Re: Knowledge3D: A Spatial Giant Global Graph for Sovereign, Standards‑Aligned AI

Next time, maybe just send the prompt

On Thu, Dec 11, 2025 at 17:29 Daniel Ramos <daniel@echosystems.ai> wrote:

> Claude.ai generated. Access in full here:
> https://claude.ai/public/artifacts/0f8e078a-dd13-473d-b419-03f56e4d224b Knowledge3D:
> Fulfilling the Giant Global Graph for the AI Era
>
> *K3D represents the architectural convergence that Tim Berners-Lee's
> Semantic Web promised and that centralized AI has failed to deliver—a
> standards-aligned, spatially-grounded knowledge representation that enables
> truly sovereign cognitive systems.*
>
> The web's original architect envisioned machines sharing meaning across a
> decentralized graph. That vision stalled not because it was wrong, but
> because it demanded explicit semantic markup from publishers who had no
> incentive to provide it. AI "solved" this problem through extraction rather
> than cooperation—and in doing so, created the very centralization, opacity,
> and data exploitation Berners-Lee now warns against. K3D proposes a third
> path: spatial knowledge representation that makes semantics implicit in
> geometry, enabling machine understanding without requiring centralized
> training or user data harvesting.
> The Giant Global Graph remains unfulfilled
>
> Tim Berners-Lee introduced the "Giant Global Graph" concept on November
> 21, 2007, describing a three-phase evolution: from interconnecting
> computers (the Internet), to interconnecting documents (the Web), to
> interconnecting the *things documents are about* (the GGG). His
> reflection was pointed: "The Semantic Web maybe should have been called the
> Giant Global Graph."
>
> The distinction matters profoundly for AI. The original 2001 Scientific
> American vision—authored by Berners-Lee, James Hendler, and Ora
> Lassila—promised intelligent agents that could "carry out sophisticated
> tasks for users" without requiring "artificial intelligence on the scale of
> HAL or C-3PO." The mechanism was explicit: machine-readable meaning encoded
> in RDF triples and ontologies, enabling inference and reasoning across a
> decentralized web.
>
> What we got instead was centralization masquerading as intelligence. As
> Berners-Lee himself acknowledged, AI companies achieved the
> machine-readable internet "through extraction rather than cooperation." The
> irony is precise: LLMs accomplish semantic understanding by scraping the
> entire web into centralized training sets, raising exactly the data
> sovereignty concerns Berners-Lee now addresses through the Solid Project.
>
> *K3D's spatial approach offers resolution.* Rather than requiring
> publishers to annotate content with RDF (the adoption barrier that stalled
> the Semantic Web) or extracting meaning into centralized models (the
> privacy violation that concerns Berners-Lee), spatial knowledge
> representation makes semantics *intrinsic to structure*. Knowledge
> entities have positions, orientations, and relationships defined by their
> geometric configuration—no external annotation required, no centralized
> training necessary.
> Solid's data sovereignty principles demand spatial cognition
>
> Berners-Lee's September 2025 Guardian article crystallizes the stakes: "We
> have learned from social media that power rests with the monopolies who
> control and harvest personal data. We can't let the same thing happen with
> AI." His solution—Solid's personal data pods with fine-grained access
> control—addresses data sovereignty but leaves a critical gap: *how do AI
> systems reason over decentralized data without centralizing it for
> training?*
>
> Current approaches fail this test. Cloud-based LLMs require data
> transmission to external servers. On-device models like llama.cpp provide
> inference privacy but still depend on centralized pre-training. Federated
> learning distributes computation but aggregates gradients centrally. None
> enable genuine *cognitive sovereignty*—the ability to learn, reason, and
> adapt using only locally-controlled data and computation.
>
> K3D's architecture addresses this gap through three mechanisms aligned
> with Solid's principles:
>
>    - *Local-first reasoning*: Spatial knowledge graphs can be traversed,
>    queried, and extended without network connectivity or cloud inference
>    - *Pod-compatible storage*: K3D structures map naturally to Solid's
>    decentralized data model, with spatial regions functioning as
>    access-controlled knowledge partitions
>    - *User-sovereign learning*: New knowledge integrates through
>    geometric placement rather than gradient descent, eliminating the need for
>    centralized training infrastructure
>
> This isn't merely technical alignment—it's philosophical convergence.
