Artificial intelligence has entered a transformative phase in recent years, propelled by large language models, massive compute infrastructures, and autonomous reasoning systems. The rapid expansion has revealed challenges across technology, society, economy, ethics, and infrastructure, such as data caps, soaring compute demand, synthetic‑data recursion, valuation inflation, and societal instability. Traditional scaling paradigms are encountering friction, making continuous exponential growth difficult to sustain.
This paper frames the phenomenon as “the end of AI exponentiation” and examines fluctuations both inside and outside the ecosystem. Inside, tension arises from compute and data‑center races, speculative capital, and the sprint toward superintelligence. Outside, labor disruption, governance disputes, public uncertainty, and geopolitically accelerated development of future intelligent systems and infrastructures become prominent.
Review