Whitepaper
The Tokenomics of Enterprise AI
15 pages · 12 min read · Published July 2026
Why enterprise AI bills tripled while token prices collapsed 99.7% — and the five-year TCO math for putting a governed knowledge graph with agentic memory between your agents and your data.
What you’ll learn
- Why enterprise AI bills tripled while per-token prices fell 99.7% — the Jevons paradox at work
- The three compounding forces behind agentic token burn: loops, context stuffing, and transcript replay
- Modeled per-seat economics — ~22× fewer tokens and 77% lower cost per user with a graph-plus-memory architecture
- A five-year, 50→500-seat TCO ramp with every assumption named: ~$2.4M saved at today’s rates, ~$22M under repricing
Trusted by leading enterprises
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