The Problem We're Solving

AI systems today are epistemically brittle. They generate plausible outputs without grounding in verifiable reality. When they fail, no one can explain why. When they succeed, we can't reproduce the result.

This isn't a compute problem or a scale problem. It's an infrastructure problem.

We're building the systems that aren't black boxes—we're missing the semantic substrate that would let AI systems:
- Represent knowledge with verifiable provenance
- Compose across domains without hallucination
- Show their reasoning in auditable chains
- Ground outputs in reproducible computation

Without this foundation, we're building increasingly powerful systems on increasingly unstable ground.

What SIF Does

The Semantic Infrastructure Foundation develops open semantic infrastructure for trustworthy AI:

Semantic Memory

Systems for provenance tracking and verification. Every result carries cryptographic proof of its computational history. You can ask "where did this come from?" and get a complete audit trail.

Universal Semantic Intermediate Representation (USIR)

A common language for cross-domain knowledge. CAD models, scientific simulations, legal reasoning—all expressible in a shared semantic framework that preserves meaning and enables composition.

Deterministic Computation

Engines that eliminate hidden state. Same inputs → same outputs, every time. Reproducible, verifiable, debuggable.

Progressive Disclosure Tools

Human-AI interfaces that scale from structure to detail. Show me the outline, then the specifics I need—don't drown me in tokens or hide everything behind a black box.

Open Standards

No single entity controls the semantic layer. We build in the open, publish our work, and advocate for adoption across the ecosystem.

The Bell Labs Inspiration

SIF aspires to the Bell Labs model: long-term fundamental research protected from quarterly pressures, focused on infrastructure that enables entire industries.

Bell Labs gave us Unix, C, information theory, the transistor—foundational work that took decades and required patient capital. We need something similar for semantic infrastructure.

Current Status: Honest Assessment

What exists today:
- Scott Senkeresty (founder, Chief Architect) — solo researcher with decade-long commitment
- 4 production systems proving the vision (Reveal, Morphogen, TiaCAD, GenesisGraph)
- Extensive strategic documentation (governance, funding strategy, technical architecture)
- Clear roadmap toward Semantic OS and Agent Ether

What doesn't exist yet:
- Filed 501(c)(3) entity (SIF is planned, not operational)
- Funding ($0 revenue, Scott self-funded)
- Team beyond founder (no engineers, advisors identified but not committed)
- Lab space or operational foundation

Why publish now?

Because the work matters and transparency matters more. We're building systems that work, documenting architecture in public, and inviting collaboration from those who believe this problem is worth solving.

The vision is ambitious. The current reality is small. But the trajectory is clear, and the need is urgent.

Who Is Scott Senkeresty?

Chief Architect, SIF

Scott has spent the last several years building the production systems that prove semantic infrastructure is possible. Before SIF:
- Software engineer and architect focused on distributed systems
- Decades of experience building production tools and infrastructure
- Deep background in parametric design, computational geometry, and knowledge representation

His approach: Build working systems first. Prove the concepts in production. Document the patterns. Then scale.

The four production systems (Reveal, Morphogen, TiaCAD, GenesisGraph) represent thousands of hours of solo development work—not demos, but tools passing comprehensive test suites and used in real workflows.

The Vision: 10-Year Horizon

Year 1-2: Establish foundation, secure initial funding, form core team
Year 3-5: Deploy semantic infrastructure in research institutions, build ecosystem
Year 5-10: Semantic OS becomes standard layer, Timeline B momentum builds

Success metrics:
- Major AI labs adopt USIR as common IR layer
- Regulators mandate provenance for high-stakes decisions
- Scientific replication rates improve 20%+
- Multi-agent systems run critical infrastructure reliably

Honest probability:
- 20% chance: Bell Labs-tier success (transform the ecosystem)
- 65% chance: Meaningful impact (advance the state of art, influence standards)
- 15% chance: Fail to achieve critical mass

We think those odds justify the effort.

Governance & Funding

Governance model: Inspired by Wikimedia Foundation, Mozilla Foundation, and Linux Foundation—balance between mission focus and operational pragmatism.

Funding strategy: Hybrid model avoiding capture. Max 10% from any single source. Revenue from grants, enterprise support contracts, and philanthropic funding.

Anti-capture protections: Technical decisions made by research leads, not funders. Source code published under permissive licenses. Standards development in the open.

See our funding strategy documentation for details.

Why Now?

The window for foundational work is closing. As AI capabilities surge, the brittleness compounds. The earlier we build semantic infrastructure, the less painful the transition.

In 5 years, either:
- We've built the substrate and AI systems are becoming more trustworthy, or
- We're drowning in hallucinations, accepting opaque governance, and living in the Grey Fog

SIF exists to make Timeline B possible.

Get Involved

We're early-stage, but we're building in the open.

This is the beginning. The work is long, the need is urgent, and transparency is non-negotiable.

Let's build Timeline B together.