The Problem: Currently, if a teacher (or lead dev) wants 50 students (or junior devs) to use an LLM with a specific, deep context (e.g., a 50-page curriculum or a complex repo), all 50 users have to re-upload and re-tokenize that context. It’s redundant, expensive, and forces everyone to have a high-tier subscription.
The Solution: USST allows a "Sponsor" (authenticated, paid account) to run a Deep Research session once and mint a signed Context Token. Downstream users (anonymous/free tier) pass this token in their prompt. The provider loads the pre-computed KV cache/context state without re-processing the original tokens.
Decouples payment from utility: Sponsor pays the heavy compute; Users pay the inference. Privacy: Users don't need the Sponsor's credentials, just the token. Efficiency: Removes the "Linear Bleed" of context re-computation.
I wrote up the full architecture and the "why" here: https://medium.com/@madhusudan.gopanna/the-8-6-billion-oppor...
The Protocol Spec / Repo is the main link above.
Would love feedback on the abuse vectors and how this fits with current provider caching (like Anthropic’s prompt caching).
mgopanna•2mo ago
When you look at the hidden costs of "Per-Seat" architecture in an education setting, the numbers get large very quickly. I broke down the cost of redundant context re-processing:
The Baseline:
The USST Math: By shifting from "Raw Mode" (everyone tokenizes everything) to "USST Mode" (Sponsor tokenizes once, students reuse): The Grid Impact: Beyond the money, this is an infrastructure stability issue. A simultaneous classroom start (e.g., 10:05 AM) currently looks like a 1 Megawatt spike on the grid. With shared context tokens, that drops to a 15 Kilowatt blip (just the inference delta).We don't need 100x more chips to solve this; we just need a protocol that stops treating every user session as a blank slate.