IA FORUM MEMBER INSIGHTS: ARTICLE
By Laxmikant Pukale, Director, Advanced Capabilities - AI & Intelligent Automation, USAA
Every week brings another headline about the rising cost of AI. Some of them are genuinely startling. One large technology company reportedly exhausted its entire 2026 AI coding budget in four months. Gartner now predicts that by 2028, AI coding costs will overtake the average developer’s salary, driven by rising token consumption and the industry shift to consumption-based pricing.
The pattern is not isolated to one company. Through the first half of 2026, several large enterprises introduced per-employee monthly caps on AI coding tools, and at least one major technology division moved its engineers onto a cheaper alternative after token billing consumed an annual budget ahead of schedule. When organizations of that size are surprised by their own invoices, the problem is not carelessness. It is a cost model that behaves differently from the ones our budgeting processes were built for.
It is easy to read those numbers and conclude that AI has simply become too expensive. I do not think that is what is happening. I think we are measuring the wrong thing.
AI is not getting more expensive. AI is getting more ambitious, and more real.
Unit Prices Are Falling. Consumption Is Not.
The cost of inference has fallen at a remarkable pace. Stanford’s AI Index found that querying a model at GPT-3.5 level dropped from $20 per million tokens in November 2022 to $0.07 by October 2024, a more than 280-fold reduction in roughly eighteen months. Go back further and the curve is steeper still: the first commercial large language model API launched in 2020 at around $60 per million input tokens. Competition, better hardware, and model optimization keep pushing that number down.
So why are enterprise AI bills growing?
Because the workload changed. A simple chatbot query is one model call. An agentic workflow, where the model plans, calls tools, checks its own work, and iterates, consumes far more. Gartner’s analysis places agentic tasks at five to thirty times the tokens of a standard chatbot exchange. Add retrieval, which pushes thousands of pages of context into a single query, and the multiplier grows again. Work that cost a few cents as a one-shot call can cost dollars once it is rebuilt as an orchestrated agent session.
Token prices went down. Token consumption went up faster. That is the whole cost crisis in one sentence.Read More...
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