AI Training Data Has Run Out: Where Do We Go From Here?

Back in 2023, just one year after ChatGPT was released, researchers warned that the world could run out of high-quality data to train AI by 2025. In the years that followed, many studies echoed similar concerns.

Fast forward to February 2025, and the issue has resurfaced. Elon Musk himself jumped into the conversation and agreed with the industry experts that AI training data has, in fact, already been exhausted. In a livestream on X, Musk said:

“We’ve now exhausted basically the cumulative sum of human knowledge … in AI training.”

So, what happens now? Is the scarcity and diversity of AI training data limiting innovation? Could synthetic data solve the problem, or are there alternative solutions?

In this report, we answer these and other questions to better understand the current state of AI and where it is inevitably headed.

Table of Contents Table of Contents

1. Key Takeaways
2. Why Is AI Training Data Running Out?
3. What Happens When AI Training Data Runs Out?
4. How Private & Low-Quality Data Impact AI
5. Where Can We Find More AI Training Data?
6. The Bottom Line
7. FAQs
8. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. Why Is AI Training Data Running Out?
3. What Happens When AI Training Data Runs Out?
Show Full Guide
4. How Private & Low-Quality Data Impact AI
5. Where Can We Find More AI Training Data?
6. The Bottom Line
7. FAQs
8. References

Why Is AI Training Data Running Out?

While the general consensus is that training data is running out due to AI models becoming larger, faster, and more data-hungry, other factors are also at play. The quality of available data and the increasing restrictions on its use are playing a big role in this growing gap.

A 2024 study audited 14,000 web domains to gain insight into data consent, crawlable web data, and restrictions imposed by sites to limit AI developers from using their content to train AI.

Researchers concluded that in a single year (2023-2024), there has been a “rapid crescendo of data restrictions from web sources.”

The study found that restrictions on AI training data are widespread and growing. In many cases, websites actively limit access, while in others, their data infrastructure is simply not designed to accommodate the large-scale repurposing of online content for AI model training.

Ultimately, while the increasing size, power, and capabilities of AI models contribute significantly to the scarcity of AI training data sets, there are other factors at work behind the scenes.

One key issue is data quality, as AI models require high-quality tokens to train effectively. Although the world generates vast amounts of new data daily, much of it is either inaccessible to AI or fails to meet the high standards required by advanced LLMs and other machine learning (ML) models.

What Happens When AI Training Data Runs Out?

The short answer? Nothing good. Training data defines an AI model’s performance and capabilities. When high-quality data becomes scarce or runs out completely, AI models that are already in production will begin to drift, producing less accurate and reliable outputs.

Meanwhile, AI models still in development may be abandoned altogether, never making it to production.

But why does a lack of data have such a profound impact on AI models?

Epoch AI researchers estimated that the total stock of human-generated public text data is about 300 trillion tokens, with a 90% confidence interval ranging from 100T to 1000T. These figures exclude low-quality data.

We can think of those 300 million tokens as a massive, ever-expanding universal dictionary. Pieces, AI developer copilot, vividly illustrated this concept:

“Imagine you are teaching a child a new language. The more words and phrases they encounter, the faster they can learn. But if you only repeatedly show them the same words, their learning will slow and eventually stop.”

Like a child who has learned just a few words, an AI limited by a certain amount of tokens will hit a wall.

This wall threatens to impede the impressive speed of AI innovation.

How Private & Low-Quality Data Impact AI

Running out of publicly available AI data might have other indirect consequences, such as the urge to use private data. However, using private data requires heavy regulation as it causes privacy concerns and might lead to legal challenges.

To avoid these risks, developers increasingly explore lower-quality datasets, synthetic data, and legally compliant private data sources. Yet, each presents its own risks, including bias, compliance issues, hallucinations, privacy concerns, and cybersecurity vulnerabilities.

In essence, the training data shortage can impact AI development and lead to dangerous consequences ranging from legal problems to financial and reputational loss, cybersecurity breaches, and privacy leaks.

Where Can We Find More AI Training Data?

The synthetic data market is projected to grow from $351.2 million in 2023 to more than $2.3 billion by 2030.

While tech giants like IBM, Google, and Microsoft lead the charge, many other companies provide synthetic data tailored to various sectors such as healthcare, manufacturing, law enforcement, defense, border security, logistics, and IT.

According to Gartner, synthetic data will become the main type of data used for AI training by 2030.

Synthetic data can be produced through different methods, from combining algorithms with anonymized real-world data to using AI models to generate it.

However, synthetic data will always remain just a simulation prone to performance errors. To compensate, AI models leverage highly optimized algorithms for error correction and performance enhancement.

Synthetic data is cheaper and more consistent than private real-world data. It can be specifically customized, accelerating development workflows.

In contrast, legal channels to use private data have also emerged. On January 10, 2024, Bloomberg reported that OpenAI, Google, Moonvalley, and other companies are paying YouTube, Instagram, and TikTok creators for unpublished footage to train their AIs.

The deals are reported to be higher for those who sell 4K high-quality videos, netting thousands of dollars. This exemplifies how private high-quality data emerges as an organic response to the AI data gap crisis.

By optimizing models for specific tasks, developers can reduce errors, enhance engagement, and improve resource efficiency, including lower energy and cooling requirements in data centers.

The Bottom Line

No single approach – AI optimization, synthetic data, or private data alone – can solve the problem of AI training data shortage for developers. However, a combination of these solutions proves promising.

Ironically, the exhaustion of publicly available AI data could be a good thing. Faced with an imminent data shortage, developers will be forced to step out of their comfort zone and innovate, bringing new AI capabilities.

FAQs