What is Compute Sponsoring?
Standard in brief
Compute Sponsoring attributes a clearly labeled ad exposure to a unit of AI compute and defines constraints (separation, no influence, consent, proof) so ads can fund inference without touching model I/O.
Why Compute Sponsoring exists
GenAI has a unit-cost problem: every prompt consumes compute during inference. Costs scale with usage.
If a product runs a meaningful free tier, it needs a variable revenue stream. Advertising is one option, but only works if influence and privacy are bounded.
Compute Sponsoring is the constraint set: keep ads out of model I/O, require consent for semantic relevance, and make every billed impression auditable.
Classic ad standards like OpenRTB cover auction mechanics, not AI-specific separation, consent, or proof.
Compute Sponsoring v1.0 formalizes these requirements so ad-funded inference can be transparent and verifiable.
The Principles
These principles are architecture requirements, not policy guidelines. For separation, the ad delivery path is isolated from model I/O at the infrastructure level so they cannot interact.
Separation
ad delivery is isolated from model I/O at the infrastructure level
No input influence
sponsor information never enters the prompt or context window
No output influence
ads never cause prioritizations, omissions, or rephrasings
Data sovereignty
sponsors never receive user-level semantic data
Advertising transparency
ads are always clearly and fully labeled
Consent-based targeting
semantic relevance targeting requires explicit user consent
Classification
Compute Sponsoring classifies systems along two dimensions. Semantic Source, S0 to S3, describes what data is used to target ads. Semantic Persistence, P0 to P2, describes how long that data is stored. Together, they define three profiles:
CS-N
Compute Sponsoring Neutral
No semantic data is used for ad targeting. Ads are matched based on system metadata only, like language or time of day. This is the least invasive profile. Example: a coding assistant that shows untargeted banner ads during inference.
CS-S
Compute Sponsoring Situational
Semantic data from the current prompt is used to match ads, but nothing is stored beyond the current request or session. This profile allows relevant ads without building user profiles. Example: a travel chatbot that shows flight deals when the user asks about weekend trips, but forgets the topic on the next request.
CS-P
Compute Sponsoring Profiling
Semantic data is accumulated across sessions. This enables profile-based advertising and requires the strongest consent and transparency mechanisms. Example: an AI assistant that learns user preferences over time and targets ads based on accumulated interests.
wavebird operates as CS-S (S1/P0): situational relevance based on prompt topic, no data persistence across requests. Semantic targeting requires explicit user consent.
How wavebird implements it
wavebird operates as CS-S (S1/P0): it may use the current request’s topic category for situational relevance, and it does not persist that signal across requests.
The delivery signal is produced by the data firewall (topic category + language). Raw prompts stay inside your app.
Consent gating is explicit; without consent, delivery falls back to CS-N. Failure behavior is fail-closed (see Safety).
Read the full standard
Read the Compute Sponsoring v1.0 white paper for the full matrix, notation, and disclosure rules.
Feedback and critique are collected on GitHub.
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Last updated: 2026-04-03