XBRL-based Digital Financial Reporting

In a LinkedIn post , Animesh Kumar articulated an interesting way to look at LLMs. Paraphrasing, every LLM carries a 𝐥𝐚𝐭𝐞𝐧𝐭 𝐨𝐧𝐭𝐨𝐥𝐨𝐠𝐲: some understanding of business concepts, how those business concepts relate to each other, and other knowledge that it uses to provide you with answers.  That understanding is distilled from millions of documents. Your information platform doesn't need to re-explain all this.  What your information platform needs to explain is the DELTA between what the LLM already gets right and what you consider the perfect answer. Kumar sees this as the "inversion of traditional data governance".  Instead of a governance committee deciding upfront what everything means, you let the AI's misinterpretations surface which concepts actually need formal definition. The area of confusion the LLM experiences is the methodology . This is a clever idea.  This idea also helps you understand how to understand the capabilities of generative artifi...

In another blog post, Structuring Systems into Discrete Stable Units , I described a system of what amounted to information "Lego Bricks".  In this blog post, I want to walk you through what I am talking about. I will use my PROOF working  proof of concept. Here is the machine readable and machine interpretable global standard XBRL. That is XML which machines can easily read and interpret.  That first link is the "instance" or the facts of a report. Here is the model which a machine can get to from the report itself. Here are some of the "conditions" (a.k.a. assertions, restrictions, constraints) such as the accounting equation which the report MUST be consistent with. And here is the underlying base reporting framework with which the report is described and "fenced" or "bounded", the "guardrails" that specifies what the report MUST adhere to. This is the same information in machine readable form. Now this is what a huma...

I remember when I heard that light is both a "wave" and a "particle" (a photon). Wave-particle duality . Well, it is not. Light, per quantum mechanics, is actually a single quantum field. "Wave" and "particle" are not two kinds of light. They are two measurement outcomes of one underlying reality. The field doesn't choose to be one or the other. The measurement apparatus determines what you observe. The field simply is. Now, apply this thinking to a database. So why is a database forced to be SQL, or RDF, or a property graph, or XBRL? Why do we build separate systems for each paradigm, store the same facts in different formats, and then struggle to keep them synchronized? The data doesn't choose to be relational or graph. We choose how to query it. The query paradigm is the measurement apparatus. The substrate should just be. The Problem The root cause is that SQL , RDF , LPG , and XBRL were each designed around a different "atomic...

The result of the past fifty to seventy five years of constructing accounting information systems is craft-based processes where data integrity depends on human discipline, human bucket brigades which move information,  rather than based on good system design.  The question is not whether there are better alternatives; there are.  The question is why the accounting and audit profession has tolerated this current situation for so long, and what it will take to transition to something fundamentally more sound. Imagine being able to click on a link to view the audit evidence knowledge graph which attests to the fairness of a financial statement. Imagine an artificial intelligence (AI) agent or a human being able to interpret that information reliably.  Why imagine it? Why not build it? The Nature of the Problem Gartner points out  (page 2) that the typical Fortune 1000 company uses about 800 electronic spreadsheets to create its compliance report.  Each of t...

Defensible knowledge moats come from tacit, lived, context‑rich human experience that AI can’t copy or automate, making them the deepest and most durable form of expertise advantage. Think about it.  How do humans perform work ?  Humans bring knowledge in the form of  skills, experiences, and judgement to the table to perform that work.  How exactly is artificial intelligence going to perform work without that same knowledge; those same skills, experiences, and judgement that humans need? Maybe magic? Not! A moat is anything that makes it hard for competitors to catch you. Knowledge and experience moats are among the deepest because they can't be bought, copied overnight, or shortcut with capital. A knowledge moat is something you understand better than anyone else.  An experience moat is something you have done more times than anyone else. But what exactly makes a knowledge moat or an experience moat "defensible"? The notion of a moat originated with Warren Buf...

I am still contemplating the enterprise , building an enterprise knowledge graph , contemplating a theory for the enterprise , and connecting the enterprise to what I see as the "skeleton of the enterprise knowledge graph" which is the accounting information systems and compliance reporting . It seems to me that the notion of general endeavor management (GEM) is part of this. Roy Roebuck has created what he refers to as Roebuck General Endeavor Management ( RGEM ). Here is a primer which is available on Amazon.com. Here is a book that explains Roebuck's framework for GEM. General Endeavor Management (GEM) is a theoretical and operational framework designed to model, organize, and execute complex endeavors using unified systems architecture and knowledge representation.  An enterprise is effectively an endeavor. Unlike traditional management models like the Zachman Framework , TOGAF , ArchiMate , EACOE that separate distinct business functions; such as operations, fin...