The AI industry is facing the problem of 'data exhaustion' - Elon Musk has also expressed concern.

It has been revealed that the learning data required for the development of Large Language Models (LLMs) was virtually depleted by 2023. Elon Musk of X (formerly Twitter), as the CEO of AI company xAI, stated that 'essentially, we have exhausted the collective knowledge of humanity in AI training,' highlighting significant challenges that the industry is facing.

Elon Musk stated in a live stream on X (formerly Twitter) in early 2025 that the collective knowledge of humanity had already been exhausted in AI training. Specifically, he mentioned that this critical point was reached in 2024, raising new challenges in AI development. This statement has generated significant impact in the industry not only due to Musk's position as a xAI executive, but also as a weighty observation based on his experience as a co-founder of OpenAI.

The importance of this view is further reinforced by independent observations made by Ilya Sutskever, former Chief Scientist of OpenAI. Sutskever proposed the concept of 'Peak Data' at the NeurIPS international conference on machine learning held in December 2024. This concept, reminiscent of the 'Peak Oil' concept in the petroleum industry, suggests that high-quality learning data has already reached its peak and is now on a declining trend.
Modern AI development, especially the learning process of large-scale language models (LLMs), requires vast amounts of text data available on the Internet. These models have been utilizing all forms of text data created by humanity, such as web pages, books, academic papers, and social media posts, as learning materials. However, industry leaders have recognized that there are clear limitations to high-quality data, especially data containing specialized knowledge or academic content.

This situation not only demonstrates the limitations of current AI development methods but also suggests a turning point for the industry. Improvements in model performance have traditionally relied mainly on the quantitative expansion of learning data. However, the depletion of available data indicates that this strategy is not sustainable. Of particular concern is the lack of high-quality data in specialized fields and emerging scientific and technological areas. In these fields, where the absolute amount of existing documents and data is limited, AI developers are forced to explore new approaches.

Additionally, data depletion raises not only quantitative issues but also qualitative challenges. Among the content on the Internet, there is a significant amount of misinformation and low-quality content. Excluding these, the actually usable high-quality data becomes even more limited. This situation poses a serious constraint, especially in the development of accurate AI models required in scientific and technical fields.

The AI industry is intensifying the use of synthetic data as a solution to the challenge of real-world data shortage. Synthetic data refers to learning data generated by AI models themselves, representing a new approach that does not rely on traditionally human-created content. According to research firm Gartner's forecast, by 2024, as much as 60% of data used in AI projects will be synthetically generated, surpassing industry expectations in both scale and speed of this transition.

Major technology companies have already embarked on this trend. Microsoft's Phi-4 model, which was open-sourced in early 2024, adopts a hybrid approach combining real-world data and synthetic data. Similarly, Google's new Gemma model also utilizes synthetic data, and the adoption of synthetic data by major technology companies is proving its practicality and effectiveness.
Notably, synthetic data plays an important role in the development of the latest high-performance AI models. Anthropic's Claude 3.5 Sonnet achieves some of its outstanding performance through the use of synthetic data. Additionally, Meta actively uses AI-generated data in the fine-tuning of the latest Llama series, highlighting that synthetic data is not merely a complementary tool, but a key element in cutting-edge AI model development.
The utilization of synthetic data is showing interesting advancements from a technical perspective. As mentioned by Elon Musk, there is an emerging cyclical approach where AI evaluates data generated through its self-learning process and utilizes it for further learning. This suggests the emergence of a new development model where artificial intelligence generates its own learning material, evaluates and refines it to evolve.

The innovation of this approach also lies in the ability to control the quality and diversity of data. Noise and bias that were inevitable in traditional real-world data theoretically become controllable. Moreover, the ability to generate data specialized for specific fields or situations as needed may lead to more efficient development of specialized AI models. For example, AI company Writer developed the Palmyra X 004 model primarily using synthetic data, reducing development costs to 0.7 million dollars compared to an estimated 4.6 million dollars for a similar OpenAI model.
However, there are also technical challenges in this transition. Quality control of synthetic data, ensuring transparency in the generation process, and most importantly, verifying the reliability of the generated data have become crucial issues. In particular, careful consideration is required concerning the potential amplification of unexpected biases or errors when using data generated by AI to train another AI in a cyclical process.
Researchers particularly point out the risk of 'model collapse' due to the use of synthetic data. This phenomenon refers to the decreased creativity of models, reinforcement of biases, and ultimately serious impairment of functions. Concerns are raised about the possibility of existing biases and limitations being amplified through synthetic data.

Furthermore, from legal and ethical perspectives, the use of synthetic data raises new considerations. Issues such as copyright, data ownership, and accountability for generated data require the establishment of a new legal framework distinct from traditional data usage. These challenges have become important points that the industry needs to address as it moves towards full-scale utilization of synthetic data.