Survey: just 11% of 396 firms could predict AI spending; Microsoft says cheaper models cost more on 32% of 6,800+ tasks.
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| Source: Techmeme | Original article
A Wall Street Journal survey of 396 enterprises reveals that just 11 % can accurately forecast their AI spending, underscoring how token‑based pricing has turned AI costs into a moving target for finance teams. The same report cites a Microsoft‑backed study that ran more than 6,800 math, coding and science tasks across a range of models. In 32 % of those tasks, cheaper, lower‑priced models actually consumed more tokens – and therefore cost more – than their higher‑priced counterparts.
The findings matter because AI budgets are already straining corporate finance. As we reported on Oct. 5, “Spending on AI Is Becoming Almost Impossible for Businesses to Budget,” and the new data confirms that the problem is not just perception but measurable mis‑forecasting. When token consumption varies unpredictably, line‑item budgeting becomes unreliable, forcing firms to either over‑allocate funds or risk service interruptions. The Microsoft result also challenges the assumption that the cheapest model is always the most economical, suggesting that performance‑per‑token efficiency can outweigh headline pricing.
What to watch next are the tools and governance frameworks that will emerge to tame this volatility. Vendors are likely to roll out more granular cost‑tracking dashboards, while enterprises may adopt AI‑specific budgeting practices or shift toward models with clearer token‑usage guarantees. Analysts will also keep an eye on whether Microsoft’s Azure pricing or model‑selection guidance adjusts in response to the study, and whether the broader market moves toward standardising token accounting to make AI spend more predictable.
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