The article argues that the massive surge of innovation in AI over recent years was fueled by a wealth of real-world data — but that well of “novel and unseen” data is drying up. Because many AI models have already consumed vast amounts of public-domain text, images, and structured data, future progress will increasingly require new kinds of data that go beyond what already exists.
To address this challenge, the article proposes that synthetic data — data artificially generated through computation, simulation, or AI itself — can act as a new foundation for training advanced AI. Using computational models, simulations or “virtual experiments,” synthetic datasets can represent scenarios, edge-cases and combinations of parameters that are rare or non-existent in real data. This can be especially valuable in complex, quantitative domains such as drug discovery, materials science, climate modelling, or risk-analysis, where the space of possible configurations is astronomically large, and real-world sampling is impractical or impossible.
The use of synthetic data has many potential benefits: it enables rapid scaling of datasets, helps overcome privacy and scarcity constraints (since it doesn’t rely on sensitive personal or proprietary information), and allows creation of diversified, controlled and high-quality data tailored to specific tasks. In sectors like healthcare, finance, manufacturing or energy, this could democratize AI innovation — letting smaller firms, researchers or startups build powerful models without needing vast historical datasets.
But the article also warns that realizing this potential depends on building a supportive “innovation ecosystem”: strong data-governance standards, transparency about how synthetic data is generated, and careful validation to avoid biases or unrealistic outputs. Without oversight and rigorous practices, training AI on synthetic data risks producing models that are overfitted, misleading, or detached from real-world complexity. The article argues that companies and governments willing to invest in both generation and governance of synthetic data will have a competitive advantage in the coming years.
