V4 Flash

Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.

The unpredictability of AI spending stems from three core causes. First, the cost of training and running large language models is notoriously volatile. A single experiment might consume a modest amount of computing power, but a successful model can require exponentially more GPU time and energy, while cloud pricing for these specialized chips changes frequently based on global demand. Second, the technology landscape shifts rapidly. A promising open-source model can become obsolete within months, forcing your teams to abandon existing investments and retrain on a new, more expensive architecture. This constant iteration makes long-term budgeting nearly impossible. Third, the path from experimentation to production is riddled with hidden expenses. A proof-of-concept may run cheaply on small datasets, but scaling it to handle real customer data often demands custom data pipelines, enhanced security audits, and continuous human oversight for accuracy and bias—costs that are rarely visible at the start. To gain better control, you should shift from annual hardware budgets to flexible cloud credits that can be reallocated monthly. Institute a policy requiring any team that requests compute resources to provide an estimated cost cap before each new experiment. Finally, mandate a formal "exit cost" review after every pilot, forcing teams to calculate what it would actually take to integrate, maintain, and decommission a model before committing to full-scale deployment.