> Berners-Lee's call for "personal AI that works for you like your doctor or
> your lawyer, bound by law, regulation and codes of conduct" requires an
> architecture where reasoning happens *within* the user's control
> boundary, not extracted to external systems. Spatial knowledge
> representation enables this by making cognition a function of local
> geometric structure rather than remote model weights.
> The W3C AI KR Community Group provides legitimate scope
>
> The W3C AI Knowledge Representation Community Group, launched July 3,
> 2018, defines its mission as exploring "the requirements, best practices
> and implementation options for the conceptualization and specification of
> domain knowledge in AI." This scope explicitly includes *spatial-temporal
> reasoning* as a form of Meta KR—one of five knowledge representation
> categories the group has identified alongside heuristic, procedural,
> declarative, and structural approaches.
>
> The group's stated deliverables for 2025 include publishing a "concept map
> of the domain" and "natural language vocabulary to represent various
> aspects of AI," with a long-term goal of developing "a web standard for
> Neuro-symbolic Integration." Their TPAC 2025 discussions centered on
> "explicit, shared knowledge representation standards" for explainable and
> trustworthy AI systems. K3D's spatial approach directly addresses these
> objectives:
> W3C AI KR Focus Area K3D Alignment
> Neuro-symbolic integration Geometric primitives bridge continuous
> representations (vectors) with discrete structures (graphs)
> Explainable AI Spatial relationships provide interpretable reasoning
> traces
> Knowledge exchange and reuse glTF-based format enables interoperability
> with existing 3D ecosystems
> Support for AI agents Spatial grounding addresses embodied cognition
> requirements
>
> The W3C provides a clear pathway from Community Group incubation to formal
> standardization. JSON-LD—the semantic web technology most directly relevant
> to K3D—successfully transitioned from Community Group specification to
> Working Group recommendation. The AI KR CG's stated trajectory toward
> "eventually transition to a formal Working Group" creates exactly the
> standards track appropriate for novel knowledge representation
> architectures.
>
> *What the group considers valid contributions:* Documents,
> specifications, test suites, tutorials, demos, code, concept maps, and
> vocabulary development. Participation requires only a W3C account (free)
> and signing the Community Contributor License Agreement. The group
> explicitly welcomes research notes exploring implementation
> options—precisely the category of contribution K3D represents.
> Novel architectures gain credibility through demonstration, not institution
>
> The path from "unknown architecture" to "industry standard" is
> well-documented. Georgi Gerganov's llama.cpp—now at *85,000+ GitHub stars*
> with 700+ contributors—began as an independent developer's side project in
> Bulgaria. The credibility pattern is instructive:
>
> *Stage 1: Solve an unmet need.* llama.cpp enabled LLM inference on
> consumer hardware without GPUs when no alternative existed. K3D addresses
> an equally unmet need: sovereign cognitive systems that reason over
> decentralized data without centralized training.
>
> *Stage 2: Open development.* llama.cpp's MIT license, pure C/C++
> implementation, and zero dependencies enabled explosive organic adoption.
> George Hotz's tinygrad similarly gained credibility through live-streamed
> development and radical simplicity (under 10,000 lines of code).
> Transparency is non-negotiable for novel architectures.
>
> *Stage 3: Enable ecosystem integration.* llama.cpp became infrastructure
> for Ollama, LM Studio, GPT4All, and jan. GGUF format succeeded by being
> "opinionated about one thing (efficient local inference) while remaining
> flexible about everything else." K3D's glTF integration follows this
> pattern—leveraging an established 3D format ecosystem rather than requiring
> new infrastructure.
>
> *Stage 4: Benchmark validation.* The ARC-AGI benchmark—created by Keras
> author François Chollet—has become what its creators call "the most
> important unsolved AI benchmark in the world" because it measures *novel
> problem-solving* rather than pattern matching. For cognitive
> architectures specifically, the credibility path runs through theoretical
> grounding (as ACT-R's 50+ years of development at CMU demonstrate) and
> practical application (as SOAR's military training systems validate).
