When AI Rebuilds a Feature, What Has It Actually Copied?

Key takeaways

• Binary reverse engineering examines executable files and runtime behavior to understand how software works.
• Reproducing a feature does not mean recovering the original source code.
• Interoperability could help users move their data out of unsupported applications.
• Matching a few example outputs establishes compatibility only within the tested scope.
• Evaluating possible copying requires examining both the development process and the finished product.

Your files can outlive the software that created them. If AI can analyze an old executable and help rebuild the functionality you need, years of inaccessible work could become usable again. That possibility comes with an awkward question: where does understanding someone else’s software become copying their product?

Reading an executable is not recovering the recipe

Developers write source code. Users typically receive files containing the instructions and data needed to run the program. Those files do not necessarily preserve the comments or design decisions that explain why the developers built it that way.

Binary reverse engineering works backward from those files and the program’s behavior. It is a bit like tasting a restaurant dish and trying to work out how it was made. Getting the flavor right does not mean you found the chef’s recipe.

The same distinction applies when an AI agent does the analysis. Explaining a file’s structure, reproducing one feature, and replacing an entire application are different achievements.

A claim that an agent “successfully analyzed” a program tells you very little until someone specifies which of those things it actually did.

The compelling use case is getting your data back

Imagine a design application you used for years. Support has ended, and the program no longer runs properly on your new computer. You still have every project file. Technically, your work is right there. Practically, it might as well be in a locked filing cabinet.

Understanding the file format could make it possible to build a converter that opens those projects elsewhere. That task may require only a small slice of the original application’s functionality.

This is the value of interoperability: allowing different programs to exchange data and work together.

If AI can help with that analysis, users could gain a practical way to leave an application without abandoning their work. Building new software is useful. Keeping accumulated work accessible deserves some attention, too.

A convincing demo has a limited warranty

When someone says they have reproduced a feature, the useful follow-up is simple: for which inputs?

A converter might open five sample files perfectly. That tells us little about older format versions, damaged files, or unusual combinations of features.

Even matching what appears on screen can hide problems. A number might lose decimal precision when saved. A date might survive the conversion while changing meaning.

The key question is the scope of validation. Which ordinary inputs work? Which exceptions have been tested? Does the implementation preserve the information users actually need?

Compatibility and copying also require different evidence. A new implementation that reads the same file format raises questions about how that functionality was built. An implementation containing the original product’s code, images, or documentation raises additional questions about the material it incorporates.

Similar output alone cannot tell you what was taken from the original.

Purpose matters. So do the process and the product.

“For compatibility” is a statement of intent, not a complete explanation. Equally, overlapping functionality does not by itself establish unauthorized copying.

Start with the purpose and scope. Was the goal to extract a user’s data, connect two tools, or replace the original application broadly? That helps explain how much analysis the task required.

Then examine the process. What materials were accessed? What behavior was observed? How did those observations inform the new implementation? Involving an AI agent does not remove the need to account for those steps.

Finally, inspect the result. Which behaviors enable the intended connection between programs? Does the output also include code or visual assets from the original? Can the developer explain which parts were newly implemented and which came from elsewhere?

These questions provide a starting point for assessing the work. They do not, on their own, settle whether a particular implementation is legally permitted.

Better AI analysis could give users more ways to keep old software useful and their data accessible. Making that promise credible requires a clear account of how the replacement was built and where its contents came from. If your essential application stopped working tomorrow, how much of it should you be able to examine to get your own work back?