We're asking the wrong question about the cost of enterprise AI
Enterprise AI is reaching an important economic turning point.
For the past two years, organizations have largely evaluated AI through the lens of token pricing and model capability.
As AI moves from experimentation into business-critical operations, that approach is becoming increasingly incomplete.
The question is no longer simply what each token costs, but what it costs to deliver AI capability that is affordable, sustainable and commercially predictable at enterprise scale.
Founder and Chairman of Argyll Data Development.
Every AI interaction ultimately depends upon physical IT infrastructure, consuming compute, memory, networking, electricity and cooling regardless of how those costs are presented to the customer.
Understanding the economics of that infrastructure is becoming just as important as understanding the capabilities of the models themselves.
Organizations that focus only on the price of a token risk overlooking the factors that will ultimately determine the long-term cost, resilience and sustainability of enterprise AI.
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