Saigon: A Name That Never Disappeared

English Tiếng Việt

In 1976, after the end of the war and the reunification of Vietnam, Saigon was officially renamed Ho Chi Minh City. On administrative documents, maps, stamps, and government forms, the new name has existed for almost half a century.

Yet in daily speech, in memory, in poetry, in music, in business names, and in Vietnamese communities around the world, the name Saigon continues to live.

From lelf to right: Bến Thành Central Market; Notre-Dame Cathetral; Central Post Office; Norodom Palace; Lysée Petrus Ký; A boulevard in Saigon; Central Park Tao Đàn 

This raises an interesting question: why does a name that was officially replaced nearly fifty years ago still refuse to disappear?

To me, the answer lies in three dimensions: language, culture, and history.

A Name with Linguistic Power

From a linguistic point of view, “Saigon” has a natural advantage. It is short, compact, easy to say, easy to remember, and easy to use in everyday conversation.

By contrast, “Ho Chi Minh City” is long. It works well in administrative documents, official speeches, maps, and forms. But in natural speech, people tend to choose the shorter and more familiar name.

The name Saigon also lives in everyday expressions: “going into Saigon,” “going up to Saigon,” “Saigon rain,” “Saigon people.” It even lives in the familiar street vendor calls once heard across the city: “Saigon baguette, full inside, fragrant with butter, Saigon baguette, one thousand dong a loaf...” 

These phrases need no administrative approval. They are passed from mouth to mouth, from the South all the way to the North, proving that the name Saigon has entered deeply into the living language of the Vietnamese people.

Very few people, in casual daily conversation, would naturally say: “I am going to Ho Chi Minh City.”

Language has its own rules. People tend to choose words that are short, easy to pronounce, and emotionally familiar. This is especially true in the South of Vietnam, where everyday speech often favors shorter, lighter, and more direct expressions.

For that reason, “Saigon” continues to be used, not necessarily as a political statement, but because it is natural, convenient, and intimate.

Most major city names in Vietnam are also short: Hanoi, Hue, Da Nang, Can Tho, My Tho, Bien Hoa, Vung Tau, Hai Phong. In that flow of Vietnamese place names, “Saigon” feels natural. It belongs to the rhythm of the language.

A Name That Lives in Culture

A city name is not only a label on a map. It is also a sound carried by memory.

“Saigon” appears in music, poetry, literature, and in the intimate language of generations. People sing:

Saigon is beautiful, Saigon, oh Saigon!

 

People remember the famous poetic line:

The Saigon sun suddenly feels cool as you walk beneath it...

Such songs and poems do not merely name a city. They evoke an entire cultural atmosphere: sunlight, sudden rain, tree-lined streets, cafés, schools, street sounds, afternoon shadows, and old memories of love and youth.

In art, not every name can enter the heart. A name may be printed on official papers, but to become poetry it must have music. “Saigon” has that music. It is soft, brief, and resonant. It can live inside a poem, a song, a love letter, or a simple call from one person to another.

“Ho Chi Minh City,” on the other hand, has a more administrative character. It is the name of documents, addresses, forms, and official usage. It is much harder for it to become a natural poetic image.

It is no accident that so many brands still use the name Saigon: Bia Saigon, Saigon Co.op, Saigontourist, Saigon Newport, Saigon Times, Saigon Centre. Businesses choose names with strong recognition, and “Saigon” still carries enormous cultural and commercial value.

The same is true overseas. Wherever there are large Vietnamese communities, people speak of Little Saigon. No one calls these communities “Little Ho Chi Minh City.” This shows that “Saigon” is not only the name of a city. It is also the name of a shared memory.

A Name Rooted in History

Saigon is also attached to a historical title that many Vietnamese people still recognize with pride: the Pearl of the Far East.

Whatever one’s political perspective may be, it is difficult to deny that Saigon was once one of the most distinctive cities in Southeast Asia. The French built Saigon with the ambition of creating a smaller Paris in the East. The city had Notre-Dame Cathedral, the Central Post Office, Norodom Palace, the zoo, museums, elegant schools, straight boulevards, rows of trees, and cool public parks.

Saigon was not merely an administrative center. It was a city with style: open, dynamic, graceful, and shaped by a mixture of East and West, tradition and modernity.

When people call Saigon “the Pearl of the Far East,” they are not speaking about an administrative decision. They are speaking about a historical image, a period of urban development, and an Eastern dream carrying the shadow of Paris.

For that reason, changing the name of Saigon was not simply a matter of changing the name of a city. It also meant replacing a historical symbol with a political name.

Renaming Cities in History

Renaming cities is not rare. Around the world, many cities have been renamed for political reasons. St. Petersburg became Leningrad, and later returned to St. Petersburg. Stalingrad later became Volgograd.

In Vietnam, Thang Long was also renamed Hanoi under the Nguyen dynasty. That decision also carried a political dimension, since the name Thang Long was associated with earlier dynasties such as the Ly, Tran, and Le. Yet the name Hanoi was eventually widely accepted. Today, Thang Long lives on as a historical and cultural symbol, while Hanoi lives as the modern administrative and cultural name. The two names do not cancel each other out.

The case of Saigon is different. Nearly fifty years later, the new administrative name has still not fully replaced the old one in natural life. On paper, the city is Ho Chi Minh City. But in speech, in brands, in music, in memory, and in Vietnamese communities abroad, the name Saigon continues to appear.

This reveals a simple truth: a name does not live by authority alone. It lives by human acceptance.

The Administrative Name and the Name in People’s Hearts

Some names are created by resolutions. Other names are preserved by memory.

Ho Chi Minh City is the administrative name. Saigon is the cultural name, the historical name, and the name of everyday life.

For many people who were born and raised in the South before 1975, Saigon is childhood, school, streets, noon sunlight, sudden rain, and a world that has disappeared but never truly gone away.

Even many people born after 1975 still say “Saigon” in daily life. For them, it is not necessarily a political declaration. It is simply the most natural name.

Language is sometimes more honest than slogans. People call a place by the name that feels close to them. And when millions of people continue to say “Saigon,” that fact itself becomes cultural evidence.

One Day

History is always moving. An administrative decision can change the name of a city in a single day. But changing human memory requires much more.

Nearly fifty years have passed. The name Saigon has not disappeared. It remains present in songs, poems, business names, street signs, daily speech, and in the hearts of many generations of Vietnamese people.

Saigon lives not only in its streets, its songs, and its famous brands. It also lives in the gentle melody of the Southern Vietnamese voice, in the softly spoken "dạ," in the affectionate way people address one another as anh, chị, cô, or chú, and in the quiet elegance of an urban culture that is at once courteous, warm, and deeply human.

Perhaps one day, the city will officially regain the name Saigon. If that happens, it will not be the creation of a new name. It will simply be the return of a name that never truly disappeared.

Saigon is still Saigon.

Sài Gòn: Một cái tên chưa bao giờ mất

English Tiếng Việt

Năm 1976, sau khi chiến tranh kết thúc và đất nước thống nhất, Sài Gòn chính thức bị đổi tên thành Thành phố Hồ Chí Minh. Trên giấy tờ hành chính, tên mới ấy đã tồn tại gần nửa thế kỷ. Nhưng trong đời sống hằng ngày, trong ký ức, trong thơ ca, âm nhạc, thương hiệu và trong cộng đồng người Việt khắp thế giới, cái tên Sài Gòn vẫn tiếp tục sống.