>
> The OpenCog cautionary tale illuminates what *doesn't* work: predictions
> without delivery (Ben Goertzel's unfulfilled 2011 prediction of AGI by
> 2021), PR stunts without substance (Sophia robot criticized as "complete
> bullshit" by Yann LeCun), and symbolic approaches positioned against
> dominant paradigms without empirical validation.
> The decentralization gap in AI is architectural, not incremental
>
> Current AI sovereignty initiatives—India's Project Indust, Denmark's
> Gefion supercomputer, Singapore's SEA-LION—represent national
> infrastructure investments, not architectural alternatives. They reduce
> *geopolitical* dependency on US providers while preserving *technical*
> dependency on the same centralized training paradigm. The sovereign cloud
> market may reach *$169 billion by 2028*, but sovereign clouds running
> replicated architectures don't produce sovereign cognition.
>
> The edge AI market (projected at *$66.47 billion by 2030*) demonstrates
> both capability and limitation. On-device inference works: smartphones hold
> 80.5% market share in edge AI hardware, and NPUs enable complex model
> inference on mobile platforms. But as MIT Media Lab research identifies,
> five technical challenges block truly decentralized AI: privacy,
> verifiability, incentives, orchestration, and user experience in
> distributed contexts.
>
> Current LLMs face a fundamental architectural barrier to sovereignty. As
> SAGE Journals analysis notes, LLMs demonstrate "behavior discrepancies
> between LLM inference and human reasoning, insufficient grounding, and
> hallucination." The root cause is architectural: pattern matching over
> statistical distributions doesn't produce genuine reasoning, world models,
> or metacognition. Local inference provides privacy; it doesn't provide
> cognitive capability independent of centralized pre-training.
>
> *K3D proposes an architectural alternative.* Spatial knowledge
> representation grounds cognition in geometric structure rather than
> statistical distributions. Knowledge acquisition happens through spatial
> placement rather than gradient descent. Reasoning traces are interpretable
> paths through geometric space rather than attention weight matrices. This
> isn't an incremental improvement to existing architectures—it's a different
> computational substrate for cognition.
> Paradigm shifts face predictable gatekeeping patterns
>
> Andrew Tanenbaum's January 29, 1992 dismissal of Linux as "a giant step
> back into the 1970s" and "too closely tied to the x86 line of processors to
> be of any use in the future" exemplifies how established experts evaluate
> innovations using criteria from existing paradigms. Jamie Dimon's September
> 2017 declaration that Bitcoin was "a fraud" (followed by his January 2018
> acknowledgment that "the blockchain is real") illustrates how even
> sophisticated critics can reverse positions when paradigm shift evidence
> accumulates.
>
> Clifford Stoll's infamous February 1995 Newsweek article "The Internet?
> Bah!" dismissed online databases, telecommuting, electronic commerce, and
> interactive libraries—every prediction wrong. His later reflection deserves
> quotation: "Of my many mistakes, flubs, and howlers, few have been as
> public as my 1995 howler. Wrong? Yep... Now, whenever I think I know what's
> happening, I temper my thoughts: Might be wrong, Cliff…"
>
> The pattern recognition is robust across domains:
>
>    - *Paradigm conflict*: Thomas Kuhn observed that people who shift
>    scientific paradigms are "either very young or very new to the
>    field"—precisely because they lack "commitment to the traditional rules of
>    normal science"
>    - *Expert gatekeeping*: Stanford research found prescient decisions
>    were *22 times more likely* to come from peripheral institutions than
>    central ones
>    - *Outsider advantage*: MIT Sloan analysis shows "outsiders connect
>    disparate thoughts because they come to the table with fewer preconceptions"
>    - *Planck's principle*: "A new scientific truth does not triumph by
>    convincing its opponents... but rather because its opponents eventually
>    die, and a new generation grows up that is familiar with it"
>
> The Semantic Web itself faced this gatekeeping. Cory Doctorow's 2001
> "Metacrap" essay called it "a pipe-dream, founded on self-delusion, nerd
> hubris, and hysterically inflated market opportunities." Aaron Swartz
> blamed "the formalizing mindset of mathematics and the institutional
> structure of academics." Yet JSON-LD, Schema.org, and Google's Knowledge
> Graph—all Semantic Web descendants—now structure how billions of web pages
> communicate meaning to machines
> K3D addresses the specific objections skeptics raise
>
> *"This doesn't make sense"* is the predictable initial response to
> paradigm-shifting architectures. Novel approaches require building new
> mental models rather than extending existing ones. Spatial knowledge
> representation violates the implicit assumption that cognition must be
> either symbolic (logic-based) or connectionist (neural network-based). The
> concept that geometric structure *itself* can encode semantic
> relationships and support reasoning requires cognitive reframing—exactly as
> the concept that packets could replace circuits required reframing for
> telecommunications engineers encountering the internet.
