans are easily lured by immediate praise and metrics we can flaunt: a dazzling slide deck that wins the room, an introduction crammed with titles and certifications, a highlight reel of quick wins. Such displays look impressive—until time passes. The brighter the flare, the faster the colors drain, and the more trust reserves we unknowingly burn. By contrast, the moves that seem unpolished or even costly today—candid self‑disclosure, awkward apologies, quiet behind‑the‑scenes work—compound into unshakable trust and opportunity over ten or twenty years.

My thesis is straightforward: **When you assume every relationship is long term, self‑centered impulses recede as the rational choice, and both individuals and communities grow stronger.** I urge readers to adopt this long‑range lens and cultivate their ties accordingly.


## Chapter 1 – The Anatomy of Short‑Term Maximization

### 1. What Does “Short‑Term Optimal” Behavior Look Like?

People bent on maximizing near‑term rewards typically fall into one or more of the patterns below:

* **One‑sided kindness** — offering help nobody asked for and extracting gratitude in return. Think: announcing “I finished all the prep for you” and leaving colleagues scrambling to adjust.
* **Excessive self‑promotion** — spotlighting titles, certifications, and numbers while erasing the process and partners who made them possible. “I led every part of this project,” one proclaims, air‑brushing the team out of the picture.
* **Surface‑level empathy** — churning out generic praise that requires no real listening or risk.
* **Relationship resets** — bolting the moment criticism or failure looms, then migrating to a new crowd in search of fresh applause.

All four share one trait: **they score quick visibility but arrest deep trust.**

### 2. The Psychology and Upbringing Behind the Pattern

Two roots intertwine.

**(1) Communal narcissism.** If childhood taught you “stand out and you’ll be loved,” you may cloak your craving for applause in acts of ‘kindness’ and ‘team spirit,’ feeding on others’ gaze to validate existence.

**(2) Hyper‑reciprocity.** When home and school drilled “always pay back favors,” helping turns into debt transfer. Both giver and receiver feel a weight, and relationships drift toward transactions rather than camaraderie.

Together they breed the **reset habit**. The moment imperfection shows, leaving seems easier than facing critique. One clean escape cements itself as a nifty strategy, reinforcing the belief, “So long as I impress quickly, that’s enough,” and the cycle repeats.

### 3. The Hidden Bill for Resetting

Escape looks cheap; over time it’s exorbitant. First, **the cost of trust soars**—each abandoned history makes partners wonder whether you’ll vanish again. Second, **collaboration opportunities shrink**—the deepest ventures sprout only in soil tilled over years. Third, the mind internalizes a damaging lesson: shallow ties suffice, so why invest long? What looks like local optimization quietly locks your growth ceiling low.


## Chapter 2 – The Compound Interest of Long‑Term Bonds

### 1. How a Long Horizon Rewires Behavior

Picture yourself still collaborating with the same person ten years from now. Suddenly lies aren’t worth it, breaking small promises feels absurd, bragging loses appeal; sober consistency pays. Simply imagining longevity makes **frank debate, shared vulnerability, and tough but honest feedback** the rational path.

### 2. Cooperation Logic in Behavioral Science

Iterated prisoner’s‑dilemma experiments boil down to this: when players know they’ll meet again, they pick the deferred gains of cooperation over the sugar rush of betrayal. **The larger the shadow of the future, the stronger altruism becomes.** The same logic governs startups, family businesses, and research partnerships.

Reputation spreads exponentially. Demonstrate steady conduct over the long haul and word of your reliability precedes you, funneling information and resources your way—**social capital compounding** at rates no short‑term accolade can match.

### 3. The Time‑Reversal Law—“Awkward Now, Brilliant Later”

Stretch the timeline far enough and the rule flips: the acts that look awkward now become badges of honor later.

* **Sharing failure and weakness.** Opening with your blunders may feel self‑deprecating today; a decade on, it crowns you as transparent and trustworthy.
* **Handing credit to teammates.** Saying, “I only handled the last five percent,” sounds over‑humble at first. Years later you’re revered as the leader who elevates others.
* **Anonymous generosity.** Unsigned donations and backstage labor earn no spotlight, yet gratitude travels quietly through networks, bringing unforeseen introductions and offers.