Từ trái sang phải: chợ Bến Thành; Nhà Thờ Đức Bà; Bưu Điện; Dinh Norodom; Trường Petrus Ký; một con đường ở Saigon; Công viên Tao Đàn

Điều đó đặt ra một câu hỏi thú vị: tại sao sau gần 50 năm, một cái tên đã bị thay thế trên bản đồ hành chính vẫn không biến mất?

Câu trả lời, theo tôi, nằm ở ba khía cạnh: ngôn ngữ, văn hóa và lịch sử.

Một cái tên có sức mạnh ngôn ngữ

Về mặt ngôn ngữ, “Sài Gòn” có một lợi thế rất tự nhiên. Nó chỉ gồm hai âm tiết: ngắn, gọn, dễ gọi, dễ nhớ, dễ đi vào lời nói hằng ngày.

Trong khi đó, “Thành phố Hồ Chí Minh” là một tên gọi dài. Nó phù hợp với văn bản hành chính, con dấu, giấy tờ, đơn từ. Nhưng trong giao tiếp tự nhiên, người dân thường chọn cách nói ngắn hơn.

Tên Sài Gòn còn sống trong những cách nói rất đời thường: “đi vô Sài Gòn”, “lên Sài Gòn”, “mưa Sài Gòn”, “người Sài Gòn”. Nó sống trong cả những tiếng rao bình dân từng vang trên đường phố: “Bánh mì Sài Gòn, đặc ruột thơm bơ, bánh mì Sài Gòn, một ngàn một ổ...” 

Những câu nói ấy không cần văn bản hành chính nào bảo chứng. Chúng được truyền từ miệng người này sang miệng người khác, từ miền Nam lan ra cả miền Bắc, như một bằng chứng rằng cái tên Sài Gòn đã đi sâu vào đời sống ngôn ngữ của người Việt. 

Ít ai trong đời sống thường ngày nói một cách tự nhiên rằng: “Tôi vào Thành phố Hồ Chí Minh.”

Ngôn ngữ có quy luật riêng của nó. Người dân luôn có khuynh hướng chọn những từ ngắn, dễ phát âm và giàu cảm xúc. Đặc biệt, người miền Nam lại càng có thói quen nói gọn, nói nhẹ, nói nhanh. Vì vậy, “Sài Gòn” vẫn tiếp tục được dùng, không phải vì một phong trào nào, mà vì nó thuận miệng và thân thuộc.

Hầu hết tên các thành phố lớn ở Việt Nam cũng rất ngắn: Hà Nội, Huế, Đà Nẵng, Cần Thơ, Mỹ Tho, Biên Hòa, Vũng Tàu, Hải Phòng. Trong dòng chảy ấy, “Sài Gòn” là một cái tên rất Việt Nam, rất tự nhiên.

Một cái tên sống trong văn hóa

Một cái tên không chỉ để định vị trên bản đồ. Nó còn là âm thanh của ký ức.

“Sài Gòn” xuất hiện trong âm nhạc, trong thơ ca, trong văn chương, trong lời nói thân mật của nhiều thế hệ. Người ta hát:

Sài Gòn đẹp lắm, Sài Gòn ơi! Sài Gòn ơi!

Người ta nhớ câu thơ:

Nắng Sài Gòn em đi mà chợt mát...

Những câu hát, câu thơ ấy không chỉ gọi tên một thành phố. Chúng gọi dậy một không gian văn hóa: nắng, mưa, hàng cây, con đường, quán cà phê, trường học, tiếng xe, tiếng rao, những buổi chiều và những mối tình đã xa.

Trong nghệ thuật, không phải cái tên nào cũng có thể đi vào lòng người. Một cái tên có thể được in trên giấy tờ, nhưng để trở thành thi ca thì nó phải có nhạc tính. “Sài Gòn” có điều đó. Nó mềm, ngắn, có âm vang riêng. Nó có thể đứng trong một câu thơ, một câu hát, một lời nhắn, một tiếng gọi.

Ngược lại, “Thành phố Hồ Chí Minh” mang tính hành chính nhiều hơn. Nó là tên của văn bản, của địa chỉ, của đơn từ. Nó khó trở thành một hình tượng thi ca tự nhiên.

Không phải ngẫu nhiên mà rất nhiều thương hiệu vẫn dùng tên Sài Gòn: Bia Sài Gòn, Saigon Co.op, Saigontourist, Saigon Newport, Saigon Times, Saigon Centre. Thương hiệu luôn chọn những cái tên có sức nhận diện mạnh. Và trong thị trường, cái tên “Sài Gòn” vẫn có sức sống rất lớn.

Ở hải ngoại cũng vậy. Nơi nào có đông người Việt, người ta gọi là Little Saigon. Không ai gọi là Little Ho Chi Minh City. Điều đó cho thấy “Sài Gòn” không chỉ là tên của một thành phố, mà còn là tên của một cộng đồng ký ức.

Một cái tên gắn với lịch sử

Sài Gòn còn gắn liền với một danh xưng mà nhiều người Việt vẫn tự hào: Hòn ngọc Viễn Đông.

Dù nhìn từ phía nào của lịch sử, khó ai phủ nhận rằng Sài Gòn từng là một đô thị đặc biệt của Đông Nam Á. Người Pháp đã xây dựng Sài Gòn với tham vọng tạo nên một Paris nhỏ ở phương Đông. Thành phố có Nhà thờ Đức Bà, Bưu điện Trung tâm, Dinh Norodom, Sở thú, viện bảo tàng, trường học, đại lộ thẳng tắp, hàng cây hai bên đường và những công viên mát mẻ.

Sài Gòn không chỉ là một trung tâm hành chính. Nó là một đô thị có phong cách: cởi mở, năng động, thanh lịch, pha trộn Đông và Tây, truyền thống và hiện đại.

Khi nói “Sài Gòn là Hòn ngọc Viễn Đông”, người ta không nói về một quyết định hành chính. Người ta nói về một hình ảnh lịch sử, một thời kỳ đô thị hóa, một giấc mơ phương Đông mang bóng dáng Paris.

Vì thế, đổi tên Sài Gòn không chỉ là đổi tên một thành phố. Nó còn là thay thế một biểu tượng lịch sử bằng một tên gọi chính trị.

Những lần đổi tên trong lịch sử

Việc đổi tên thành phố không phải là chuyện hiếm. Trên thế giới, nhiều thành phố từng bị đổi tên vì lý do chính trị. St. Petersburg từng thành Leningrad, rồi sau đó trở lại St. Petersburg. Stalingrad sau này thành Volgograd.

Ở Việt Nam, Thăng Long cũng từng được đổi thành Hà Nội dưới triều Nguyễn. Quyết định ấy cũng có yếu tố chính trị, vì tên Thăng Long gắn với các triều đại cũ như Lý, Trần, Lê. Nhưng tên Hà Nội cuối cùng được xã hội chấp nhận khá rộng rãi. Ngày nay, Thăng Long vẫn sống như một biểu tượng lịch sử, còn Hà Nội sống như tên gọi hành chính và văn hóa hiện đại. Hai cái tên không loại trừ nhau.

