The business case for AI is strong, as executing a task for $2-$5 via AI can save an enterprise $55. This significant return on investment suggests companies are financially motivated to increase, not decrease, their spending on AI services over time, despite current market concerns.
Contrary to fears that efficient models will curb computing needs, lower costs will attract more users and enable complex applications, leading to higher overall consumption. This is a classic example of Jevon's paradox, where increased efficiency drives greater demand for a resource.
The primary obstacle to AI's growth is not semiconductor supply but physical power infrastructure. Data centers face a massive power deficit, needing more than double the contracted grid capacity by 2028, with long delays for connections, labor shortages, and local opposition acting as major hurdles.