Why Tokenomics Is More Than Just Counting Tokens — Virtualization Review

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Why Tokenomics Is More Than Just Counting Tokens

A few weeks ago, I had the pleasure of attending Splunk’s annual conference in Denver. One of the big topics of conversation at the event was something Splunk refers to as tokenomics. As the name suggests, tokenomics is essentially the idea of managing the costs associated with AI tokens.

On the surface, that concept is deceptively simple. The logical assumption is that tokenomics is all about controlling costs by counting tokens. In fact, one could argue that, to date, this has been the primary way organizations have attempted to control their AI costs. A company might say, “We consumed 10 million tokens last week. We know the cost per token, so we spent X dollars on AI.”

In reality, though, the cost-per-token metric is almost meaningless because it assumes that all tokens are equally valuable.

Splunk’s approach to tokenomics is to use its footprint within the enterprise to create actionable spending insights. The basic idea is that if you can attribute costs to specific users, teams, models, tools and workflows, then it becomes easier to see where token-related costs are coming from and to create a spending forecast before the bill arrives.

All of that is undeniably useful, but I think it only begins to…