The economics for enterprises adopting AI are incredibly favorable. A task costing $55 in human labor can be completed by an LLM for a fraction of the $5 cost of a million tokens. This massive arbitrage creates a powerful incentive for adoption and justifies large-scale infrastructure spending.
The market often misinterprets AI progress as linear. However, a clear 'scaling law' dictates that a tenfold increase in the computing power used to train LLMs results in a twofold capability improvement. This exponential relationship means future advancements will be far more disruptive and surprising than incremental projections suggest.
Despite rapid advances in AI models, the average corporate user has not yet caught up, creating a gap between capability and widespread implementation. This lag means the significant revenue inflection for hyperscalers' massive AI investments is not imminent but is more likely a 2026 event, once enterprise adoption matures.
Contrary to the view that AI token intensity will drop after the initial coding boom, the move from simple queries to autonomous 'agentic' workflows will cause an order-of-magnitude (10x) increase in token usage per task. This applies across all knowledge-based jobs, ensuring sustained and explosive demand for compute.