OSCF — Building the Shared Intelligence Infrastructure for Human–AI Co‑evolution

Introduction

Artificial intelligence is advancing fast, but the way humans interact and learn with these systems is fragmented. We need a new paradigm that moves beyond AI as a tool or black box: a shared language and knowledge substrate where humans and AI co‑evolve—learning together and producing aligned, transparent, verifiable collective intelligence by building together.

OSCF is defining a new category: AI–Human Shared Intelligence Infrastructure. We are building the foundation on which people and models contribute to, govern, and reuse knowledge in common—so every answer becomes auditable, current, and useful.

Problem Statement

• Opaqueness: Outputs arrive without lineage, validity windows, or tests—undermining trust and slowing approvals in sensitive domains.

• Siloed intelligence: Static, model‑centric workflows lack a continuous human‑AI feedback loop; expertise and values don’t shape knowledge in real time.

• Limited collaboration: Humans are treated as end‑users, not partners in intelligence; AI’s discoveries aren’t systematically folded back into organizational memory.

• Fragmented fixes: Post‑hoc governance and point tools observe behavior but don’t provide a unified substrate for knowledge to stay correct, connected, and up‑to‑date.

OSCF’s Solution Overview — Two Integrated Engines

1) OSKI — Open Symbolic Knowledge Infrastructure

A global knowledge engine that breaks down claims into Knowledge Atoms—small, source‑backed units tagged with metadata, tests, and provenance. These atoms link into a dynamic Knowledge Tree, an ever‑evolving map of verified information.

When asked a question, OSKI assembles answers and issues an Answer Label—a receipt detailing sources, a confidence score, and an expiry/review window. Behind the scenes, an Entropy Field Protocol monitors knowledge decay; as facts age or conflicts arise, OSKI triggers refresh routines to update or retire affected atoms. In short, OSKI turns AI from a black box into a living knowledge base—every answer is transparent, testable, and refreshable.

2) The AI‑Native University

A first‑of‑its‑kind education and productization engine that trains people to innovate, govern, and co‑evolve with symbolic, time‑aware AI. The curricula span symbolic design, temporal epistemology, emergent agency, language folding, and field restructuring—reclassifying classical disciplines into new cross-disciplinary domains for the AI era.. Learners practice building/refining Knowledge Atoms, reading Answer Labels, managing entropy/refresh policies, and upholding governance. Education + infrastructure creates a feedback loop: a skilled community improves the knowledge base, sets update policies, and audits outcomes.

Day‑One Impact

• Answer receipts: Each AI response ships with a verifiable label—what it’s based on, who/what verified it, and when it was last checked.

• Confidence meter: Decision‑makers see how sure the system is and why (evidence, tests, recency).

• Expiry/review windows: Every answer carries valid‑from/valid‑to; stale items are visible at a glance.

• Auto‑refresh & graceful decay: Approaching expiry or new evidence triggers refresh; unvalidated claims decrease in confidence instead of silently persisting.

  Net effect: AI moves from opaque to accountable and current on day one—no rip‑and‑replace of your LLM/RAG stack.

Value Proposition — Business Impact & Public Good

• Risk down, trust up: Provenance, confidence, and expiry reduce compliance exposure and errors; stakeholders trust answers they can inspect.

• Time & cost savings: Target 30–50% reduction in manual verification; faster approvals, fewer rework cycles.

• Compounding reuse: With Knowledge Atoms, ≥20% of new artifacts assemble from verified building blocks; cross‑team reuse accelerates learning.

• Auditability at scale: Aim for ≥80% audit coverage (answers trace to sources/tests); aligns with emerging transparency expectations.

• Public‑good infrastructure: Open standards + education democratize trustworthy AI beyond tech majors; the university scales talent and governance.

12‑Month Goals & Metrics

• Verification time ↓ 30–50% in targeted workflows.

• Audit coverage ≥ 80% of answers with receipts/tests.

• Refresh latency ≤ 48h for tracked facts (from change to updated atom/label).

• Reuse ≥ 20% of verified atoms in new artifacts.

Gated Roadmap (Phases A–D)

• Phase A — Prototype & Foundation: KA schema, basic Knowledge Tree, entropy/refresh mechanics; draft curriculum.

  • Gate: claim → Knowledge Atom → Answer Label with confidence/expiry demonstrated.

• Phase B — Pilots: Integrate OSKI in 2–3 real workflows; launch Cohort 1 of the university; refine governance.

  • Gate: documented value (e.g., ~10–40% verification time reduction) and validated policies.

• Phase C — Scale & Ecosystem: Open standards; expand cohorts/partners; formalize governance; offer hosted components where appropriate.

  • Gate: community contributions, multi‑domain adoption, metrics trending to targets.

• Phase D — Consolidation & Impact: Broad deployment; certifications become a talent standard; publish Methods & Benchmarks v1 and public outcomes.

  • Gate: cross‑sector endorsements; sustained KPI performance.

Strategic Differentiation & Defensibility

• Unique symbolic stack: Knowledge Atoms + Knowledge Tree + Answer Labels + Entropy Protocol = trust by design (not bolt‑on oversight).

• Governance baked in: Roles, review, and update policies are part of the substrate and the education pipeline.

• Education flywheel: The AI‑Native University seeds a workforce fluent in symbolic, time‑aware practice—adoption accelerant competitors can’t copy quickly.

• Open standards, operational excellence: Open protocols invite an ecosystem; managed services and stewardship ensure sustainability.

Call to Action — Funders, Pilots, and Partners

• Investors/Donors: Back the category‑defining substrate for shared intelligence—public infrastructure with clear metrics and durable advantage.

• Pilot Partners (policy/compliance, DevX/security, research ops): Turn your docs, repos, and policies into a living Knowledge Tree; ship answers with receipts, confidence, and expiry.

• Academic/Policy Collaborators: Co‑develop curricula, methods, and field maps; help set the standard for transparent, time‑aware AI.

OSCF is building the Shared Intelligence Infrastructure so humans and AI learn together, and every answer is aligned, transparent, and beneficial from first principles.