PM-KR Community Group: Procedural KR for Sustainable AI Infrastructure

Hi Tzviya,

I'm reaching out from the Procedural Memory Knowledge Representation 
(PM-KR <https://www.w3.org/community/pm-kr/>) Community Group, founded 
last week at W3C. Our work on lightweight procedural reasoning has 
significant implications for digital sustainability, and I wanted to 
connect our mission with the Web Sustainability Guidelines you're 
developing.

The Carbon Challenge in AI:

Current AI systems have massive carbon footprints:

  * Large language models: 175B+ parameters, gigawatt-scale training

  * Video processing: computationally expensive per-frame analysis

  * Robotics: inefficient cloud-based inference loops

PM-KR's Procedural Approach:

Our Knowledge 3D (K3D) architecture uses procedural knowledge 
representation instead of declarative data duplication, achieving:

Performance:

  * 7M parameters (vs 175B in typical LLMs) — 25,000× smaller

  * <100µs latency (vs 142.5s current SOTA) — 1,425,000× faster

  * 200:1 to 1000:1 compression via procedural programs vs redundant data

Carbon Impact (10-year projection 
<https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/CARBON_BLUEPRINT_10_YEAR_PROJECTION.md>):

  * 12.826 Gt CO₂e cumulative savings (equivalent to removing 550M cars)

  * 2.565 Gt CO₂e/year by 2035 (6.9% of global emissions)

  * Video streaming: 53.85 Mt CO₂e/year saved via procedural compression

  * Robotics: 2.4 Gt CO₂e/year enabled through AI-optimal edge deployment

Alignment with Web Sustainability Guidelines:

PM-KR's procedural foundation directly supports WSG principles:

  * Reduce data transfer: Procedural programs (KB) vs embeddings (GB)

  * Optimize compute: CPU/GPU/native RPN

  * Enable edge deployment: Lightweight 7M-param models run on NPUs/mobile

  * Eliminate redundancy: Single source of truth, symlink-style references

Collaboration Opportunity:

Would you be open to exploring how PM-KR's technical approach could 
inform WSG's recommendations for sustainable AI infrastructure? We're 
particularly interested in:

 1. Web AI sustainability metrics (carbon cost per inference,
    compression ratios)
 2. Procedural standards for reducing AI data redundancy
 3. Cross-CG collaboration (PM-KR + Sustainable Web IG)

Our Community Group is moving fast — we're targeting 6-12 month CG → WG 
progression using AI-partnered specification development 
<https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/multi_vibe_orchestration>. 
Having sustainability baked into our data model from day 1 would set a 
strong precedent.

Next Steps:

If this resonates, I'd love to:

  * Share our Phase 1 Data Model Specification (draft in progress, ~15%
    complete)

  * Discuss carbon impact methodology (see
    CARBON_BLUEPRINT_10_YEAR_PROJECTION.md)

  * Explore joint deliverables (e.g., WSG case study on procedural KR)

PM-KR Context:

Founded: February 24, 2026
Co-Chairs: Daniel Ramos (EchoSystems AI Studios), Milton Ponson (Rainbow 
Warriors Core Foundation)
Members: Christopher Allen (Blockchain Commons), Blockchain Commons, 
Stream44.Studio
Charter: PM-KR Charter
Mailing list: public-pm-kr@w3.org
Looking forward to connecting sustainable web standards with procedural 
knowledge representation!

Best regards,
Daniel Ramos
Co-Chair, PM-KR Community Group
EchoSystems AI Studios