Trường hợp Sài Gòn thì khác. Sau gần 50 năm, tên hành chính mới vẫn chưa thay thế được tên cũ trong đời sống tự nhiên. Trên giấy tờ là Thành phố Hồ Chí Minh, nhưng trong lời nói, trong thương hiệu, trong âm nhạc, trong ký ức và trong cộng đồng người Việt hải ngoại, cái tên Sài Gòn vẫn tiếp tục hiện diện.

Điều đó cho thấy một sự thật đơn giản: một cái tên không chỉ sống nhờ quyền lực. Nó sống nhờ sự chấp nhận của con người.

Tên hành chính và tên trong lòng người

Có những cái tên được đặt bằng nghị quyết. Có những cái tên được giữ lại bằng ký ức.

Thành phố Hồ Chí Minh là tên hành chính. Sài Gòn là tên văn hóa, tên lịch sử, tên của đời sống thường ngày.

Đối với nhiều người sinh ra và lớn lên ở miền Nam trước năm 1975, Sài Gòn là tuổi thơ, là trường học, là con đường, là buổi trưa nắng, là cơn mưa bất chợt, là một thế giới đã mất nhưng chưa bao giờ thật sự rời xa.

Ngay cả nhiều người sinh sau năm 1975 cũng vẫn nói “Sài Gòn” trong đời sống hằng ngày. Với họ, đó không phải là một tuyên bố chính trị. Đó đơn giản là cách gọi tự nhiên nhất.

Ngôn ngữ đôi khi trung thực hơn khẩu hiệu. Người dân gọi một nơi bằng cái tên mà họ cảm thấy gần gũi. Và khi hàng triệu người vẫn tiếp tục nói “Sài Gòn”, điều đó tự nó đã là một bằng chứng văn hóa.

Một ngày nào đó

Lịch sử luôn vận động. Có những quyết định hành chính có thể thay đổi tên một thành phố trong một ngày. Nhưng để thay đổi ký ức của con người thì cần nhiều hơn thế.

Gần 50 năm đã trôi qua. Cái tên Sài Gòn vẫn không biến mất. Nó vẫn có mặt trong câu hát, câu thơ, thương hiệu, biển hiệu, lời nói hằng ngày và trong trái tim của nhiều thế hệ người Việt.

Sài Gòn không chỉ sống trong tên đường, bài hát hay thương hiệu. Nó còn sống trong giọng nói miền Nam, trong tiếng “dạ” rất nhẹ, trong cách người ta gọi nhau bằng anh, chị, cô, chú, trong sự mềm mại của một nền văn hóa đô thị vừa lịch sự vừa gần gũi

Có lẽ một ngày nào đó, thành phố ấy sẽ chính thức lấy lại tên Sài Gòn. Nếu điều đó xảy ra, đó không phải là đặt lại một cái tên mới. Đó chỉ là sự trở về của một cái tên chưa bao giờ thật sự mất.

Sài Gòn vẫn là Sài Gòn.

The Foundation of AI: Why Large Language Models Are Only the House, Not the Basement

The Basement Beneath the Machine

Large language models are the grand houses of the present AI boom. But the real story begins underground.

Every technological age has its showpiece. The steam age had the locomotive. The electrical age had the glowing city. The internet age had the browser, that rectangular window through which humanity learned to shop, argue, publish, flirt, procrastinate, and occasionally discover wisdom.

The present age has the chatbot.

Ask a question, and it answers. Give it a document, and it summarizes. Request a poem, a contract, a recipe, a Python script, or a bedtime story about a melancholy dragon, and it obliges with unnerving fluency.

It is tempting, therefore, to mistake the visible marvel for the whole field. To many people, artificial intelligence now means Large Language Models, or LLMs. In the public imagination, AI has acquired a face, and that face is conversational.

This is understandable. It is also misleading.

A Large Language Model is not the foundation of artificial intelligence. It is a house built upon that foundation. It may be a very impressive house, with glass walls, automatic doors, and a library that talks back. But it is still a house.

And no house, however elegant, stands without a basement.

A city of possible houses: LLM (langue AI), Biology AI and Physical AI (Robotic) 

The unseen structure

The basement of AI is less glamorous than the rooms above it. It does not greet users in polished sentences. It does not generate images of astronauts riding horses or compose polite emails to dissatisfied customers.

It consists instead of mathematics, probability, statistics, classical machine learning, and neural networks.

This is where the load-bearing walls are.

Linear algebra gives AI its internal language. Words, images, sounds, and biological signals must all be transformed into numbers before machines can manipulate them. A word becomes a vector. An image becomes a grid of values. A patient record becomes a pattern in a high-dimensional space.

Beneath the apparent softness of language lies a stern kingdom of matrices.

Calculus supplies the machinery of learning. A model improves because it can measure error and adjust itself. It asks, again and again, a small but consequential question:

If this parameter changes slightly, does the prediction become better or worse?

Derivatives guide that movement. Gradient descent, one of the great workhorses of machine learning, is not a slogan from the AI boom. It is calculus doing its patient underground labor.

Probability and statistics allow machines to live in a world of uncertainty. No model knows everything. It estimates. It ranks possibilities. It weighs evidence.

The next word in a sentence, the diagnosis from a scan, the risk of a loan default, the likely shape of a protein: all are questions asked in the language of probability.

Then comes classical machine learning, the older but still sturdy part of the structure. Regression, decision trees, clustering, support-vector machines, and random forests may no longer enjoy the same glamour as deep learning, but they remain essential.

They taught computers to learn patterns from data rather than merely follow explicit instructions. They were the workshops in which many of AI’s habits were formed.

Neural networks built upward from there. By stacking layers of artificial neurons, they allowed machines to learn representations of increasing complexity. Edges became shapes. Shapes became objects. Words became meanings. Sentences became patterns of intention.

With enough data, computation, and clever architecture, these networks produced the systems now changing how people write, work, and think.

The toolbox is not the basement

Where, then, does Python fit?

Python is indispensable in modern AI. It is practical, readable, and surrounded by powerful libraries. NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow have made it the favored language of researchers, engineers, and students.

A person can build useful AI systems with Python in a way that would once have required far more effort.

But Python is a tool, not the foundation.

Confusing Python with AI is like confusing a toolbox with architecture. A builder may own the finest saws, drills, and measuring devices in town. That does not mean the builder understands soil, weight, stress, drainage, or design.

Likewise, a programmer may know how to call an API or import a library without understanding why a model learns, why it fails, or why it hallucinates with such confidence.

This distinction matters because tools change quickly. Foundations change slowly.

Today’s fashionable framework may be tomorrow’s museum exhibit. But vectors, derivatives, probabilities, models, and representations will remain part of the bedrock.

The language house

The Large Language Model is the most famous house in the AI neighborhood.

Its architecture is especially suited to language. It is trained on vast amounts of text: books, articles, websites, manuals, discussions, and code. From this ocean of language it learns statistical patterns, semantic associations, and astonishingly useful forms of imitation.

Its success has been so dramatic that it has distorted public understanding. Because LLMs can speak, people attribute to them a kind of generality. Language is, after all, the medium in which humans explain almost everything.

A model that can answer in language appears to know the world.

But language is not the world.

It is a map, a record, a shadow, a gossip network, a museum of human expression. An LLM trained primarily on language is immensely powerful in the language domain. It can assist writers, programmers, lawyers, teachers, analysts, and students. It can compress knowledge, generate drafts, transform tone, and explain concepts.

