A factory for the decision layers AI is missing
The mega-labs are building one layer of the AI stack: the models. The layers that decide which model to call, which tool to use, which context to retrieve, which prompt strategy fits — those sit in internal scripts or get absorbed into lab margin. Hokusai is the protocol for building them as shared infrastructure instead, owned by the engineers who improve them.
The router is the first. The same primitives apply to every other decision layer where outcomes from many Integrators can train one shared policy.
How it works
An Integrator routes decisions through a Hokusai model.
Every decision call pays a small per-decision fee in USDC. For the router, that's a fee per coding task routed. Integrators get a smarter decision than they'd make with hardcoded rules, and the fees flow into the model's bonding curve.
Outcomes feed back to the model.
Test pass/fail, cost, latency — whatever the model's evaluation rubric measures. Outcomes are attributed on-chain to the Contributor who supplied them. The model gets better.
Contributors earn a position in the fee stream.
When outcome data produces a measurable improvement — a DeltaOne — the protocol mints tokens to the Contributor who supplied it. The token is a position in the model's fees, not a speculative asset. Hold it, or redeem for USDC anytime.
Two roles, often the same person
Integrators
Integrators route tasks through a Hokusai model. They pay the per-decision fees and they get a better decision than they'd make alone. Commercial Integrators can keep the resulting token flow as a new revenue line. OSS Integrators can pass it through to their users as an ownership feature.
Contributors
Contributors supply outcome data — the test results, latency, and cost signals that make the model better. In practice, most Contributors are also Integrators: the same harness that routes a task generates the outcome. The token rewards flow to whoever the Integrator configures at integration time.
What backs the token
Each Hokusai model has its own token and its own bonding curve. The curve fills with USDC from two sources: per-decision fees paid by Integrators, and direct USDC contributions when a buyer wants a position in the model.
DeltaOne mints tokens to Contributors when their data lifts the model's measured performance. The redemption value of those tokens tracks the USDC in the curve. As more Integrators route through the model, fee volume grows, the curve fills, and the redemption value of every outstanding token grows with it.
The token is a position in a real fee stream, not a position in a metric. The metric — DeltaOne — is just how the protocol decides who earned what.
DeltaOne and bonding curves, under the hood
For Integrators and Contributors who want the mechanics: how DeltaOne maps to tokens, how fees enter the curve, and how redemption works.
Each Hokusai model has a performance metric and a fee-backed economic loop matched to the decision layer it serves. Under the hood, the protocol creates a token for that model, and the token represents a position in that model's fees.
Hokusai uses DeltaOne as a unit of measurement for performance improvement. For example, improving the router's cost-adjusted task success from 42% to 45% would represent 3 DeltaOne units.
Each DeltaOne unit mints a predetermined amount of tokens, creating a direct link between measurable model lift and Contributor rewards.
Each model also has a bonding curve funded by Integrator fees and direct USDC contributions. The amount a Contributor earns for a DeltaOne improvement depends on the USDC already in that curve and the model's mint schedule.
This creates strong incentives for Contributors to supply high-quality outcome data and for Integrators to route more decisions through the shared model. Tokens can be converted to USDC at any time if crypto gives you the ick.
Core protocol properties
Automated attribution
Every outcome is attributed on-chain to the Contributor who supplied it. No judging committee, no off-chain bookkeeping.
Earned ownership
Tokens are minted in proportion to a Contributor's measured impact on the model — not for showing up, not for staking, not for governance theater.
Fee-backed value
Every Hokusai model is funded by the per-decision fees Integrators pay to use it. Tokens redeem against that fee stream, not against a promise.
Privacy by scope
Your data only ever trains the specific model you contribute to. Hokusai does not share, resell, or use contributions to train anything else.
Reference apps
The protocol is already broad enough to support multiple model categories. The coding task router leads here, alongside the existing healthcare, finance, and language examples already visible in the Hokusai model directory.
Routing / Multi-Model Coding
Coding Task Router
The first decision layer on Hokusai: a shared router for multi-model coding harnesses that improves through real routing outcomes.
Open routerImaging / Radiology
Chest X-Ray Classifier
A reference model showing how the protocol rewards measurable performance lifts on clinically relevant benchmarks.
Explore modelsFinance / Trading
Crypto Trading Prediction
A market-facing example of shared model improvement, transparent metrics, and on-chain contributor incentives.
Explore modelsNLP / Healthcare
Medical Text Analysis
Evidence that the same protocol primitives apply across specialized domains where better data compounds model performance.
Explore modelsBuild on the protocol
Got a decision layer worth sharing?
If you've identified a routing, selection, or optimization problem that today gets solved in scripts or captured by labs, Hokusai's primitives DeltaOne measurement, bonding-curve incentives, on-chain attribution give you a path to build it as a shared, owned model. Talk to us.