Source: Transformer
The frontier AI labs racing toward increasingly capable models face a concrete resource constraint: they're running out of high-quality training data faster than expected, and synthetic data generated by AI itself introduces quality degradation at scale. Labs are already slowing training cycles, pivoting toward smaller models, or relying on more expensive human-curated datasets—moves that flatten the cost advantages powering recent acceleration. The bottleneck exposes a hard technical ceiling that compute capital alone cannot overcome, potentially compressing timelines for breakthroughs that seemed inevitable two years ago.