AI token spending jumps 122% to 160% as prices fall 67%, a16z data shows

AI just got a lot cheaper, and companies responded by spending a lot more on it.

Venture firm a16z reports that average AI token prices have fallen 67% since May 2026, while total token spending has climbed more than 120%, with estimates ranging from 122% to 160%. The data appeared in the firm’s Charts of the Week, published on October 31, 2026.

What the numbers show

First, a quick vocabulary check. In this context, a “token” is the billing unit for AI model inference. It is a small chunk of text a model reads or writes, and providers charge by the token.

The headline divergence is stark. Per-token prices dropped 67% since May. Total spend over the same stretch rose somewhere between 122% and 160%.

So the unit got cheaper, and the receipt got longer. Usage grew fast enough to swamp the price cut entirely.

The biggest driver appears to be AI agents. According to the a16z data, agents consume nearly five times as many tokens as human users do.

A person asks a chatbot a question and reads the answer. An agent runs a longer task, carrying more context and taking more steps, and every step burns tokens.

Agent token usage has also grown roughly 14 times since February 2026. That points to a shift toward larger, more automated workflows rather than one-off prompts.

The Jevons paradox, now with GPUs

A16z ties the trend to the Jevons paradox. The idea is simple: when a resource gets more efficient or cheaper to use, total consumption can rise instead of fall.

Cheaper inference makes new use cases economical. Tasks that were too expensive to automate at May prices suddenly pencil out at today’s rates.

There is a twist on the supply side. While token prices kept dropping, GPU rental prices moved sharply in the opposite direction.

Rental costs for Nvidia’s B300 rose 106% since mid-July 2026, according to the data. B200 rental prices climbed 66% over the same period.

That is an awkward combination for anyone selling inference. The output they sell is getting cheaper while the hardware they rent to produce it is getting pricier. The gap between those two curves has been labeled a “rack spread.”

The a16z charts also point to concentration among buyers. A relatively small group of top users accounts for a significant share of total token spending.

What this means

For AI model providers, the data is a mixed blessing. Volume is surging, which is the good part. But revenue per token is collapsing, and the cost of the GPUs underneath is rising.

For GPU owners and cloud operators, the picture looks friendlier. A 106% jump in B300 rental rates since mid-July suggests demand for compute is outrunning supply, at least for the newest chips.

For companies building on AI, each individual call is cheaper than it was in May. Yet agent-heavy products can rack up bills quickly, since agents use nearly five times the tokens of a human user.

The concentration point deserves attention too. When a small group of heavy users drives most of the spending, the market becomes sensitive to their decisions.

For now, a16z’s charts tell a straightforward story with a counterintuitive ending. AI inference got two-thirds cheaper in roughly five months, and the world’s response was to use so much more of it that total spending more than doubled.