Rosemary J Thomas, Senior Technical Consultant, AI Labs, Version 1, on synthetic data fast becoming a critical enabler for next-generation Artificial Intelligence, offering scalable, diverse and privacy-safe datasets that can outperform traditional data sources in both quality and applicability.
In the race to build smarter Artificial Intelligence (AI) and supporting systems, many digital organisations and technology developers are focused on making more efficient models. The architecture, scale and benchmark scores are all under the microscope.
Yet it must not be forgotten that behind every impressive model lies a more fundamental force: data. Not just any data, but data that is high quality, diverse and available in sufficient quantity.
As we reach the limits of what real-world data can offer, whether that is due to privacy concerns, cost or simple scarcity, a quiet revolution is gaining pace. Synthetic data is emerging not just as a workaround but as a cornerstone of the next generation of AI. Those of us behind AI technology are seeing firsthand how synthetic data is reshaping the way models are trained, refined and deployed. Whether for automation, large language models or AI applications in tightly regulated sectors, synthetic data is solving problems that traditional data cannot.
Synthetic data may not be what the public thinks of when they imagine breakthroughs in AI and similar technologies. Yet behind every sophisticated chatbot, every automated decision system and every model making millions of predictions per second, there is a dataset that trained it. Increasingly, synthetic data is the invisible thread weaving through these systems, enabling their creation, evolution and accountability. As AI becomes more powerful and apparent both in business and day-to-day life, the importance of the data that fuels it will only grow.
Why synthetic data works
The answer lies in control and precision. While collecting real-world data is slow, expensive and increasingly encumbered by legal and ethical constraints, synthetic data can be created at scale. It is tailored to specific use cases and cleaned of potential noise or bias. It may not be perfect but it is flexible and increasingly practical.
Importantly, it can be generated in ways that real-world data cannot. Are you in need of data that models rare edge cases in financial fraud detection? Would you like a dataset that captures unusual but plausible interactions in a driverless car system? These are scenarios where real data is sparse or even non-existent and synthetic data steps in.
For example, in autonomous vehicle development, collecting real-world footage of extreme weather incidents, obscure road layouts or rare pedestrian behaviours could take years. By contrast, simulation environments can create thousands of plausible variations within weeks, giving models exposure to situations they may only encounter once in a million miles on actual roads.
Similarly, in financial crime detection, genuine examples of certain fraud patterns may be too infrequent to be statistically useful. Synthetic data can model these behaviours repeatedly, enabling algorithms to “practice” identifying them long before they surface in real transactions.
Data quality, diversity and volume
One of the most urgent challenges in AI development today is ensuring that models are not just accurate but fair, explainable and robust. That requires data that is representative across a wide range of demographics, scenarios and environments.
This is particularly important in healthcare AI. Suppose an algorithm is designed to detect skin cancer from medical imagery. If the majority of the real-world training images are from lighter-skinned patients, the model may underperform for darker skin tones. Synthetic data allows developers to generate additional images that realistically simulate variations in skin tone, lighting and lesion presentation, improving model fairness.
Recent events underscore the risks of failing to address this. In early 2024, Google’s Gemini model made headlines for generating historically inaccurate images, a byproduct of fine-tuning efforts that failed to balance diversity with contextual accuracy.
Volume also matters. Large language models require trillions of tokens during training. Real-world text data of this magnitude is expensive to acquire and often riddled with licensing and privacy restrictions. Synthetic data can expand datasets without breaching compliance rules.
Simulations deliver proven solutions
At the heart of synthetic data generation are simulations. These digital environments mimic real-world dynamics and can be used to test what works and what fails, creating controlled scenarios from which synthetic data can be drawn.
Simulations provide a safe, repeatable environment for experimentation, one that is particularly valuable in sectors like healthcare and financial services where real data is both sensitive and scarce.
Advanced techniques like GANs and VAEs allow us to push even further. GANs, through a competitive training process between generator and discriminator models, can produce highly realistic synthetic data. VAEs, meanwhile, offer a more stable and interpretable route, particularly valuable when explainability is paramount.
Responsible innovation
Synthetic data does not only enable better AI. It supports more responsible AI.
With privacy concerns growing and regulatory frameworks tightening, synthetic data offers a way forward that is compliant by design.
However, this is by no means a silver bullet. Generating effective synthetic data still requires significant computational resources and domain expertise. Relying too heavily on synthetic data without grounding models in the real world can lead to model collapse.
Industry adoption is accelerating
Across industries, synthetic data is moving from niche experimentation to mainstream adoption.
The common thread is that synthetic data removes bottlenecks in access, privacy and scalability, enabling faster iteration cycles.
A new era of model development
Perhaps the most exciting use of synthetic data lies in what happens after a model is trained.
Leading companies are already embracing this. Meta has used large models to generate synthetic training data for smaller ones. Google uses distillation to pass knowledge from larger models to more efficient variants like Gemini Flash.
Looking ahead: the strategic advantage
The next generation of AI systems – whether they are autonomous drones, conversational agents or predictive analytics engines – will require data that is richer, more varied and more adaptable than anything the real world can provide on its own. Synthetic data is poised to fill that gap.
