Thus, enterprises struggle to forecast and control costs, and, because AI is moving so fast, many organizations lack the “maturity and frameworks” to determine ROI, he noted. Agent-driven workflows are difficult to govern, context windows become bloated, budgets are wiped out earlier than anticipated, and token spend becomes hard to justify.
Added to this, light users such as non-developers will increase their usage as they become more familiar with, and even reliant on, AI tools, driving up token consumption and spend even more.
Tyagi said that, while AI is incredibly valuable, he sees no “direct relationship” between the number of tokens developers consume and their productivity gains. Rather, applying context engineering principles to optimize or reduce token consumption increases quality.