But it is not identical with intelligence itself.

It is one architectural style.

Other houses will rise

The next stage of AI will not be limited to language. Other houses will be built, each with its own materials, architecture, and purpose.

Consider biology.

A future AI for biological discovery will not be built merely by feeding a language model more essays about cells. It will need biological data: protein structures, gene expression, hormone interactions, cell images, molecular pathways, clinical measurements, and experimental results.

Its task will not be to produce elegant paragraphs. It may need to predict protein folding, design drugs, model disease progression, or simulate cellular behavior.

Such a system may borrow ideas from language models. It may even use language as one interface. But its deeper training will have to come from life itself.

Biology is not merely a vocabulary. It is a dynamic, wet, noisy, astonishingly complex system.

Now consider physical AI: robots, autonomous machines, and embodied agents.

These systems must learn from sensors. Cameras, microphones, lidar, radar, pressure sensors, accelerometers, gyroscopes, and temperature readings will form their experience of the world.

A physical AI must understand not only what an object is, but whether it can be lifted, pushed, broken, avoided, or handed gently to a child.

Language models live in sentences. Physical AI must live in gravity.

That requires a different house. It needs rooms for perception, motion, planning, feedback, safety, and control. Its mistakes do not merely produce awkward prose. They may break a glass, damage a machine, or harm a person.

The architecture must therefore match the domain.

Different architecture, same basement

This is the central lesson.

AI is not one house. It is a city of possible houses.

Some houses will be built for language. Some for biology. Some for medicine. Some for engineering, finance, robotics, climate science, or education.

Their architectures will differ because their domains differ. The data will differ. The risks will differ. The evaluation methods will differ.

A model that writes a charming paragraph is not automatically qualified to diagnose cancer, pilot a drone, or design a new enzyme.

Yet the basement remains familiar.

Mathematics turns the world into structure. Statistics deals with uncertainty. Machine learning extracts patterns. Neural networks learn representations. These are the common foundations beneath otherwise different systems.

The mistake of our moment is to confuse the most fashionable building with the entire city.

LLMs deserve admiration. They are among the most impressive inventions of the digital age. But they are not the end of AI. They are a beginning, a demonstration, a spectacular first district in a much larger urban plan.

Why learners should care

For students, engineers, and curious professionals, this distinction is liberating.

One need not worship every new tool. One need not chase every product announcement, every model release, every new interface with the panic of a squirrel in a server room.

The better strategy is to understand the layers.

Learn the tools, certainly. Use Python. Experiment with LLMs. Practice prompting. Build small applications. Curiosity should have dirt under its fingernails.

But do not stop at the surface.

Learn why a model learns. Learn what data does. Learn why overfitting happens. Learn what a vector is. Learn why probability matters. Learn how neural networks adjust their weights. Learn why evaluation is difficult.

And above all, learn why intelligence in language is not the same as intelligence in biology, robotics, medicine, or the physical world.

Those who understand only the house may be impressed by the wallpaper.

Those who understand the basement can judge whether the structure will stand.

The durable view

Final Thought

The AI boom has brought excitement, money, exaggeration, and confusion. That is normal. Every great technological shift attracts both engineers and magicians, both builders and salesmen.

The task is not to reject the excitement, but to look beneath it.

Large Language Models are not a trick. They are real, useful, and important. They will change work, education, software, and communication.

But they should be understood as part of a larger story.

The house of language AI stands because earlier generations poured the basement: mathematicians, statisticians, computer scientists, cognitive scientists, and engineers.

The next houses will require the same discipline. Biology AI will need biological truth. Physical AI will need sensorimotor reality. Medical AI will need clinical rigor. Scientific AI will need experiments, not merely eloquence.

In the end, artificial intelligence is not magic dust sprinkled on data. It is construction. It needs foundations, tools, materials, architecture, and inspection.

It needs builders who know the difference between a basement, a blueprint, and a balcony.

The chatbot is the house we can see today.

The basement beneath it is older, deeper, and more important.

And the city has only begun to rise.

Does AI steal from human artists?

A Conversation With My Son at MIT

For my son, on the occasion of his graduation from NTNU - Friday, 4 June 2026

Tomorrow, my son graduates from NTNU (Norwegian University of Science & Technology) 

On such a day, a father’s mind naturally travels backward and forward at the same time. I think about the child he once was, the young man he has become, and the future he is about to enter.

I also find myself thinking about a conversation we had during my visit to him at MIT (Massachusetts Institute of Technology) in Boston.

We went together to an art gallery. It was one of those quiet father-and-son moments when a simple walk becomes something deeper. We looked at paintings. We talked. Somewhere between the colors on the walls and the thoughts in our minds, the conversation turned to artificial intelligence.

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My son asked a question that many young artists, designers, writers, and musicians are asking today:

Does AI steal from human artists?

It was not a casual question. It was not only a technical question. It came from someone who has an artist’s mind.

My son has always lived with one foot in science and one foot in art. He studies engineering and robotics, but he also paints. Every summer, when he came home on vacation, he often created a painting. He makes origami. He plays the violin whenever and wherever he has time. He has played with student symphony orchestras in both Boston and Trondheim.

So when he looked at AI image tools and said that they could create a painting in minutes, while a human artist might spend days, weeks, or years developing the skill to create one, I understood his concern.

He was not simply defending artists as a profession. He was defending the value of human effort.

The Pain Behind the Question

To someone who has never created art, the question may sound simple. If a tool can make an image faster, why not use it?

But an artist knows that the final image is only the visible surface. Behind a painting there are years of observation, practice, failed attempts, frustration, patience, and small discoveries. Behind a violin performance there are scales, rehearsals, tired fingers, imperfect notes, and the quiet discipline of returning to the instrument again and again.

When AI creates an impressive image in seconds, it can feel as if the machine has skipped the whole human journey. It is like arriving at the mountaintop by helicopter while others have climbed with tired legs and bleeding feet.

That feeling of unfairness should not be dismissed.

Many artists worry that AI systems have learned from human artwork without permission. They worry that their styles may be copied. They worry that clients may choose cheap machine-generated images instead of paying living artists. They worry that society may begin to value output more than effort, speed more than craft, and convenience more than meaning.

These concerns are real. But the question still has another side.

No One Creates From Nothing

Scientific discoveries do not appear from thin air.

Each generation prepares the road for the next generation. Earlier scientists ask questions, build instruments, make mistakes, discover patterns, and leave behind knowledge. Later scientists inherit that knowledge and continue the journey.

Newton did not create physics from emptiness. Einstein did not think in an empty universe. Modern engineers do not begin by inventing mathematics from the beginning. They inherit the work of previous generations.

The same is true in art.

A painter learns by seeing other paintings. A musician learns by listening to other musicians. A writer learns by reading other writers. A violinist learns not only from the notes on the page, but also from teachers, conductors, friends, orchestras, and centuries of musical tradition.

When my son plays the violin in an orchestra, he is not stealing from Mozart, Beethoven, or the musicians who came before him. He is participating in a tradition. He learns, interprets, transforms, and gives the music something of himself.

Human creativity is never born in isolation. Every artist carries an invisible museum inside the mind. Every musician carries an invisible concert hall. Every writer carries a library of voices, memories, and sentences.