The inverse holds too: flashy boasting fades within twenty‑four hours, morphing into distrust once substance is weighed. Those who shout loudest today often have the least to say when time calls their bluff—that is the cold calculus of time reversal.


## Epilogue – What Remains at the End of Life

When the final hour comes, it isn’t last year’s party buddies who gather at your bedside. It’s family, decade‑old friends, colleagues who weathered storms with you, a partner whose love matured over seasons. **Our capacity for deep happiness correlates with how skilled we are at sustaining long bonds.**

So what builds that skill? The answer is deceptively simple: **decide to treat every relationship as long term.** One mental commitment shifts daily decisions, steering you from momentary impulse toward future trust.

A concrete first step: **celebrate each friend’s birthday every single year.** A brief message or quick call suffices. Marking one another’s milestones, without fail, plants a quiet ritual that whispers, “We are in this for the long haul.” Keep it up and you’ll sense it internally—these relationships will still stand ten, twenty years hence. Yes, the greeting delights the recipient, but more crucially it forces your own mind into “long‑term mode.” For that reason, I prize this habit above all others.

When I judge character, my first question is, “Do they sustain relationships over decades?” Those who can answer yes value long‑run credibility over quick profit, making them safe stewards of major projects and deep collaborations.

**Short‑term swagger ages into long‑term shame; short‑term awkwardness matures into long‑term prestige.** Carry the shadow of the future on your back as you choose today’s actions—that is the fastest route to genuine magnitude and allure. Cultivate long‑term ties and keep investing those humble, unglamorous strokes of care. They will be the badge you wear with pride when the curtain falls.


Long‑Term Relationships Change Us


## 0. Where Affection Comes From

Every time we grow fond of someone, **uncertainty plummets** first. The other person’s inner workings shine through, the prospect of harm or betrayal recedes, and our hearts slacken—the circuit of resonance closes.

Social psychologists frame this as *Uncertainty Reduction Theory* (Berger & Calabrese, 1975). Children and animals are easy to love precisely because they wear no social mask and hide no latent risk.


## 1. Self‑disclosure Is a Peeling Process

A baby fears nothing. Whatever enters the eye is received whole; whatever stirs inside is released whole. That is the native form of self‑disclosure.

Growing up, we taste ridicule, scolding, and the pain of competition, and we conclude that offering ourselves entire is dangerous. In that instant the mind straps on armor. Armor is handy, yet it fogs our outline and buries the seeds of affection.

Disclosure, then, is not about **adding** tricks but about **removing** excess. What we once did unconsciously we must now reclaim deliberately. The task is simple to name, hard to do: watch the fear and shed it, layer by layer.


## 2. Hierarchy of Purpose and the Sense That “The Universe Is One”

Children dash forward naked and crash because the world is still a *private island*. Islands collide painfully.

As vision widens and one feels that *self and other are just separate vortices in the same stream*, interests overlap by themselves. When the sense that **the universe is one** sprouts, the line between like and dislike thins.

*Survive → Nurture bonds → Realize the self → Cultivate community*

Each step dissolves another slice of the gain‑loss ledger. Seen from the top step, the joys of self and other look like ripples on a single river. Drop the armor at that altitude and disclosure is read not as selfishness but as a clear motion that smooths the flow. Innocence and maturity finally inhabit the same body.


## 3. Encountering Immaturity, Friction, and Growth

Some still clutch the island map. Faced with them, two roads appear:

* **Keep distance.** Re‑armor and wait while their map rewrites itself.
* **Embrace.** Widen our reach and walk while holding their fear as well.

The second road is harder. We must keep disclosure from sliding into exhibition and supply safety with deeds, not words. Yet that struggle stretches us and fattens the channel of affection.

Friction is not a verdict of good or evil. It signals that the maps have not yet merged. Find the blank where new lines can be drawn and the flow reunites.


## 4. Look Straight into the Eyes

A baby never wonders how it is being scored. It simply mirrors the person before it and mirrors itself. For an adult, to “look straight” is to recreate that state on purpose.

1. Do not deposit your worth in another’s gaze.
2. Do not dam the impulse of this very moment.

Meet the eyes, sense your breath, pulse, and faint tension. Boundaries blur and the field of interests expands. Even before words, this stance softens the air.


