Seven terms that decode whether AI is overpriced, fairly priced, or criminally underpriced. Read in order.
Start with the aggregate gauge: how stretched are AI valuations?
A 0-1000%+ composite score measuring how bubbled (or not) the AI sector is · BenchGecko's flagship economy indicator.
“The Bubble Index exists because no one else tracks this right. CoinGecko has crypto indices. We have AI. Per BenchGecko, the number is 487%.”
Read full chapterPrice to sales: the ratio behind most AI valuation debates.
Valuation divided by annual revenue · the single number that captures whether an AI company is fairly priced, frothy, or bubbled.
“P/S is the single most important AI economy metric. Every other valuation debate is downstream of this number.”
Read full chapterAnnual recurring revenue, the number AI companies report most.
Annualized Recurring Revenue · take current monthly recurring revenue × 12. The standard AI/SaaS revenue metric.
“AI ARR is inflated by token-cost deflation · more tokens per dollar means more revenue with the same underlying customer workload. Watch active workload growth, not ARR.”
Read full chapterHow fast labs spend the money they raise.
How fast an AI company is spending investor cash · usually measured as monthly cash outflow minus revenue.
“AI burn rates are dotcom-era · not by accident. The labs that convert burn into moat survive. The ones that don't, won't.”
Read full chapterThe infrastructure spending cycle behind the boom.
The billions hyperscalers and AI labs spend each year on GPUs, datacenters, and training clusters · the #1 driver of AI spending narrative.
“Nobody knows yet · but BenchGecko tracks it daily.”
Read full chapterWhy the cost of a token keeps falling.
A model architecture where only a subset of experts activate per token, slashing inference cost while preserving quality.
“MoE is why the AI price floor just dropped by 30×. Any model that isn't MoE by end of 2026 will be priced out of the commodity tier.”
Read full chapterThe premium tier, and why it matters for pricing.
A model that generates internal reasoning tokens before producing the answer, trading inference cost for accuracy on math and logic.
“Reasoning models changed the pricing ladder: the frontier is no longer one price.”
Read full chapterBy the end you can read the AI Bubble Index like a pro · understand what drives valuations, why inference economics matter, and where the real fragility lives.
Six terms to go from "I need an AI" to "here is the cheapest model that meets my spec."