We create from what we have seen, heard, loved, questioned, suffered, and remembered.

AI also learns from previous human creations. But it does so at a scale and speed no human life can match. A human artist may study thousands of works in a lifetime. An AI model may be trained on millions of images.

So perhaps the difference is not that humans learn from the past while AI does not.

Both do. The difference is speed, scale, permission, and lived experience.

The Camera Once Entered the Gallery

When photography appeared, many painters feared that painting would lose its purpose. If a camera could capture a realistic image in seconds, why would anyone still need a painter?

At first, the fear made sense. For centuries, one important role of painting had been to preserve the appearance of people, places, and events. A camera could do that with mechanical speed and accuracy.

But photography did not kill painting. Instead, painting changed.

Artists began to explore what the camera could not easily capture: emotion, dreams, movement, abstraction, memory, inner life, and personal interpretation. Photography itself also became an art form. The new tool did not end creativity. It moved creativity into new territories.

AI may be another camera entering the gallery.

At first, it feels threatening because it can produce images quickly. But speed alone does not decide the value of art. A camera can take a photograph instantly, but not every photograph is meaningful. A piano can produce sound immediately, but not every sound is music.

The tool can help produce. It cannot decide why something should exist.

What AI Can Do

AI can generate images based on patterns it has learned from human-created data. It can combine styles, imitate visual forms, and produce surprising results.

That is powerful. It is also unsettling.

AI can create a picture of a violinist. It can create a picture of an orchestra. It can create a picture of a young engineer standing beside a drone, or an artist painting in the summer light.

But it has never practiced the violin when it was tired. It has never folded paper carefully into origami. It has never stood nervously before a concert. It has never spent a summer afternoon painting because something inside wanted to become visible.

AI can generate an image of a refugee boat on the sea. But it has never been on that boat.

This matters.

Human art is not only the object produced. It is also the life behind the object. It is the memory, the wound, the joy, the patience, the love, and the purpose.

AI has breadth. It can absorb patterns from more works than any person could study in a lifetime.

Humans have depth. We have lives.

So, Is AI Stealing?

My answer is not a simple yes or no.

If an AI system copies the style of a living artist too closely, allowing others to profit from imitation without permission, credit, or compensation, then there is a real ethical problem. If companies train AI systems on artists’ work without transparency or respect, then society has a serious issue to solve.

Artists deserve protection. Their labor has value. Their names, styles, and livelihoods should not be treated as free material for everyone else’s profit.

But if we say that AI is stealing simply because it learns from previous human creations, then the question becomes more complicated.

Human culture itself is built from learning, borrowing, transforming, and responding. Every generation receives something from the previous generation and adds something new.

The real problem is not that AI learns. The real problem is how it learns, who benefits, who is credited, and who may be harmed.

Therefore, the better question may not be:

Does AI learn from human artists? Of course it does.

The better question is: How can AI learn from human culture in a way that is fair, transparent, and respectful?

The Future Artist

I do not believe AI will end human creativity. But I do believe it will change the role of the artist.

The future artist may use brushes, cameras, tablets, code, and AI tools. A digital creator may use AI not as a replacement for imagination, but as a new instrument. The creative act may shift from making every stroke by hand to directing, selecting, refining, combining, and giving meaning.

This does not make the artist less important. In some ways, it makes human judgment more important. When images become easy to generate, taste becomes more valuable. When production becomes faster, intention becomes more important. When tools become powerful, responsibility becomes essential.

When photography was invented, some people feared the camera would replace artists. Yet history showed something different. Cameras became instruments in the hands of artists. The most celebrated photographs were not created by cameras alone. They were created by human beings with vision, patience, and imagination.

I suspect AI may follow a similar path. The technology will become increasingly powerful, but the most meaningful works will still come from people who bring curiosity, judgment, experience, and purpose to the process.

The future masterpiece may be created with AI, just as a photograph is created with a camera. But in both cases, the true artist remains human.

Fathers, Sons, and New Tools

When I was my son's age, I did not have AI.

Later, when I wrote my master’s thesis, I had to fight many battles at the same time. I had to study in Norwegian, read research papers, conduct research, learn academic writing, and improve my English. There was no AI assistant available day and night to explain difficult concepts, summarize papers, or help polish sentences.

My generation learned computers when computers were still strange to many people. My son’s generation must now learn AI.

Every generation meets a new tool that feels powerful, strange, and sometimes threatening. The printing press changed knowledge. The camera changed art. The computer changed work. The internet changed communication. AI is now changing creativity, learning, research, and many parts of daily life.

The wrong answer is blind worship of technology. The wrong answer is also blind fear. The better answer is the path humanity has followed for generations: to learn, adapt, and use new tools wisely, transforming them from sources of uncertainty into instruments of opportunity.

Learn the tool. Question the it. Use it carefully. Improve it if possible. But do not stand outside the future only because the future arrives wearing unfamiliar clothes.

For my youngest son

As I think back to that conversation in Boston, I do not want to dismiss your concern.

Your concern came from love for art. It came from respect for the human effort behind creation. It came from the part of you that paints, folds paper, plays violin, listens, experiments, and tries to make something beautiful with your own hands.

That part of you should never disappear. But I also hope you does not become afraid of AI. You belongs to a generation that will need to understand it, guide it, challenge it, and use it responsibly.

Perhaps one day you will use AI in your research on autonomous marine drones. Perhaps one day you will use AI in your art. Perhaps you will also help shape the ethical rules for how such tools should be used.

But whatever tools you uses, the curiosity behind the research and the soul behind the art will still be yours. 

The machine may generate an image but only human gives it meaning.

Conclusion

AI does not create from nothing. It is built on the accumulated experience of humanity. But humans do not create from nothing either. We learn from parents, teachers, books, music, paintings, science, history, and one another.

The difference is that humans transform what we learn through lived experience. We attach memory, emotion, purpose, and responsibility to creation.

So perhaps AI is not the end of art.

Perhaps it is another mirror, another instrument, another camera entering the gallery. It will challenge artists. It will disturb old assumptions. It will create ethical problems that must be solved.

But it may also open new doors.

The important task for the younger generation is not to run away from AI in fear, nor to use it carelessly. The task is to learn it, question it, guide it, and use it with human judgment.

Tomorrow, my son graduates.

He steps into a world where art and technology will meet more often, not less. I hope he carries both courage and caution with him. I hope he remembers that tools can become dangerous when humans stop thinking, but powerful when humans use them with wisdom.

The future will not belong to AI alone. It will belong to humans who know how to remain human while using it.

The New Role of the Software Engineer in the Age of AI

Preface

This essay is a reflection on my professional journey as a software engineer. Over the years, I have worked with many technologies, projects, colleagues, and customers. Some experiences were successful, others taught valuable lessons.

I originally wrote these notes as a way to preserve ideas and insights that I may wish to explore further in future writing.

I dedicate this essay to Martini on the occasion of his graduation from NTNU in June 2026. As he begins the next stage of his own journey, I hope that some of these experiences may be useful to him and perhaps to future generations of our family.

Papi

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For decades, software engineering was closely associated with writing code. A software engineer was often imagined as someone sitting in front of a computer, typing thousands of lines of instructions in C, C++, Java, Python, or another programming language.

Today, that picture is beginning to change.

