PRESS RELEASE / PRESS KIT INTRODUCTION
For Immediate Release
MSP-1 Introduces a Foundational, No-Hype Protocol for AI Understanding in Real-World Systems
New Haven, CT, January 25, 2026 — As artificial intelligence systems become embedded across industries, a practical challenge has become increasingly visible: modern AI systems are often required to interpret content without explicit knowledge of its intent, provenance, or interpretive boundaries.
In the absence of clear declarations, systems are forced to infer meaning heuristically — increasing variability, cost, and the likelihood of misinterpretation.
MSP-1 (Mark Semantic Protocol, named for its markup-based implementation) is introduced today as a foundational response to this condition. Implemented through simple JSON-LD markup that integrates seamlessly with existing web infrastructure, MSP-1 is a lightweight, declarative protocol designed to allow websites to state intent, provenance, and interpretive context directly to AI systems — clearly, conservatively, and without enforcement or coercion
At its core, MSP-1 exists to reduce unnecessary inference.
A Pragmatic Philosophy for AI Interpretation
For much of the web’s history, systems have relied on indirect signals to infer meaning. This approach evolved organically and worked well enough when ambiguity carried limited cost.
In real-world AI systems, that assumption no longer holds.
Inference now carries measurable consequences — including computational overhead, energy use, citation inconsistency, and downstream decision risk. As AI systems move from passive retrieval toward active summarization, recommendation, and action, the cost of misinterpretation increases accordingly.
MSP-1 addresses this not by adding intelligence, but by removing guesswork.
It enables publishers to declare:
- why content exists
- how it should be interpreted
- where it comes from
- and what scope those declarations apply to
These signals are explicit, scope-bound, and intentionally conservative.
Foundational by Design
MSP-1 is designed as infrastructure.
It provides a declarative baseline for intent and interpretation, without prescribing outcomes, enforcing behavior, or introducing incentives.
It establishes a stable semantic baseline — a shared reference point that AI systems may use to reduce ambiguity when interpreting human-authored content.
This restraint is deliberate.
By remaining narrowly scoped, backward-compatible, and independent of business incentives, MSP-1 is designed to integrate cleanly with existing web practices while remaining resilient to shifts in AI models, platforms, and economic conditions.
Practical Implications Across Systems
The effects of interpretive ambiguity extend beyond AI performance.
- Operational efficiency: Clear declarations reduce redundant inference and fallback processing.
- Environmental impact: Lower inference overhead translates directly into reduced energy consumption at scale.
- Trust and attribution: Explicit provenance and framing reduce mis-citation and unintended reuse.
- Economic clarity: Deterministic signals lower uncertainty in automated pipelines and downstream decisions.
MSP-1 does not claim to solve these challenges on its own. It addresses a shared upstream cause: the lack of a simple, truthful way to declare meaning to machines.
A Neutral Approach
MSP-1 is offered as open, neutral infrastructure.
Adoption is voluntary.
Absence is neutral.
Presence is declarative.
The protocol does not seek exclusivity or dominance. Its purpose is to make clarity easier to express and easier to recognize — for both humans and machines.
In an environment increasingly shaped by automated interpretation, MSP-1 represents a measured step toward a more interpretable, efficient, and trustworthy web — not by amplifying claims, but by stating them plainly.
About MSP-1
MSP-1 (Mark Semantic Protocol) is an open, machine-first semantic protocol implemented via JSON-LD markup. It enables websites to declare intent, provenance, and interpretive context for AI systems through simple, structured data that integrates with existing web standards. MSP-1 is independent of ranking systems, platforms, and enforcement mechanisms, and is designed to remain conservative, stable, and extensible over time