Portrait of Anjali Shrivastava

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, nor investment advice.

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

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. As variance compounds, cost and value diverge (Suggestions welcome)
    TL;DR
    • Cost, value and scale line up in software but not in AI
    • Compounding variance means cost and value diverge at scale
    • The Trilemma: every pricing metric forces a tradeoff
    • Value is in coordination, not outcomes
  4. 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:


Open Research


I'm treating AI pricing as an open research question. The best part of public-facing work is when others push my ideas into new territory. Like when:

I also publish my drafts in public Google Docs. You can see revisions, debates in the comments, and where my perspective changes.

These questions feel under-discussed relative to their importance. Many talk about AI strategy, but much of that analysis is not grounded in empirical commercial realities, architectural constraints, or actual market opportunity.


If you're thinking about any of this, I'd love to compare notes. I'm at anjali.shrivastava99@gmail.com and on Twitter @anjali_shriva.

View the old version of this site at /vintage