What Is Synthetic Data? How AI Learns From Data That Was Never Real

The short version

Synthetic data is information that an algorithm generates rather than information collected from the real world. Instead of recording an actual customer transaction or photographing a real street, a model produces new examples that resemble real ones in their statistical patterns, without copying any specific real record.

The point is not to fake reality for its own sake. It is to give AI systems something to learn from when real data is hard to get: too scarce, too expensive to collect and label, or too sensitive to share. A self-driving system needs examples of rare near-crashes. A medical model needs balanced examples across patient groups. A fraud detector needs more of the rare fraud cases than the world conveniently provides. Synthetic data is one way to fill those gaps.

How it is generated

There is no single method. Some synthetic data is rule-based: you write the logic of a system and simulate it, the way driving simulators produce road scenes or game engines render objects from many angles. Some is statistical: you measure the distributions in a real dataset and sample new rows that follow the same patterns.

The newer approach uses generative models. Generative adversarial networks (GANs) and diffusion models learn the structure of real images, text, or tabular data and then produce fresh examples. This is the same family of techniques behind AI image generation, which is why synthetic data and generative AI are now closely linked in practice.

A useful mental model: synthetic data tries to keep the shape of the real data, the relationships and frequencies, while dropping the specific identities. Done well, the patterns survive and the people behind the original records do not appear at all.

Why teams actually use it

Privacy is the headline reason. Traditional anonymization can sometimes be reverse-engineered to re-identify people. Well-made synthetic data contains no real individuals to begin with, so teams can develop and test models without exposing personal information, and in some cases share datasets more freely for research.

Scale and cost are the second reason. Collecting and hand-labeling real data is slow and expensive. Generative methods can produce large labeled datasets quickly, which speeds up experimentation.

Edge cases are the third. Models often fail on rare situations precisely because rare situations are rare in the training data. Synthetic data lets teams deliberately create more of the hard, unusual examples, and it can help rebalance datasets that under-represent certain groups, which is one tool against some forms of AI bias.

This is also why synthetic data has become central to forecasts about AI. In 2023 Gartner predicted that more than 60 percent of data used to train AI would be synthetic by the end of 2024, a sharp jump from an estimated 1 percent in 2021. Treat the exact figure as a directional forecast rather than a measured fact, but the direction is clear: synthetic data is no longer a niche trick.

The honest risks

Synthetic data is not free of problems, and the honest version of this story matters. The first risk is bias. If the real data used to build a generator was biased, the synthetic data can carry that bias forward, and generating more of it can amplify the problem rather than fix it. Synthetic data can reduce bias when used carefully, but it does not do so automatically.

The second, more subtle risk is model collapse. In a Nature paper published in July 2024, Ilia Shumailov and colleagues showed that when models are trained recursively on their own generated output, generation after generation, quality degrades. Rare patterns in the tails of the distribution fade first, then broader quality erodes. The intuition is a photocopy of a photocopy: each pass loses a little, and the losses compound.

Importantly, later work has pushed back on the most alarming reading. Several studies found that when synthetic data accumulates alongside real human data, rather than replacing it, collapse is largely avoided. The practical lesson is consistent: synthetic data is a supplement to real data, not a replacement for it.

A third caution: privacy is not guaranteed by the label alone. If a generator memorizes and reproduces real records, the output can leak information. Good synthetic data requires deliberate evaluation, not blind trust.

Where this touches ecommerce and product visuals

If you sell online, you already meet generative AI through product imagery. Tools that clean backgrounds, place a product on a clean white background, or generate a scene around it are using models trained on large image datasets, and synthetic or augmented images are part of how such systems learn to handle many products, angles, and lighting conditions.

Renderivo sits in this space. It uses AI to clean and frame product photos for marketplaces, so the connection to synthetic data is real but indirect: the same broad techniques that generate training data also power practical, everyday image tools. The honest framing is that synthetic data is plumbing, useful infrastructure behind better models, not magic.