## 5. Courage to Stand Unarmored

Self‑disclosure is “Don’t think—take it off.” It is the act of dropping armor to vibrate in phase with the universe. We love someone the instant they show that courage—when **uncertainty shrinks and the boundary turns transparent**.

Start by locking eyes like an infant. No words are needed. Affection is already in motion, the universe hugging itself through us.


On Loving Others


## Humans as Functions

I see human beings as functions—systems that take in inputs from the environment and produce outputs based on parameters shaped by experience and learning. I don’t believe in some fixed “essence” or personality residing inside people. Behavior is the result of inputs and conditions at a particular moment in time, not a revelation of some immutable core.

That’s why I don’t dislike people. Not “rarely”—I simply don’t. While I may feel discomfort or disapproval in response to certain actions, I don’t project that feeling onto the person as a whole. In the past, I used to have those kinds of emotional reactions, but not anymore. I evaluate behavior based on context, not people based on assumptions.

## Why People Dislike Others as People

Many people, however, respond differently. When they see someone behave poorly or hold immature views, they don’t just judge the act—they reject the person entirely. Instead of separating behavior from identity, they assume the behavior reflects something wrong at the core of the person. “Someone who acts like that must be that kind of person,” they think.

That reaction, while emotionally charged, is cognitively efficient. Evaluating each behavior in its full context requires time and mental energy. Labeling someone as “bad” simplifies things. In social settings, sharing those labels also serves a purpose—it communicates values, builds in-group solidarity, and defines social boundaries. From a neurological standpoint, our brains also tend to remember negative information more vividly than positive. Once someone leaves a bad impression, it’s difficult to overwrite.

So while disliking someone might appear irrational, it actually serves as a kind of shortcut: it minimizes cognitive effort and helps stabilize social dynamics, even if it sacrifices accuracy.

## The Asymmetry of Change

I know that human beings change—because I’ve changed. I wasn’t always able to treat people with kindness or think beyond myself. With time and experience, I’ve grown, expanded my thinking, and developed different behaviors. That’s not a theory—it’s something I’ve lived.

But this creates an asymmetry. I can track my own change in detail, from the inside. I know the thought processes, the struggles, the intentions. With other people, I only see fragments: behaviors, choices, outcomes. Their inner transformations—if they happen—are often invisible. This makes it easy to assume that while I’ve changed, others cannot.

That discrepancy creates a kind of illusion: we trust our own growth but doubt others’ capacity for the same. It’s a structural bias, not a fair assessment.

## Choosing to Guide Instead of Dismissing

When I encounter someone who expresses an immature idea or behaves in a harmful way, I don’t dislike or look down on them. Instead, I consciously shift into a different mode of thought: How can I guide this person? What kind of conversation or influence might lead them toward change?

This isn’t because I expect it to be easy. On the contrary, I know that influencing another person’s behavior is one of the hardest things to do. But precisely because it’s difficult, I see it as a valuable challenge. It forces me to grow. It gives me a chance to confront the limits of communication, empathy, and patience. In that sense, encountering someone who needs guidance isn’t a burden—it’s a fortunate opportunity to learn something I couldn’t learn otherwise.

Trying to help someone change is not just about them. It becomes a mirror for my own development.

## Seeing Change Instead of Fixing Identity

I judge behavior, not people. And I see those behaviors as the output of a dynamic process, not the expression of something fixed or eternal. Human beings are constantly changing, often in ways that are hard to observe from the outside. That’s why I resist the impulse to reduce someone to a single label based on one moment.

Closing off to someone because of one behavior means cutting off a whole range of future possibilities. But recognizing that they might change—might grow—means staying open to re-evaluating. Not out of naïve optimism, but out of respect for how real transformation happens over time.

To dislike someone is to stop watching, to stop expecting, to stop engaging. I would rather stay in a position where I can still observe, still adapt, and still be surprised. Because human beings are not finished objects. We are constantly in motion, shaped by what we take in and what we’ve been through.

Disliking someone? That closes a door. But staying open to change—that’s where I think the real work begins.


Latest Memos

A Fun Company

Delicate Automation

Long‑Term Relationships Change Us

On Loving Others

On Disliking Others

Anonymous Message