Artificial Intelligence can now generate functions, classes, database schemas, API endpoints, documentation, and even unit tests. Some observers have concluded that software engineers may soon become obsolete.

I believe the reality is quite different.

AI is changing software engineering, but it is not eliminating the need for software engineers. Instead, it is shifting the center of gravity of the profession from coding toward architecture, design, testing, validation, and human judgment.

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AI Can Write Code, But It Still Needs a Blueprint

In many ways, software development resembles the construction of a house.

Before construction begins, someone must determine why the building is needed. Someone must understand the requirements, create the blueprint, and ensure that the structure will be safe, useful, and adaptable.

The construction workers follow the blueprint.

If the blueprint is flawed, even the most skilled builders will produce a flawed building.

AI is becoming an increasingly capable builder of software. It can generate code faster than most humans and can automate many repetitive programming tasks.

But AI still requires direction.

The software engineer increasingly becomes the architect.

Software Engineering Was Never Just About Coding

Throughout my career, I worked in several different industries.

My master's thesis focused on image processing. My first professional position involved seismic data processing at Schlumberger, where our team participated in developing the Geoframe platform. Later, I worked in Silicon Valley with enterprise middleware technologies and eventually joined Kongsberg Defence & Aerospace, where I worked on airborne surveillance and defense systems.

Although the technologies were different, the underlying challenges were remarkably similar.

The difficult problems were rarely about syntax.

The real challenges involved questions such as:

• What problem are we solving?
• How should the system be structured?
• How should different components communicate?
• How can new functionality be added later?
• How do we integrate legacy systems with new systems?
• How do we ensure reliability and maintainability?

These are architectural questions.

They remain architectural questions in the age of AI.

From Programmer to System Architect

As AI becomes increasingly capable of generating code, the value of architecture becomes more visible.

Architecture determines how a system is organized. It defines boundaries, interfaces, communication patterns, data flows, and responsibilities.

A well-designed architecture allows a system to evolve gracefully as requirements change. A poor architecture creates technical debt that accumulates year after year.

Many experienced software engineers discover that as their careers progress, they spend less time writing code and more time designing systems.

This trend is likely to accelerate in the AI era.

The future software engineer may spend more time defining requirements, designing architectures, reviewing AI-generated code, and validating results than manually writing every line of implementation.

The Lego System Philosophy

One of my proudest engineering achievements involved designing an object-oriented framework that integrated multiple sensors and both new and legacy systems.

The architecture was first modeled using UML before implementation began.

The objective was to create a flexible framework where components could be added, removed, or replaced with minimal impact on the rest of the system.

I often think of this approach as a Lego system.

Each component behaves like a Lego block. It has a well-defined interface and a specific responsibility. The internal implementation can change, but the connection to the rest of the system remains stable.

This approach allows systems written in different programming languages and developed by different teams to work together.

The goal is not merely to solve today's problem.

The goal is to build a system that can adapt to tomorrow's problems as well.

Design Patterns: Reusable Engineering Wisdom

Software architects have long relied on design patterns to solve recurring problems.

Design patterns are similar to architectural patterns in building design. Architects do not reinvent doors, windows, staircases, or roofs every time they design a house. Instead, they reuse proven solutions.

Software engineers do the same.

Patterns such as Adapter, Factory, Observer, Strategy, and Facade represent accumulated engineering knowledge gained through decades of experience.

Programming languages change. Technologies evolve. Frameworks come and go.

Yet many design patterns remain relevant because they solve fundamental organizational problems.

AI may generate code that implements these patterns, but engineers still need to understand when and why each pattern should be used.

Testing Becomes More Important, Not Less

A common misconception is that AI will reduce the need for testing.

In reality, the opposite may occur.

When AI can generate large amounts of code rapidly, the bottleneck shifts from implementation to validation.

The engineer must still determine:

• Does the code satisfy the requirements?
• Does it handle edge cases correctly?
• Is it secure?
• Is it maintainable?
• Does it integrate properly with the rest of the system?
• Does it fail safely under unexpected conditions?

AI-generated code may look convincing, but appearance is not the same as correctness.

Testing, verification, and validation become increasingly valuable skills.

The future engineer may spend less time typing code and more time evaluating whether the generated code is trustworthy.

Prompting Becomes a Core Engineering Skill

One of the most important new skills for software engineers may be AI prompting.

A prompt is not merely a question. In many cases, it resembles a miniature software specification.

A weak prompt might say:

Build a customer management system.

A stronger prompt might say:

Design a customer management module that supports customer profiles, order history, validation, audit logging, and future integration with external payment systems. Explain the architecture before generating code.

The difference is clarity.

Good prompting requires the same discipline that good software engineering has always required:

• Define the objective clearly.
• Provide relevant context.
• Specify constraints.
• Describe the expected output.
• Review and refine the result.

Prompting is becoming a communication skill between the engineer and an intelligent coding assistant.

From Waterfall to Continuous Evolution

Traditional engineering often viewed projects as having a clear beginning and end.

Software is different.

A software system is rarely finished.

Requirements change. Markets change. Regulations change. Technologies change. User expectations change.

Modern software systems evolve continuously.

This reality led to the rise of Agile development methodologies, which recognize that change is not an exception but a normal part of software development.

AI systems themselves evolve in a similar way.

Software engineers increasingly work in environments where both the software and the tools used to build the software are continuously changing.

The ability to learn, adapt, and iterate becomes more important than mastering a particular programming language or framework.

The Human Role Remains Essential

Despite the remarkable progress of AI, software engineering remains fundamentally a human activity.

Someone must understand the business problem.

Someone must communicate with stakeholders.

Someone must evaluate trade-offs.

Someone must make architectural decisions.

Someone must take responsibility for the final result.

AI can assist with implementation.

AI can accelerate development.

AI can generate code.

But AI does not own the responsibility for the system.

The engineer does.

Final Thoughts

The future of software engineering is not a story of humans versus AI.

It is a story of collaboration between human judgment and machine capability.

The engineer who focuses solely on coding may feel threatened by AI.

The engineer who understands architecture, system design, testing, communication, and problem-solving will likely become even more valuable.

AI can build parts of the house faster than ever before.

But someone still needs to design the blueprint, inspect the structure, verify the safety, and decide whether the building truly serves its purpose.

That responsibility remains with the software engineer.

In the age of AI, the software engineer evolves from being primarily a coder into becoming an architect, reviewer, tester, communicator, and trusted decision-maker.

The tools will change.

The technology will advance.

But the ability to transform human intent into reliable systems will remain one of the most valuable skills in the profession.

How to Learn a New Language and What Benefits It Brings

A simple, natural approach inspired by babies, music, and lifelong learning

Learning a new language is often seen as memorizing vocabulary, studying grammar, and passing exams. But the most natural way to learn a language is much older and simpler. It is the way every baby learns a mother tongue: first by listening, then by imitating, then by repeating, and only later by reading, writing, and studying grammar.

Learning a new language opens not only the mouth, but also the ear, the mind, and the heart.

A baby does not begin with grammar rules. A baby hears the voices of parents, grandparents, brothers, sisters, neighbors, and the world around them. Slowly, sounds become familiar. Words begin to carry meaning. Sentences become patterns. The child repeats imperfectly, receives gentle correction, and tries again. Over time, the language grows inside the child like a small tree finding sunlight.

