AI faces a “data hunger”: The trillion-dollar game is no longer about models
For years, the AI race has focused almost exclusively on creating ever-larger large language models (LLMs). But a new reality is emerging: AI is not limited by computing power, but by data fuel.
The problem: Big models, little data
According to EPOCH AI, the size of training datasets for LLMs has increased 3.7 times per year since 2010. If this pace continues, the world’s public, high-quality data resources could run out as early as 2026–2032.
That means that, before the supermodels reach their algorithmic limits, they could face a “wall” of data scarcity.
Data: The New Scarce Asset
As companies tighten access controls and copyright laws become more stringent, real-world human-generated data is becoming the gold standard—both scarce and expensive. The data labeling market is expected to be worth $3.7 billion by 2024 and could reach $17.1 billion by 2030, reflecting a clear shift in power from modelers to data owners.
Synthetic data—the way out or the trap?
Some companies are betting on synthetic data. But “AI training AI” poses the risk of loops, bias, and illusions. More importantly, synthetic data often lacks the chaos and irregularity that help AI understand the real world.
Who has the power?
If the early days of AI were about “who has the most powerful GPU,” the coming era is about “who has the rarest data.” Large platforms like Google, Meta, or X (Twitter) are now data fortresses – controlling not only a huge amount of information, but also the power to shape the next generation of AI.
This also opens up a new direction: individuals, communities, and small businesses, if they know how to manage and commercialize data, can become key players in the AI value chain.
The future: Not just building models, but also nurturing models
The AI industry is entering a phase where every byte of data has strategic value. The big question is no longer “who will create the strongest LLM?”, but “who will own the exclusive data set to feed it?”.
