Breaking News from Mars Finance, June 15 — UBS stated in its latest research report that corporate AI adoption is currently facing new friction caused by the rapid increase in token and computing power costs, but this issue is more due to a surge in usage rather than unit price inflation, and the overall risk may be overestimated by the market. The report pointed out that with the deployment of high-intensity tools such as AI coding agents, corporate token consumption has far exceeded expectations. This phenomenon has frequently appeared in investor discussions and has raised concerns that the spread of AI technology at the enterprise level may slow down. UBS found through interviews with about 13 IT executives from various companies that approximately 60% of the surveyed organizations consider AI token and computing power costs as a substantial issue, especially after shifting from simple chatbots to autonomous agent applications, where costs have changed from fixed SaaS expenses to variable consumption expenses, significantly reducing budget predictability. Most companies have already or plan to introduce safeguards, including token pooling, model downgrades, waste reminders, and restrictions on heavy users, to eliminate obvious waste rather than completely halt adoption. Some executives explicitly stated they are reluctant to heavily restrict employee use of AI, emphasizing "our goal is to get employees to start using AI," and thus they choose to optimize other budgets by reducing external IT services and consolidating cloud expenses to balance the rising AI costs. The report emphasized that almost all surveyed companies mentioned that AI adoption rates are accelerating, especially among developer teams, indicating that the cost increase is mainly driven by usage growth rather than unit cost inflation. UBS believes this is a normal cost control behavior for enterprises and not a sign of hindered AI adoption. Even companies like Uber, which used up their entire annual AI budget within a quarter, still maintain high token limits and actively promote AI applications, while offsetting costs by improving engineer efficiency. UBS further analyzed that AI model providers and large-scale cloud service providers are accelerating efforts to improve token efficiency, which may limit recent price increases and impact the market share distribution among cloud providers, with Google Cloud and AWS potentially gaining advantages through self-developed chips and models. Meanwhile, resistance to usage-based pricing models may increase, further pressuring non-AI software expenditures. Combining this with a previous survey of 140 companies on AI, the report pointed out that "uncertain return on investment" remains the biggest obstacle to adoption, while "lack of budget" has not yet entered the top five. However, as token cost issues become more prominent, this dynamic is increasingly becoming a key factor for companies to pragmatically optimize AI deployment.

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UBS: The rising costs of enterprise AI adoption stem from a surge in usage, and the market has overestimated the risk of token inflation.

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