AI Pioneer Warns: Industry Depends Too Much on Synthetic Data

Rich Sutton, Canadian computer scientist and Turing Award winner, believes the current approach in the AI industry has serious problems. According to his statement on a Sequoia Capital podcast, major technology companies are increasingly dependent on synthetic training data, which may be steering the sector off course. Synthetic data is information created by algorithms, unlike real data collected from the world. This approach has attracted companies because AI providers struggle to find enough new real data sources. OpenAI has actively sought large proprietary datasets, while Google recently spent 10 million dollars to acquire internal data from defunct airline Spirit Airlines, demonstrating the growing value of real data for training. Sutton advocates using real experience data, obtained when AI agents interact directly with real environments and learn from the results. He argues that simulations cannot capture the complexity of the physical world, citing examples such as the impossibility of predicting a drone's real behavior solely with synthetic data, or accounting for variables like friction and wear in robot motors. Recently, Sutton founded Oak Lab with his former student Khurram Javed. The startup develops AI agents that learn continuously from their own experiences, rather than relying primarily on large pre-prepared datasets.