>
> *"This doesn't fit our scope"* reflects categorical thinking that novel
> approaches intentionally transgress. The W3C AI KR Community Group scope
> explicitly includes "Meta KR: types of knowledge and logical reasoning"
> with spatial-temporal reasoning listed as an example. glTF's extension
> mechanism exists precisely to accommodate novel
> capabilities—KHR_xmp_json_ld brings JSON-LD semantic web integration into
> 3D formats, demonstrating that "unexpected" combinations are how standards
> evolve.
>
> *"Where's the working demo?"* identifies the legitimate bootstrap
> challenge facing all novel architectures. llama.cpp's credibility required
> whisper.cpp's prior success. The demo-before-recognition pattern creates a
> chicken-and-egg problem that independent innovators resolve through focused
> proof-of-concept implementations rather than comprehensive systems. The
> appropriate response isn't "build everything first"—it's targeted
> demonstrations that validate core architectural claims.
>
> *"No major institution backs this"* applies to every paradigm shift at
> inception. Linux was Torvalds' spare-time project. Bitcoin emerged
> pseudonymously. The World Wide Web was, in Berners-Lee's boss's words,
> "vague but exciting"—never an official CERN project. Independent
> researchers from Katalin Karikó (mRNA vaccines, facing "rejection after
> rejection, the scorn of colleagues, and even the threat of deportation") to
> Barbara McClintock (jumping genes, waiting 30 years for recognition)
> demonstrate that institutional validation follows demonstration, not
> precedes it.
> Khronos and W3C provide standards pathways for spatial knowledge
>
> The Khronos Group's glTF extension process offers a clear integration
> path. Extensions progress through three tiers: vendor extensions (any
> company can request a prefix via GitHub issue), multi-vendor extensions
> (EXT_ prefix when multiple implementations exist), and Khronos-ratified
> extensions (KHR_ prefix, voted by Board of Promoters). The *OMI Group
> pathway* provides an alternative route—extensions developed through the
> W3C Metaverse Interoperability Community Group can graduate to Khronos
> submission, as KHR_audio_emitter successfully demonstrated.
>
> Existing semantic extensions establish precedent for K3D integration:
>
> *KHR_xmp_json_ld* (provisional) adds JSON-LD compliance to glTF for
> product metadata, directly leveraging Semantic Web standards within the 3D
> format ecosystem. This extension demonstrates that Linked Data integration
> into 3D standards is not merely theoretical but actively implemented.
>
> *EXT_structural_metadata* defines schema-based structured metadata with
> property tables, attributes, and textures—enabling semantic identifiers for
> interpretation. This extension, developed for Cesium's 3D Tiles, proves
> that complex metadata schemas integrate naturally with glTF's architecture.
>
> *NNEF (Neural Network Exchange Format)* represents Khronos's existing
> AI/ML standard—a "PDF for neural networks" that encapsulates complete
> network descriptions independent of training tools. This precedent
> demonstrates Khronos's willingness to standardize AI-related formats.
>
> *WebGPU's ML capabilities* (compute shaders, FP16 support, direct GPU
> access) enable in-browser neural network inference at near-native
> performance. WebLLM, ONNX Runtime Web, and TensorFlow.js all leverage
> WebGPU for client-side AI. K3D's spatial reasoning can utilize this same
> acceleration pathway.