This natural method is also close to the Suzuki method in music education. Shinichi Suzuki believed that children could learn music in a similar way to how they learn their mother tongue: through listening, imitation, repetition, encouragement, and a nurturing environment.¹ In the Suzuki approach, children often learn to play music by ear before they learn to read musical notes. The ear comes first. Symbols come later.

This is a powerful lesson for language learning. Before we ask a beginner to analyze grammar, we should let the beginner hear the music of the language. The rhythm, melody, pronunciation, and common phrases should enter the ear first. Grammar is useful, but it becomes much easier when the learner has already heard many examples. Grammar should be a lamp, not a prison.

One of the best ways to begin is to listen every day. Listen to songs, short stories, simple videos, conversations, or children’s programs in the new language. At first, you may understand very little. That is normal. The goal is not to understand everything immediately. The goal is to let the sounds become familiar. The ear must be trained before the mouth can speak freely.

The second step is imitation. Repeat short phrases aloud. Do not begin with long sentences. Begin with small chunks: “Good morning,” “I am here,” “I love you,” “Where are you going?” In Italian, for example, a beginner can start with a song phrase such as Resta qui, amore mio, meaning “Stay here, my love.” This small phrase teaches a verb, an adverb, a noun, a possessive word, pronunciation, rhythm, and emotion all at once.

The third step is correction. Correction does not need to be harsh. A good teacher, parent, or language partner can simply repeat the correct version. A child says something imperfectly, and the adult answers naturally with the better form. This kind of correction is gentle, immediate, and easy to remember. It is not a red pen. It is a guiding hand.

The fourth step is repetition. Repetition is not boring when the material is beautiful. This is why songs are so useful. When we love a song, we want to hear it again and again. Each repetition strengthens memory. The melody carries the words. The rhythm carries the pronunciation. The emotion makes the phrase unforgettable.

Songs are especially helpful because they combine language with music. Many people can sing a foreign song with surprisingly good pronunciation, even if they cannot yet hold a conversation in that language. The song gives them a structure. It supports the tones, stress, rhythm, and flow of speech. Music becomes a bridge into language.

Learning through songs should not remain passive, however. The learner should read the lyrics, understand the meaning, learn the vocabulary, notice the grammar, and then reuse the phrases in daily speech. In this way, a song becomes more than entertainment. It becomes a living classroom.

The fifth step is speaking without fear. Many adults are afraid to make mistakes. But mistakes are not enemies. They are footprints on the path. Babies make thousands of mistakes before they speak fluently. Musicians play wrong notes before they play beautifully. Language learners must also accept the beginner’s stage with patience.

The sixth step is to add grammar gradually. After listening and speaking for a while, grammar becomes much more meaningful. The learner begins to recognize patterns: how verbs change, how nouns have gender, how adjectives agree, how questions are formed. Grammar then explains what the ear has already heard. It becomes useful because it is connected to real language.

Reading and writing should also come gradually. They are important, but they should not replace listening and speaking. A balanced method includes all four skills: listening, speaking, reading, and writing. But for a beginner, listening and speaking should be the roots. Reading and writing can grow as branches.

Now we can ask: what are the benefits of learning a new language?

The first benefit is communication. A new language allows us to speak with more people, not only through translated words but through their own cultural voice. When we speak someone’s language, even imperfectly, we show respect. We say, “Your world matters enough for me to enter it.” This can turn strangers into friends and travelers into welcomed guests.

The second benefit is cultural understanding. Every language carries history, humor, memory, food, music, family life, and ways of seeing the world. When we learn another language, we also learn another way of being human. The British Council has noted that language learning helps people understand different cultures and places, and many students see languages as useful for future careers.²

The third benefit is brain training. Learning a language exercises memory, attention, listening, pattern recognition, and problem solving. Research has often connected bilingualism and language learning with cognitive benefits, especially in attention and mental flexibility.³ The brain must switch between sounds, meanings, grammar structures, and cultural contexts. It becomes more flexible, like a hand trained by many instruments.

The fourth benefit is better learning habits. Language learning teaches patience. You cannot master a language in one week. You must return every day, listen again, repeat again, fail again, and improve again. This builds discipline. It teaches the quiet truth that progress often comes in small steps, not sudden miracles.

The fifth benefit is confidence. The first time you understand a sentence in a new language, something small but beautiful happens inside. The world becomes larger. The first time you order food, greet a neighbor, understand a song, or have a short conversation, you feel a new confidence. You realize that the mind can still grow.

The sixth benefit is opportunity. In work, travel, study, and friendship, language skills open doors. In a global world, people who can communicate across languages and cultures can build better relationships, solve problems more easily, and understand situations more deeply. Language is not only a school subject. It is a bridge.

The seventh benefit is empathy. When we learn a new language, we become beginners again. We speak slowly. We make mistakes. We depend on the patience of others. This experience can make us more humble and more compassionate toward immigrants, children, older learners, and anyone struggling to express themselves.

There is also a special connection between music and language. Studies have suggested that musical training may support skills such as verbal memory, pronunciation, reading, and executive functions.⁴ This makes sense. Music trains listening, timing, memory, movement, attention, and discipline. These are also important in language learning. A child learning violin is not only training the fingers. The child is training the whole brain to listen, remember, coordinate, and persist.

For this reason, a good language-learning method should feel partly like music practice. Listen first. Imitate carefully. Repeat often. Accept correction. Practice daily. Play with others. Enjoy the sound. Then, slowly, learn the written system and the rules behind it.

In the end, learning a new language is not only about becoming bilingual or multilingual. It is about becoming more open. It teaches us that our own language is not the only window through which the world can be seen. Each language adds another window, another melody, another path through the garden of human experience.

Final thought: In the spirit of Taoist wisdom, language learning is a practice of balance. We listen before we speak. We receive before we answer. We accept being small before we grow strong. A new language should not be used to show superiority, but to build understanding. The wise learner walks gently between cultures, carrying curiosity in one hand and respect in the other. In every new word, there is a chance to become more patient, more responsible, and more fully human.

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Footnotes

1. The Suzuki Method is based on the “Mother Tongue Method,” where children learn music through listening, imitation, repetition, encouragement, and a nurturing environment. Source: International Suzuki Association, “The Suzuki Method.” https://internationalsuzuki.org/method ↩

2. The British Council has reported that many pupils see speaking other languages as important for understanding different cultures and useful for future careers. Source: British Council press release, 2021. https://www.britishcouncil.org/about/press/speaking-other-languages-important-understanding-different-cultures-and-places-say ↩

3. Research on bilingualism has discussed possible cognitive benefits connected to attention, mental flexibility, and cognitive reserve. Source: Bialystok, E. “Bilingualism and the aging brain,” Language and Linguistics Compass, 2017. https://compass.onlinelibrary.wiley.com/doi/10.1111/lnc3.12213 ↩

4. A review on music training and child development reported links between musical training and skills such as verbal memory, pronunciation accuracy, reading ability, and executive functions. Source: Miendlarzewska & Trost, “How musical training affects cognitive development,” Frontiers in Neuroscience, 2014. https://pmc.ncbi.nlm.nih.gov/articles/PMC3957486/ ↩

America, China, Taiwan, and Southeast Asia: When the Old World Order Begins to Tremble

English Tiếng Việt

This essay explores the evolving geopolitical relationship between the United States and China through the lens of history, Taiwan’s strategic importance, the rise of the MAGA movement, and the balancing act of Southeast Asia in the emerging Indo-Pacific order.

