AI Is a Tool. Creativity Is Still Ours.

A few months ago, I found myself sitting in front of a spinning piece of clay.

There were around twelve of us in the pottery class. We had the same teacher, the same instructions, and roughly the same amount of clay. We were all trying to accomplish the same thing: make a plate.

You might expect twelve similar plates.

That isn’t what happened.

Some were wide and flat. Others were deeper, smaller, or slightly uneven. Mine had its own shape too.

We had started with the same material and followed the same instructions, so why were the results so different?

Because the tool and the material were only part of the process.

Each of us used a different amount of water. We applied different pressure with our hands. We pushed the pedal with different intensity and frequency. We reacted differently when the clay started moving in a direction we didn’t expect.

The final object wasn’t created by the clay or the pottery wheel alone.

It was shaped by the person using them.

And lately, I’ve been thinking about how much this resembles what is happening with AI.

We Have Been Here Before

We are living through an important technological transition, and it is natural that it creates uncertainty.

As someone born in the 90s, I feel my generation has had an interesting seat for this journey.

We watched technology transform around us.

We went from computers being something you occasionally used to carrying one in your pocket every day. We saw the internet move from a place we visited to infrastructure behind much of our lives. And as software professionals, we watched abstractions, frameworks, cloud platforms, open-source libraries, and automation continuously change how we build things.

Now we have AI.

And once again, we are asking:

What happens to the skills we spent years developing?

I don’t think those skills become less important.

I think the way we use them changes.

From Wood to Iron

Human beings have always created tools to extend what we are capable of doing.

We worked with wood. Then we learned to work with iron.

Iron didn’t make craftsmanship disappear. It expanded what craftspeople could build.

I see a similar transition happening in software development.

Writing everything manually can sometimes feel like working with wood. AI can feel like suddenly being handed iron.

The new material may allow us to build faster and attempt things that were previously difficult or expensive.

But having iron doesn’t automatically make someone a great builder.

You still need to understand what you are building.

You need to know when to use the tool, when not to use it, and what happens when something goes wrong.

Most importantly, you still need creativity.

The Same Tool Doesn’t Produce the Same Result

Give two developers the same AI model and the same problem, and I don’t believe you will necessarily get the same solution.

Why?

Because neither developer arrives empty-handed.

We bring years of experiences, mistakes, technical knowledge, curiosity, preferences, and ways of thinking.

One developer may recognize an architectural problem that another doesn’t. Someone with networking experience might approach a problem differently from someone specialized in frontend development. Someone who understands the business deeply may realize that the technically impressive solution isn’t actually the right solution.

AI doesn’t erase those differences.

It can amplify them.

Just like in that pottery class, we may have the same clay and the same wheel, but our hands are different.

And speed alone doesn’t determine whether the plate is good.

It still needs shape.

It still needs our hands.

It still needs our decisions and judgment.

AI as a Software Engineering Tool

One of the biggest advantages I have found using AI as a software engineer is not simply that I can do things faster.

It is that I can explore beyond some of my own limitations.

Recently, I wanted to help a friend solve a problem he was experiencing in his daily work.

I understood part of the problem well.

I knew how a LAN worked. I knew software could potentially solve the issue while costing him less than the solution he would otherwise need to pay for over the coming months. And I knew how to think about the product based on his business, environment, and actual needs.

But there was a problem.

I didn’t know enough about the hardware.

In the past, that knowledge gap could have significantly slowed the project down. I would have needed to spend considerable time discovering the available hardware, understanding the tradeoffs, comparing approaches, and figuring out where to begin.

Instead, I used AI to help me investigate.

Not to make the decision for me, but to help me explore the territory.

I could describe the environment, constraints, network, problem, and ideas I already had. AI helped me identify possible hardware approaches and gave me directions I could investigate further.

Then something interesting happened.

The project became bigger than the original problem.

Once I could connect my software and networking knowledge with hardware possibilities that I hadn’t previously considered, I started seeing opportunities beyond the products already available in his industry.

We weren’t simply finding a cheaper version of an existing solution anymore.

We were designing something specifically for his problem.

And we are still improving it.

That experience changed how I think about AI.

AI didn’t replace the knowledge I already had.

My existing knowledge was what made the AI useful.

Without understanding networks, software architecture, product design, and my friend’s business needs, I wouldn’t necessarily know which suggestions made sense, which questions to ask, or which ideas were worth pursuing.

The tool expanded the territory I could explore.

But I still had to decide where to go.

The Puzzle We Don’t Have to Start Again

I sometimes think about technology as a giant puzzle that humanity has been working on for generations.

Someone discovers a few pieces.

Someone else finds the edges.

Another person realizes that two sections connect.

Then the next person arrives.

Should they throw everything on the floor and start the puzzle again?

Probably not.

They can look at what has already been discovered, understand why those pieces fit together, and continue from there.

AI gives us access to an enormous number of pieces that other people have already helped discover.

That is incredibly powerful.

But having more pieces in front of us doesn’t tell us what picture we should create.

That part is still ours.

As software engineers, I believe this makes understanding technology more important, not less. We need to understand the systems behind what we build. We need to make responsible decisions about how technology affects people. And we need enough knowledge to recognize when a generated solution is useful, incomplete, inappropriate, or simply wrong.

AI can help us move faster.

It can help us explore areas outside our expertise.

It can help us continue parts of the puzzle that others have already started.

But creativity, judgment, curiosity, responsibility, and the ability to understand a human problem are still ours.

The wheel can spin faster.

The clay can become easier to shape.

But we still have to decide what we want to make.