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Discusses token economics in AI, emphasizing that token value depends on intelligence and speed, and that optimizing tokenomics should start with customer use case.
A deep-dive analysis exploring why AI companies continue to scale systems despite prominent researchers declaring the end of the scaling era and widespread acknowledgment of diminishing returns, examining the structural and financial incentives driving the industry.
This article argues that fundamental architectural limitations, not scaling deficits, prevent current LLMs from achieving true rationality—the ability to recognize and switch frames—citing empirical failures like the reversal curse and frame-transfer issues, and suggests that scaling alone may not bridge this gap.
Deloitte advocates moving from basic GenAI to 'autonomous intelligence' for automating complex tasks and improving decision-making to drive business growth.