Token-maxing is an AI cost sink - how to use agents without busting your budget

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Twelve months in AI is an eternity. Steve Lucas, CEO at integration technology specialist Boomi, was concerned last year that CIOs were rushing into gen AI initiatives without a clear sense of direction . Now, a year later, he's concerned that IT professionals are taking a similarly rushed approach with agentic technology , and the scale of token usage is only going one way: upward.

"Whether you work inside or outside a company, it feels like your core hustle is to use AI and you token max the heck out of that technology for your own job," he said to ZDNET.

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A token is the fundamental unit of information processed by an AI model, and research points to the soaring and unpredictable costs of agents as the technology consumes orders of magnitude more tokens.

As my ZDNET colleague Steven Vaughan-Nichols discussed recently , it wasn't so long ago -- certainly during the rise of generative AI -- that everyone was excited about their token leaderboard , which showed who had the most token usage in an organization. Today, token leaderboards are obsolete because no one can afford to waste tokens.

In the agentic era, token maxing is a sign of excess, and "tokenomics" -- the practice of measuring, pricing, and managing the consumption of tokens -- is a key business activity, even for a tech CEO like Lucas, who noted that tokens are being consumed at a much faster rate.

"A year ago, we weren't talking about tokenomics," he said. "But last year, I personally spent at Boomi 10 times the amount on Claude that I did the previous year -- 10 times; that's not sustainable. I can't do that every year."

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The onus now is on businesses and professionals to create their own enterprise-ready version of tokenomics. Agentic AI is set to transform how every business operates -- and creating a cost-effective technique for exploring agents is an urgent priority, suggested Lucas.

"What matters in the enterprise is ultimately the economics of AI," he said. "Most organizations will look to AI as the enterprise engine of the future. So, what matters now is, 'Can I operate AI at a return?' That is the fundamental question."

Business leaders suggested the best way forward is clear: Rather than constraining how people can use agents, give them guidelines and the context to make cost-effective model decisions.