What is synthetic data, and why do AI companies use it?
Synthetic data is artificially generated data that mimics real data. AI companies use it for scale, privacy and edge cases, but it can copy flaws too.
AI systems need vast amounts of data to learn from. But real data is messy, limited, expensive to collect and often contains information that should not be shared. Synthetic data has become one of the main ways the AI industry fills that gap, and it raises practical questions worth understanding.
The Short Version
Synthetic data is artificially generated data that mimics the structure and statistical properties of real data without being taken directly from real people or events. Here is what that means in practice:
- It is created by software, not collected from the world.
- It can be produced at scale, far faster and more cheaply than gathering real data.
- It is used to train AI models, test software systems and protect privacy.
- It does not automatically solve the problems real data has. It can copy them, or create new ones.
What Synthetic Data Actually Is
When an AI company says it used synthetic data to train a model, they mean that some portion of the training set was generated by a computer programme rather than harvested from the internet, medical records, financial transactions or anywhere else in the real world.
It can take many forms. Text, images, audio, video, tabular records and sensor readings can all be generated artificially. The goal is to create data that looks and behaves enough like real data that a model trained on it learns something useful.
There are several main methods for generating it. Statistical models can sample from probability distributions built to match real-world patterns. Generative AI models, including the large language models used in products like ChatGPT and Claude, can themselves produce artificial text or images. Simulation environments, used heavily in robotics and autonomous vehicle research, create artificial sensor readings by running virtual versions of the physical world. Rules-based systems can generate structured records, such as fictional customer invoices, that follow real formats without containing any real customer information.
Why AI Companies Use It
The core problem is that AI models need enormous quantities of data to train well, and obtaining that data from the real world is difficult in several ways.
Scale is the first issue. A model trained on billions of examples needs billions of examples to exist somewhere. Web scraping has provided much of that historically, but there are limits to what is publicly available and what is legally or ethically permissible to use.
Privacy is the second issue. Training a model on medical records, financial data or personal communications creates obvious risks. Even data that appears anonymised can sometimes be re-identified. Synthetic data that statistically resembles a population of patients, for example, can allow a model to learn from the structure of medical information without exposing any individual’s actual records.
Edge cases are a third reason. Real data is naturally uneven. Rare events, unusual scenarios and corner cases that happen infrequently in the real world are underrepresented in real data sets. Generated examples can be designed to include these rare events at higher frequency, so a model learns how to handle them.
Cost and speed matter too. Labelling real data, such as having humans annotate images, transcribe audio or categorise documents, is expensive and slow. The generated output can come with labels built in, because the generation process itself knows what each example represents.
The Risks: When Synthetic Does Not Mean Better
Synthetic data has significant appeal, but treating it as an automatic upgrade over real data is a mistake.
The most persistent risk is that synthetic data learns from and reproduces the patterns in real data, including the errors and biases. If a model is trained on real hiring records that reflect historical discrimination, and that model is then used to generate that hiring data artificially, the output will reflect the same biases. Cleaning the data at source is still necessary; generating copies of it does not clean it.
A related problem is the feedback loop. Increasingly, AI models are being trained on data that includes content generated by earlier AI models. If synthetic data from one generation of models is used to train the next generation, errors, stylistic quirks and hallucinations can become more entrenched over time rather than being diluted. Researchers have described this as model collapse: the distribution of outputs narrows and degrades as the model feeds on itself rather than on diverse real-world signals.
Quality control is harder than it sounds. It can appear statistically similar to real data at a surface level while missing important details that would only appear with enough real-world grounding. A model trained on artificially generated transaction data might learn the correct format of transactions without learning the subtle timing and behavioural patterns that distinguish genuine transactions from fraudulent ones.
There is also a transparency problem. When AI companies describe their training data, it can obscure what the model actually learned from. If that training material was generated by another AI model, the original sources that second model learned from matter just as much, and they may be harder to trace.
What Counts as Good Synthetic Data
The quality of synthetic data depends on how closely it preserves the statistical properties that matter for the task it is supporting. NIST, the US standards body, has published guidance noting that usefulness depends on whether the generated data maintains fidelity to the original distribution while reducing privacy risk, and that these two goals are often in tension. More privacy protection typically means less fidelity, and vice versa.
Researchers and practitioners evaluate this type of training data using several tests: whether models trained on it perform as well as models trained on real data; whether it can be distinguished from real data by an independent classifier; and whether it introduces or amplifies specific failure modes. None of these tests is simple to run, and none of them gives a complete guarantee.
A Worked Example
Consider a company building a tool to help small businesses manage invoices. They want to train a model to extract information from invoice documents automatically, including supplier name, amount, date and line items. But they cannot use their customers’ real invoices to train the model, because those documents contain confidential commercial information.
Instead, they generate artificial invoices. A programme creates thousands of plausible invoice documents with randomised supplier names, product descriptions, quantities and totals, formatted to match a range of real invoice layouts. The labels, such as which part of the document is the total and which is the date, are automatically included because the generation process controls where everything goes.
The model trained on these generated invoices learns to extract invoice fields reasonably well. But it may still struggle with genuinely unusual invoice formats it was not trained on, or with invoices that contain handwriting rather than printed text. The generated dataset was good enough for many cases, but did not cover everything real data would have revealed.
What This Means For You
If you are evaluating an AI product or reading about how a model was built, knowing that synthetic data was used tells you something, but not everything. The key questions are: what the training material was generated from, how its quality was tested, and where it falls short compared to real data?
Synthetic does not mean unreliable, and real does not mean unbiased. Both types of data require scrutiny. A model trained on well-designed synthetic data for a specific task may outperform a model trained on carelessly collected real data. The word synthetic is not a warning sign on its own; it is a prompt to ask further questions about quality and purpose.
In Plain English
Synthetic data is data that a computer generates rather than records from the real world. AI companies use it because real data is limited, expensive, private and often full of gaps. It can be produced quickly and at scale, with labels already attached. The catch is that it does not fix the problems in the real data it was modelled on, and if AI models start training on data generated by other AI models, errors can compound over time. It is a useful tool, but usefulness depends entirely on whether it was made carefully.