If AI Writes It, Who Owns It?

The conversation around AI and copyright is everywhere. Did large language models train on copyrighted books? On open-source GitHub repositories? On paywalled news articles?

Those are critical questions, and courts around the world will be answering them for years to come. Lately, though, I’ve found myself thinking about something else. Not about how AI models are trained, but about what happens after they create something entirely new.

Let's Start with a Blog Post

Imagine asking ChatGPT to write a blog post. It nails the tone, the structure, and the message. You fire up your CMS, copy-paste the text, publish it, and move on.

A week later, someone copies your post, word for word.

Can you stop them?

The answer depends on where you live.

In the United States, copyright generally requires human authorship. If a piece of content is generated entirely by AI, it may not qualify for protection at all. In practical terms, that means anyone might be free to copy it without infringing on your IP. A "no" is very likely, then.

In Europe, the picture is complex: courts require a work to be the author’s own intellectual creation, strongly suggesting that meaningful human effort is required, even if the exact boundary line remains blurry. I wouldn't be surprised if the same "no" would apply here as well.

Yet even if the law eventually resolves every copyright nuance, I don't think it changes the ultimate direction we're heading.

Europe is taking a distinct regulatory path through the AI Act, introducing transparency requirements for AI-generated or manipulated content. That made me wonder: will Generated by AI become the nutritional label of the internet? A helpful marker of origin, or a scarlet letter of low effort?

From Scarcity to Abundance

Instead of asking who owns AI-generated content, we could also ask: what happens when content is no longer scarce?

Imagine I ask an LLM to write an opinionated article about the future of software testing. Tomorrow, you ask it to write a provocative piece on the exact same topic.

Our articles won't be identical. They’ll use different words, different examples, and distinct structures. Yet they will likely arrive at many of the same core conclusions.

Nobody copied anyone, yet neither article is particularly unique anymore.

That isn't a copyright problem. It’s an abundance problem.

Software and the Declining Value of the Artifact

For decades, software companies treated source code as the crown jewels: the core IP, the defensible moat, the asset competitors couldn't touch. That made perfect sense when writing software was slow and expensive, while copying it was trivially easy.

AI challenges that equation.

Your competitor no longer needs your implementation. They just need your problem and an LLM.

Copyright protects expression, not recreation.

Suppose I spend three days crafting a custom Laravel middleware. Tomorrow, another developer prompts an LLM: "Build a Laravel middleware that solves this exact problem."

The resulting implementation will almost certainly differ in its methods, abstractions, and architecture. Yet, the functional outcome is identical, without infringing copyright.

The implementation loses economic value not because legal protections disappeared, but because recreation became friction-free.

Experience (Still) Matters

None of this implies that software engineering is becoming trivial. Ask two junior developers and a staff engineer to solve the same problem using AI. All three will likely produce working software.

The difference isn't whether the code runs; it's whether someone spots the hidden N+1 query, the race condition that only triggers under peak load, or the architectural shortcut that quietly turns into crushing technical debt six months later.

AI generates answers. Experience knows which answer to pick.

Where Intellectual Property Actually Lives

I don't think AI makes intellectual property disappear. I think it changes the balance between different kinds of intellectual property.

Source code will still matter. A well-designed architecture, a carefully engineered system, and a reliable codebase are really valuable assets.

But the advantage may increasingly come from everything around the code:

• The product decisions that define what should be built.
• The domain knowledge that explains why it matters.
• The UX choices that make it intuitive.
• The countless small choices that make software reliable, scalable, and pleasant to use.
• The architecture that ties everything together.

AI can generate a solution. It does not automatically know whether it is the right solution.

A developer with deep experience can look at the same output and see things that are invisible to others: a performance bottleneck waiting to happen, a security risk, an abstraction that will hurt the team six months from now.

The strongest software companies won't necessarily be the ones that write the most code.

They'll be the ones that understand the problem the deepest and are able to come up with the best solutions.