Token prices fell 98%. Your AI bills tripled. Goldman Sachs says this gets 24x worse.

Token prices fell 98% since 2023.

Your enterprise AI bills tripled over the same period.

Both facts are true. And the gap between them is the defining financial story of enterprise AI in 2026.

GPT-4-equivalent performance now costs $0.40 per million tokens, down from $20.

Yet the average enterprise AI budget grew from $1.2M to $7M in two years.

Per-developer token consumption surged 18.6x in nine months.

The culprit is architectural, not pricing.

Agentic workflows consume 50 to 500 times more tokens than a simple chat interaction.

A single coding agent session burns more tokens in an hour than a developer used in a month of chatbot queries.

Your infrastructure was built for chatbots. You are deploying agents.

Goldman Sachs projects token consumption will multiply 24 times by 2030, reaching 120 quadrillion tokens per month.

A former Apple engineer just raised $80M at a $450M valuation because the entire inference stack is optimized for the wrong workload.

Sail Research trades latency for throughput and claims 3x to 10x cost improvement for long-running agents.

Uber burned through its entire 2026 AI coding budget by April.

One Fortune 500 company ran up a $500M Claude bill in a single month.

Your CFO is about to ask why AI costs tripled while vendor prices collapsed.

The answer is your architecture, not your vendor.

Audit your inference stack. If you are running agents on chat-optimized infrastructure, you are burning 3 to 10x more than you need to.

Your next budget cycle will be brutal. Plan for volume, not access.