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The debate over the depletion of real data for AI training highlights a transition toward the use of synthetic data. Elon Musk and other experts highlight this shift as necessary for technological evolution, but not without risk.

Key Points:

• Data exhaustion: Real data sources for AI are reaching their limits.
• Synthetic data: Generating data through AI is an emerging solution.
• Economic Benefits: Development costs can be drastically reduced.
• Risks: Synthetic data can introduce bias into models.

The progress of AI faces a crucial challenge: the depletion of real data sources. According to Elon Musk, owner of the xAI company, humanity has essentially reached the limit of cumulative knowledge that can be used for training AI models. This statement, made during a recent conversation on X with Mark Penn, reflects a theme already raised by Ilya Sutskever, former chief scientist of OpenAI, who introduced the concept of “peak data.” Sutskever predicted that the scarcity of real data would require a change in the approach to developing AI models.

The solution proposed by Musk and other experts is the adoption of synthetic data, or information generated directly from AI models. This methodology would not only overcome the limitation imposed by real data, but also open the way for new self-learning paradigms. Musk described this process as a mechanism in which AI continuously self-evaluates and improves through the processing of its own outputs.

The tech industry is already exploring this avenue. Giants such as Microsoft, Meta, OpenAI, and Anthropic are using synthetic data to refine their leading models. Microsoft, for example, recently made open source the Phi-4 model, trained with a mix of real and synthetic data. Google has taken a similar approach for its Gemma models, while Meta has refined its Llama model set with AI-generated data. Anthropic has also leveraged this technique for the development of its Claude 3.5 Sonnet.

One of the most obvious benefits of this strategy is the cost savings. Models such as Writer’s Palmyra X 004, based almost entirely on synthetic sources, have been developed at significantly lower cost than traditional models, reducing expenses from millions to a few hundred thousand dollars. However, synthetic data is not without its pitfalls. Recent studies have highlighted the risk of a phenomenon known as “model collapse,” in which systems trained on synthetic data become less creative and more prone to repeat biases present in the source data. This risk raises questions about the quality and reliability of applications based on artificially generated data.

 The adoption of synthetic data represents a promising but complex solution needed to overcome the limitations of the current AI landscape.