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 a 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.