Power Shifts, a New World Order, and the Future of the Indo-Pacific

1. The Echo of 1972

When President Richard Nixon traveled to China in 1972, the world witnessed one of the most important geopolitical pivots of the twentieth century. The Shanghai Communiqué fundamentally changed the strategic architecture of the Cold War.^[1]

From Washington’s perspective, the opening to Beijing was a brilliant strategic move designed to counterbalance the Soviet Union. Yet from the perspective of smaller allies such as South Vietnam and Taiwan, the shift carried a deeper and more painful message: when the interests of great powers change, smaller allies may suddenly find themselves standing alone.

That memory still lingers quietly in Asia today. Many people who lived through the fall of Saigon or the diplomatic isolation of Taiwan continue to observe every U.S.–China summit with a sense of caution. They do not simply listen to official speeches. They watch for subtle signs of changing priorities beneath the surface.

2. Taiwan and the Strategy of Ambiguity

After Washington officially switched diplomatic recognition from Taipei to Beijing in 1979, the United States Congress passed the Taiwan Relations Act.^[2] The law preserved unofficial relations with Taiwan and committed the United States to providing defensive arms to the island.

Since then, American policy toward Taiwan has relied on what is known as “strategic ambiguity.” Washington deliberately avoids making absolute promises about military intervention while also refusing to rule it out. The ambiguity itself becomes a strategic tool, forcing Beijing to remain uncertain about how America would respond during a crisis.

For decades, this delicate formula helped preserve relative peace across the Taiwan Strait. But as rivalry between China and the United States intensifies, maintaining ambiguity becomes increasingly difficult.

3. Taiwan: The Unsinkable Aircraft Carrier

Taiwan is not merely an island. Strategically, it sits at the center of the First Island Chain stretching from Japan through the Philippines. Military planners have long described Taiwan as an “unsinkable aircraft carrier” positioned directly at China’s maritime gateway into the Pacific Ocean.

If Beijing were to gain full control over Taiwan, the balance of naval power in East Asia would shift dramatically. Japan, South Korea, and Southeast Asia would all feel the consequences.

But Taiwan’s importance is no longer purely military. The island has become one of the central nodes of the global semiconductor industry. Companies such as TSMC manufacture many of the advanced chips used in artificial intelligence systems, smartphones, data centers, military technologies, and electric vehicles.

Many analysts now describe Taiwan’s semiconductor dominance as a form of geopolitical insurance often called the “Silicon Shield.” The idea is simple but powerful: because Taiwan produces a critical share of the world’s advanced semiconductors, especially through TSMC, the global economy — including the United States and China themselves — has a strong interest in preventing a catastrophic conflict over the island.^[4] Taiwan’s chip industry has therefore become more than an economic success story. It functions as a strategic deterrent, binding Taiwan’s security to the stability of global supply chains. Ironically, the tiny silicon chip may now play a role once occupied by battleships and aircraft carriers in traditional geopolitics.

A major conflict around Taiwan would therefore not only trigger military instability. It could also disrupt the digital nervous system of the global economy.

4. Trump, MAGA, and America First

The rise of Donald Trump introduced a very different tone into American foreign policy. Traditional U.S. leadership after World War II was built upon alliances, long-term commitments, and maintaining a global order. Trump’s worldview, however, often appears more transactional and business-oriented.

When Trump remarked that Taiwan lies thousands of miles away from America but only a short distance from China,^[3] many observers in Asia became uneasy. The statement reflected a broader question emerging inside American society:

How much longer does America want to bear the cost of global leadership?

The MAGA movement emerged partly from war fatigue, economic frustration, industrial decline, and growing skepticism toward endless overseas commitments. Many Americans increasingly ask why their country should continue carrying the burden of defending distant regions while facing domestic problems at home.

This is perhaps the deepest geopolitical question of the present era. The issue is no longer whether the United States remains powerful. It clearly does. The real question is whether American society still possesses the political will to sustain its role as the central guarantor of the global order.

5. Iran and the Limits of Power

The growing tensions involving Iran have intensified these debates. A prolonged Middle Eastern conflict could become a symbolic climax of the post-1945 American era: a superpower attempting to manage multiple crises simultaneously while facing rising polarization at home.

The United States today must simultaneously confront challenges involving China in the Indo-Pacific, Russia in Europe, instability in the Middle East, and increasing domestic division within American society itself.

Historically, empires rarely collapse overnight. More often, they gradually become exhausted by the immense weight of maintaining global dominance.

6. Southeast Asia’s Delicate Balance

From the perspective of Southeast Asia, the situation appears far more complicated than a simple choice between Washington and Beijing.

Most ASEAN countries trade heavily with China and depend on Chinese investment, manufacturing networks, tourism, and supply chains. At the same time, many Southeast Asian governments quietly prefer a continued American presence in the Indo-Pacific as a strategic counterweight.

Vietnam perhaps illustrates this paradox most clearly. The country imports enormous volumes of machinery and industrial goods from China while simultaneously exporting heavily to the American market.

This creates a delicate balancing act. Southeast Asian nations generally do not want a new Cold War. They seek stability, economic growth, and room to maneuver between competing powers.

For smaller nations, balance often matters more than ideology.

7. The Return of the Pendulum

History often moves like a pendulum. After World War II, the United States emerged as the dominant global power. Yet the very success of American power also created enormous responsibilities, military commitments, and strategic burdens across the world.

China, meanwhile, followed a different path. For decades, Beijing focused quietly on economic growth, technological development, industrial expansion, and long-term strategic patience.

Today, the two forces increasingly collide across the Indo-Pacific region. Taiwan stands at the center of this tension. So does Southeast Asia.

Smaller nations cannot stop the tides of history. But they can attempt to maintain balance, preserve flexibility, and avoid placing their entire future in the promises of any single great power.

Final Reflection

In Taoist thought, every extreme eventually generates its opposite. A rising force carries within itself the seeds of exhaustion, while a patient and restrained force quietly accumulates strength beneath the surface.

The Indo-Pacific today reflects this ancient rhythm. America remains enormously powerful, yet increasingly divided and burdened. China continues to rise, yet also faces its own internal economic and demographic pressures.

Between them stand Taiwan and the nations of Southeast Asia, navigating carefully between dependence and autonomy, prosperity and security, memory and survival.

History never truly repeats itself. Yet its echoes continue to travel across generations like distant thunder over the Pacific Ocean.

References

1. Shanghai Communiqué (1972), Columbia University Asia for Educators.
   https://afe.easia.columbia.edu/ps/china/shanghai_communique.pdf
2. Taiwan Relations Act, American Institute in Taiwan.
   https://www.ait.org.tw/policy-history/taiwan-relations-act/
3. Bloomberg Interview with Donald Trump (2024).
   https://www.bloomberg.com/features/2024-trump-interview-transcript/
4. Richard Cronin, “Semiconductors and Taiwan’s Silicon Shield,” Stimson Center, August 16, 2022.
   https://www.stimson.org/2022/semiconductors-and-taiwans-silicon-shield/

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“In balance lies wisdom, and in stillness — clarity.”

Written by David H. Huynh

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