Enterprise AI Spend Gets A Reality Check

Token prices are collapsing, but AI bills are not. The fight has now moved to governance, budgets and balance sheets.

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The cost of using AI is falling rapidly, with the price of processing a million tokens dropping by roughly 10X every year. The new price benchmark is being set across the market: DeepSeek V4 Flash charges $0.14 per Mn input tokens, OpenAI’s GPT-5.6 Luna costs $0.20, while Meta’s Muse Spark 1.2 is priced at $1.25 for the same volume. 

On the surface, this looks like a simple equation: cheaper intelligence should mean more AI. But inside enterprises, the conversation is moving in a different direction. 

As the cost of inference falls, the bigger question is no longer how much AI companies can afford to use, but where they should use it, and whether the returns justify the spend.

Tech leaders are increasingly looking beyond the price of a token to its productivity gains, revenue impact and business outcome. They are putting budgets around AI features, tracking which teams are consuming the most tokens, deciding which tasks deserve expensive frontier models and which can be handled by cheaper alternatives.

While the AI cost curve is collapsing, the bar for proving value is rising. So, as intelligence gets cheaper, what makes an AI token worth spending? That’s the question we’re digging into in today’s edition of The AI Shift.

As humans, the first instinct is always to treat cheaper tech as a licence to use more of it, and AI is no exception. However, the leaders we spoke with are approaching the shift differently: the goal........

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