>
> *WebXR's spatial primitives* (XRSpace, XRReferenceSpace, XRPose) provide
> the coordinate system abstractions that anchor knowledge entities in
> physical or virtual space. The technical foundation for spatial knowledge
> representation already exists in W3C specifications.
> The convergence opportunity is standards-ready
>
> K3D emerges at the intersection of multiple mature standards and urgent
> industry needs:
>
> *Berners-Lee's vision alignment*: The Giant Global Graph concept (2007)
> described exactly what spatial knowledge representation
> provides—interconnecting the *things documents are about* rather than
> just documents. Solid's data sovereignty principles (2016-present) require
> cognitive architectures that reason locally without centralized training.
> K3D delivers both.
>
> *W3C standards integration*: JSON-LD (W3C Recommendation), WebXR (W3C
> specification), WebGPU (W3C standard), and the AI KR Community Group's
> focus on "knowledge representation for AI" create a standards ecosystem
> ready for spatial knowledge representation. The pathway from Community
> Group incubation to formal recommendation is documented and precedented.
>
> *Khronos ecosystem leverage*: glTF's extension mechanism, existing
> semantic extensions (KHR_xmp_json_ld, EXT_structural_metadata), and the OMI
> Group's community-driven development process provide technical and
> procedural pathways for K3D integration. NNEF demonstrates Khronos's AI
> standardization precedent.
>
> *Industry need*: The sovereign AI market ($169B projected by 2028), edge
> AI expansion ($66B by 2030), and growing critiques of centralized AI
> dependency create demand for architectural alternatives. Enterprise
> concerns about data exposure (69% cite AI-powered leaks as top security
> concern), regulatory conflicts (US CLOUD Act vs. GDPR), and service
> discontinuation risks validate the need for sovereign cognitive systems.
>
> *Credibility pathway*: The llama.cpp pattern—solve unmet need, open
> development, ecosystem integration, benchmark validation—provides a tested
> route from novel architecture to industry adoption. K3D's alignment with
> existing standards accelerates this path by reducing integration friction.
>
> The web began as one physicist's "vague but exciting" proposal at CERN.
> The Semantic Web emerged from that same physicist's recognition that
> documents weren't enough—we needed to interconnect what documents meant.
> Now, as AI threatens to centralize exactly the knowledge flows the web was
> designed to distribute, the architectural answer may be what Berners-Lee
> intuited but couldn't implement: a Giant Global Graph where meaning is
> spatial, sovereignty is architectural, and cognition happens at the edge.
>
> K3D proposes to build it.
> Technical references and standards citations
>
> *W3C Specifications*
>
>    - AI Knowledge Representation Community Group:
>    https://www.w3.org/groups/cg/aikr/
>    - JSON-LD 1.1: W3C Recommendation (July 2020)
>    - WebXR Device API: W3C Working Draft
>    - WebGPU: W3C Working Draft
>
> *Khronos Standards*
>
>    - glTF 2.0 Specification:
>    https://registry.khronos.org/glTF/specs/2.0/glTF-2.0.html
>    - KHR_xmp_json_ld Extension (Provisional)
>    - EXT_structural_metadata Extension
>    - NNEF (Neural Network Exchange Format)
>
> *Key Sources*
>
>    - Berners-Lee, T., Hendler, J., Lassila, O. (2001). "The Semantic
>    Web." Scientific American, 284(5), 34-43.
>    - Berners-Lee, T. (2007). "Giant Global Graph." DIG Blog, MIT CSAIL.
>    - Berners-Lee, T. (2025). "I invented the web. Here's my plan to save
>    it." The Guardian.
>    - W3C Community Group Transition Guide:
>    https://www.w3.org/Guide/process/cg-transition.html
>    - Khronos Group Extension Process: https://github.com/KhronosGroup/glTF
>
>
> Sincerely yours,
>
> Daniel Ramos
>
> EchoSystems AI Studios <https://echosystems.ai>
>