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# When AI Eats Itself: The Perils of Training on Synthetic Data
- URL: https://www.thedigitalspeaker.com/when-ai-eats-itself-the-perils-of-training-on-synthetic-data-2/
- Published: 2024-08-02T05:08:28.000Z
- Updated: 2026-07-27T05:28:06.000Z
- Description: Is feeding AI its own data a recipe for progress or the digital equivalent of inbreeding?
- Author: Dr Mark van Rijmenam, CSP
- Tags: News, #seo-post-1

Is feeding [AI](https://www.thedigitalspeaker.com/ai-strategy-speaker/) its own data a recipe for progress or the digital equivalent of inbreeding?

A recent study reveals that training AI models on AI-generated data leads to "model collapse," causing the models to produce nonsensical outputs. Researchers at the University of Cambridge demonstrated that successive iterations of a language model, trained on data generated by its predecessor, quickly devolved into gibberish.

This phenomenon, which I covered a year ago, poses a significant challenge as human-generated content diminishes and synthetic data pervades the internet. To avoid this collapse, AI developers must ensure diverse, high-quality human input remains in the training mix.

How will we balance the efficiency of AI-generated content with the necessity for authentic human data?

Read the full article on [Nature](https://www.nature.com/articles/d41586-024-02420-7?ref=thedigitalspeaker.com).

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