by Debbie Burgin

One of the biggest questions in AI isn’t how large a model is.

It’s whether we can trust what it produces.

We spend countless hours evaluating models, benchmarking performance, measuring hallucinations, and refining prompts. But I think one question deserves just as much attention.

Can we trace the data that taught the model in the first place?

Imagine a financial analyst presents a report with no citations.

No supporting documents.

No explanation of where the numbers came from.

Would you trust it? Probably not.

Now imagine an AI model produces an answer that influences a medical decision, a legal opinion, or an investment strategy.

Shouldn’t we ask the same question?

Where did this information come from?

Data lineage has always mattered.

In the AI era, I believe it’s becoming essential. The more transparent we are about the origin, quality, and history of training data, the more confidence we can have in the systems built upon it.

That’s one reason historical publications are so valuable.

They’re traceable. They have authors. Publication dates. Editors. Context.

They represent documented knowledge rather than anonymous fragments gathered from unknown sources.

Can AI learn from many different types of data?

Absolutely.

Should it?

Of course.

But when the provenance of that data is unclear, so is our confidence in the knowledge it helps create.

As AI becomes part of more critical decisions, trust won’t come from bigger models alone.

It’ll come from understanding what those models learned…and where that knowledge began.

Because if you can’t trace the data, you absolutely can’t trust the output.