The Stack

AI 101 — What is synthetic data?

Synthetic data is information generated by a computer — not collected from the real world — that mimics the patterns, structure, and statistical properties of real data well enough to train an AI model.

It is one of the most important tools in modern AI development, and one of the least understood. Every major lab is using it, many products depend on it, and the debate over whether it is good enough to replace real-world data is shaping where the field goes next.

Why it matters right now

Training a large AI model requires enormous amounts of data. The public internet has been scraped, deduplicated, and licensed about as thoroughly as it can be — the well is running dry. OpenAI, Google, and Anthropic have all reportedly turned to synthetic data as a way to keep scaling their models without needing permission from every website on earth. When GPT-4 and Gemini were trained, a significant share of their training examples were likely generated by earlier models, not pulled from human-written sources.

The trend accelerated in 2025 and 2026 as companies like Meta and Alibaba pushed open-weight models that were trained with synthetic data pipelines. Hugging Face released open-source tools specifically for generating synthetic datasets, acknowledging that this is now a standard part of the training workflow, not a shortcut.

The reason it matters to you is this: the AI tools you use every day were shaped by data that may have been created by other AI tools. Understanding what synthetic data is — and what it can and cannot do — helps you understand the limits and strengths of the models it produced.

The mental model

Think of a recipe book. Real data is like a cookbook written by a chef who has cooked thousands of meals — it captures real flavors, real mistakes, real improvisations. Synthetic data is like a cookbook generated by an AI that has read the chef's book and now writes new recipes following the same patterns: similar ingredients, similar techniques, similar structure. The new recipes might be perfectly good — even better organized than the originals — but they were never actually cooked. They capture the pattern without the lived experience.

The key insight is that synthetic data works best when you already know what good data looks like. You use a real dataset to teach a model what the patterns are, then generate more data that follows those patterns. The synthetic data does not invent new knowledge — it fills in gaps, amplifies rare examples, and scales up what you already have.

A concrete analogy

Imagine a wildlife photographer who has taken five thousand photos of tigers. She wants to train an AI to recognize tigers in any lighting, any angle, any environment — but she only has photos from national parks in India. The jungle backgrounds are similar, the lighting is warm, and the tigers are well-fed. She could spend years traveling to Siberia, zoos, and tropical forests to collect more photos — or she could use an image generation model to create thousands of tiger photos in snowy landscapes, dense fog, twilight, and behind cage bars. Those generated images are synthetic data. They are not real photos, but they teach the AI what a tiger looks like in conditions the photographer never saw. The AI learns from the pattern, not the pixels.

Common misconceptions

"Synthetic data is fake data, so it must be worse." It is artificial, but "fake" is the wrong word. The statistical patterns can be indistinguishable from real data. Studies from Harvard and MIT have shown that models trained on carefully generated synthetic data can match or exceed models trained on real data for specific tasks. The quality depends entirely on how it is generated.
"Synthetic data can replace all real data." It cannot. If the real data has a pattern you do not know about — a bias, a rare edge case, a subtlety that only shows up in human behavior — synthetic data will not capture it. You cannot generate what you have never observed. This is why frontier labs still spend hundreds of millions of dollars licensing real-world data from publishers, experts, and specialized sources.
"Only big companies use it." Any team fine-tuning a model uses some form of synthetic data today. When you generate training examples from an existing model to teach a smaller one — a technique called distillation — that is synthetic data. When you augment a small medical dataset with AI-generated variations, that is synthetic data. It is standard practice at every scale.
"Synthetic data is always cheaper." Generating it requires compute, and that compute costs money. A 2024 paper from Hugging Face showed that synthetic data pipelines can reduce data collection costs by up to 90% compared to human annotation — but only for well-defined tasks. For open-ended, subjective tasks, human judgment is still irreplaceable.
"More synthetic data always helps." There is a known failure mode called "model collapse" — when a model is trained on its own output, it gradually loses diversity and starts producing repetitive, less accurate data. If you generate too much synthetic data from too narrow a source, the model gets worse, not better. This was demonstrated in a widely cited 2024 study by Shumailov and colleagues.

The bigger picture

Synthetic data sits at the heart of a tension in modern AI: the models are getting better, but the data they learn from is increasingly other models, not the real world. Whether this leads to a virtuous cycle — better models generating better training data — or a slow drift away from reality is one of the most important open questions in the field.

The best use of synthetic data is as a complement to real data, not a replacement. It fills gaps, covers edge cases, and scales what humans can collect. But it is only as good as the model that generated it and the real data that started the process.

Do you think AI-generated training data will eventually be good enough to replace human-created datasets entirely? Tell us in the comments.

Related reading:

AI 101 — What is fine-tuning? — how synthetic data is used to customize models for specific tasks
AI 101 — What is RAG? — why looking things up at answer time beats retraining with new data
AI 101 — What is an AI agent? — how agents use tools and data to accomplish goals