Despite massive capital expenditures on AI infrastructure, a significant revenue inflection for hyperscalers is not expected until 2026. A lag exists because the average corporate user has not yet caught up with the rapid advancements in model capabilities, creating a temporary disconnect between spending and revenue generation.
AI model capabilities follow a predictable, non-linear scaling law: increasing training compute by 10x roughly doubles a model's capabilities. This exponential relationship, rather than an incremental one, is what will drive underappreciated and disruptive advancements across many industries.
The return on investment for enterprises adopting LLMs is exceptionally high. A typical complex task that might save $55 in human labor costs consumes a fraction of a million tokens, which cost about $5. This massive economic incentive is what fuels the surging demand for AI compute from corporate adopters.
The next wave of AI adoption involves 'agentic' workflows, where AI performs complex tasks autonomously. This shift from simple queries to agentic use is expected to increase token consumption by approximately 10x per task. This will drive a massive explosion in compute demand across all knowledge-work industries, not just coding.