Enterprises Rethink AI Spending as Token Bills Jump 60% in 9 Months

Q: Why might BigQuery's built-in token counter dangerously underestimate your actual AI costs before execution?

BigQuery’s built-in counter can dangerously understate real AI cost because it only estimates input text tokens, while actual billing and quota use include several other token sources that can be substantial. Google’s AI.COUNT_TOKENS does not count output tokens at all, so any long model response is missing from the estimate. More importantly, BigQuery’s generative AI quotas also count reasoning or “thinking” tokens as output. Those internal reasoning tokens can materially raise usage, especially on more complex prompts or models configured for deeper thinking. The function also omits added instructions and system-side prompt content that BigQuery may send along with the user’s visible input. That means the model may process more text than the pre-check suggests. It further ignores non-text and multimodal inputs such as images, audio, video, and PDFs, even though BigQuery AI functions can analyze those formats and they can increase consumption. This creates a practical risk: a workload that looks safe based on AI.COUNT_TOKENS may still run into much higher real token use, higher Vertex AI charges, or even quota exhaustion during execution. Google itself warns that the function can underestimate real consumption and recommends testing a small sample job to measure actual usage before setting production quotas. In short, the token counter is a rough sizing tool, not a reliable cost forecast, because it sees only part of what the model will actually process and generate.

3 articles · Updated · Financial Times · Oct 15

Enterprise AI computing spend has risen 60% in nine months, with Ramp saying total spending grew nearly sixfold in the 18 months to June 2026 as companies ran more complex, token-heavy workloads.
Agentic AI is driving much of that surge: autonomous workflows call multiple tools, extend task chains and make token use far less predictable even as the LLM Token Expenditure Index fell to 95 cents in early October.
Heavy users are absorbing the biggest increases. Median AI spend per employee climbed to $11.95 in July 2026 from $2.86 in January 2024, while the top 1% of users reached $7,400 and consume about 600 times more tokens.
To contain costs, companies are shifting work to cheaper models, using routing software, caching internal knowledge and monitoring per-employee usage; open-weight model adoption rose to more than 6% in July from 4.5% in January.
The broader shift is strategic: enterprises are treating tokens as a substitute for labor in some functions, with Bain expecting 20% to 30% of headcount costs to migrate into token spend over time.