Welcome

I'm Anjali, a monetization data scientist currently doing self-directed research on inference economics through the Analogue Group.

I'm interested in how AI challenges SaaS economies of scale, and what that means for software pricing. This is not AI bubble speculation, macro analysis of labor trends, or investment advice.

Selected Projects

Full collection of side projects, experiments, and half-finished things lives in /vintage

Essays

  1. A token is not a stable unit of cost (Google Doc)
    Tim O'Reilly Podcast DSP Podcast Tweet
    TL;DR
    The Nth token in a conversation is an order of magnitude more expensive than the first
    Variable costs destabilize per-token API pricing, and Cursor and Anthropic's pricing changes indicate this
    Heterogeneous usage leads to fat tailed risks that compound with usage growh
    Tail risk represents lower margins, service degradation and system outages in extreme cases
  2. Why fat tails emerge at scale (Google Doc)
    TL;DR
    A few long-context requests can explode memory use since each carries its own non-shareable KV cache
    Because real-world traffic has near-infinite variance, the mean cost per request doesn’t converge
    Without live telemetry on cache pressure, batching logic and P/D ratios, providers can’t optimize unit economics
    The emerging pricing fix is priority contracts that turn unpredictable per-request costs into a forecastable aggregate-capacity problem
  3. Opportunities for mechanism design (Suggestions welcome)

I also write about things beyond pricing. See vintage/writing.

Diagrams

I used to build data viz professionally; more of that work lives in /vintage/work.

Some things I 100% recommend:

I'm at anjali.shrivastava99@gmail.com and on Twitter at @anjali_shriva.

View the old version of this site at /vintage