LLM vendors sell tokens. Runtimes move them. Nobody independent watches the door. We do.
Enterprise LLM spending is projected to exceed $100B by 2028. The cost problem is not intelligence — it is waste. Repeat queries, unoptimized routing, and zero accountability between the vendor and the buyer.
8.5% of requests are cache-miss but consume 56% of cost. That is a structural hole, not an edge case.
No independent middleware exists between LLM vendors and runtimes. The vendor watches its own door.
Independent infrastructure that cuts waste, captures Experience, and stays on the user's side. Apache 2.0. Zero telemetry.
The Agent economy is forming. AI Agents are moving from demos to real operations — managing stores, processing orders, handling safety incidents. They need infrastructure they can trust: no vendor lock-in, no data extraction, full auditability.
When an Agent spends your money on LLM calls, you need someone watching the door — representing no vendor, aligned only with you. That is LAO.
Every Agent operation produces Experience. Accumulated Experience makes Agents measurably more reliable. The first to build this loop wins. We are already producing it.
Before the thesis, the evidence. Here is what exists today, in order:
LAO — open-source infrastructure, captures Experience from every operation TODAY
↓
Experience Protocol — frozen spec, 29/29 cross-language tests TODAY
↓
Experience Catalog — registry, 74/74 smoke tests TODAY
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9 Agents operating a real gym, producing real Experience TODAY
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The question: can AI turn 7 years of losses into profit? BEING TESTED
Every claim has a source. See the evidence →
LAO has been positioned around post-training from its open-source beginning: it creates an entry point for voluntarily authorized, confirmed experience. The data stays with the user; there is no telemetry; only the portion a user authorizes can enter the loop.
Reusable experience is matched in a marketplace by a user's cognition, habits, and workflow — not by a simplistic pairing of "wrong" and "right" examples. Selecting, passing, buying out, or leaving an experience idle are preference signals.
This post-training view is for investors. It coexists with LAO's independent-third-party position: users see savings and opportunity; agents see a tool they can verify for their owners.
Experience-card records carry founder approval, a SHA-256 fingerprint, a revenue-share rule, a withdrawal right, privacy review, and provenance.
See the confirmed experience display →Experience is classified by impact: reference-only, decision aid, or runtime behavior. Cognitive experience has a preference firewall before it can affect behavior.
A feedback bus is designed to return hit-rate and stability feedback across the RIS → LAO → RIS loop, with lineage retained for review.
Experience is structured by event domain, property labels, and output destination, so it can be reviewed and routed instead of becoming an undifferentiated data pool.
We state our own design and its evidence. Where an external comparison needs independent confirmation, the status remains visible rather than being converted into a conclusion.
| Dimension | LAO design | External comparison status |
|---|---|---|
| Data sovereignty | Data stays on the user's side; no telemetry; authorized experience is the only entry to the loop. | Interview account: Harvey relies on centralized platform-use data. Awaiting external verification |
| Ownership and revenue share | Confirmed experience cards are designed with approval, fingerprint, revenue-share rules, withdrawal rights, and privacy review. | Public information has not shown a user-experience ownership or revenue-share mechanism. Awaiting external verification |
| Collection method | Source collection begins at the authorized, confirmed-experience entry point; marketplace choices provide preference signals. | Interview account: feedback is used to retrain models. |
| Openness | Apache-2.0: the recipe is open while users retain control of their experience. | Harvey is closed-source. |
| Training-data lineage | Each sample is designed to be replayable to an authorization record, and withdrawal is designed to propagate through the training layer. | No comparable public statement has been identified. Awaiting external verification |
"Awaiting external verification" items will be finalized only after Baron's external verification receipt is available. Interview accounts are labeled as such; they are not presented as settled facts.
This is not a lab. It is a real business with a real question being tested.
Agent operations → Experience captured → Reliability improves → More operations → More Experience
What exists: Steps 1-2 are operational (LAO captures Experience from real gym operations).
What is being tested: Steps 3-5 (does reliability measurably improve? does the flywheel produce value beyond cost savings?)
The marketplace is currently in its display-and-education stage. Its first real match or transaction will be the point at which the preference loop ignites — not a milestone we claim has already happened.
Continue the thesis.