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AI inference is getting cheaper, but your agents are getting more expensive
Brief published August 19, 2026 ยท Original source published August 18, 2026
Original reporting by Taryn Plumb at computerworld.com.
Automated brief. Verify important details at the original source.
What happened
Gartner research finds that LLM token costs are projected to fall by 95% by 2030, yet overall AI inference costs are expected to rise because agentic workflows consume far more tokens per task than simpler model calls. The dynamic creates a cost paradox: cheaper per-token pricing does not translate to cheaper AI operations when agents chain multiple reasoning steps, tool calls, and context windows together. Analysts note that AI already outperforms humans on many routine tasks in speed and quality, and agents can surface patterns across large datasets faster than people can.
Why it matters
Builders pricing agentic products on today's token rates may be underestimating future infrastructure spend. As agents grow more capable and are deployed at scale, the aggregate inference bill can grow even as unit costs shrink. Teams need cost models that account for token volume per workflow, not just per-request pricing.
What to watch
Whether hardware efficiency gains or architectural changes such as smaller specialized models can offset rising per-workflow token consumption before agentic deployment scales broadly across enterprises.