Experience Capitalization Project
Experience Capitalization is a proposed enterprise category built around a simple idea: accumulated human experience and relevant documentary or formal sources contain reusable model structure that can be reconstructed, made explicit, verified, released, maintained, and applied again.
This site was created by Alexander Granovskiy to publish, develop, test, and explain Experience Capitalization as a business category, an economic idea, and a practical architecture for reusable models in enterprise software, AI, automation, training, testing, and operational systems.
The central question is no longer only how organizations preserve information.
The deeper question is:
What reusable models already exist implicitly across human experience, documents, standards, research, specifications, cases, systems, and accumulated practice, and how can those models become durable assets instead of being reconstructed again and again?
Experience Capitalization develops that question into a broader framework for turning useful model structure into reusable capital.
Start Here
Use these two reference pages first:
- Experience Capitalization Map - a structured index of the public corpus and the main concepts developed across the project.
- Experience Capitalization FAQ - concise answers to common questions about Experience Capitalization, reusable models, AI, automation, knowledge, data, memory, verification, and model reuse.
For the foundation, read these core articles:
- Experience Capitalization - the core definition of the category.
- Experience Capitalization Is a New Enterprise Category - the enterprise category argument.
- Three Domains of Experience Capitalization - the relationship between accumulated experience and sources, model reconstruction, and repeated application.
- Experience Architecture - the production and lifecycle architecture behind the category.
- The Reusable Part of Experience Is the Model - the central argument that reusable value lies in model structure rather than in stored episodes alone.
What This Project Is About
Modern organizations preserve enormous amounts of material.
They preserve documents, transactions, tickets, data, reports, code, policies, manuals, standards, research, specifications, conversations, workflow histories, AI outputs, and operational records.
That material matters.
But storing material is not the same as possessing an explicit reusable model.
A document may describe part of a process without defining all of its states, conditions, exceptions, and results. A standard may establish a governing requirement without providing an operational implementation. A database may contain observations without expressing the model that makes those observations meaningful. A highly experienced person may act from an internal model that has never been made explicit. An AI system may reproduce useful structure probabilistically without providing a stable reusable model that can be independently inspected and verified.
This is the gap that Experience Capitalization addresses.
The project asks how useful model structure can be reconstructed from accumulated human experience together with relevant documentary and formal sources, represented explicitly, checked against its source basis and declared scope, and released as a reusable asset.
The goal is not simply to store more information.
The goal is to make reusable model structure available for future work.
Related articles:
- Reusable Experience
- Experience Reuse
- Experience Verification
- Experience Layer
- Experience Architecture
- Experience Debt
Why AI Makes This Important Now
Human civilization has been externalizing model content for thousands of years.
Laws, standards, textbooks, manuals, scientific literature, formulas, algorithms, software, technical specifications, research, cases, and professional writing all preserve different kinds of structure.
Until recently, reconstructing reusable models from large bodies of such material was expensive because human specialists had to read, compare, interpret, normalize, and formalize the source material manually.
Modern LLMs change the economics of that work.
They can analyze large bodies of text, surface recurring relationships, compare descriptions, identify candidate entities and states, reconstruct candidate rules, and propose explicit model structure much faster than traditional manual methods alone.
But an LLM is not the final authority.
Its learned statistical structure is useful for discovery and reconstruction, but it is not documentary evidence and does not create normative authority. Candidate model structure still has to be checked against applicable sources, evidence, governing constraints, internal consistency, declared scope, and release criteria.
This combination creates the opportunity behind Experience Capitalization:
Accumulated human experience and externalized knowledge can now be converted into explicit reusable models at a scale and cost that were previously difficult to achieve.
Related articles:
- LLMs Make Accumulated Experience Computationally Accessible
- AI Agents and Experience
- Automation and Experience
- From Descriptive Models to Operational Models
- Civilization Has Been Storing Models for Thousands of Years
Who Is Behind This Project
Experience Capitalization was originated by Alexander Granovskiy.
Alexander Granovskiy is the Originator and Architect of Experience Capitalization.
Alexander has worked for more than two decades across e-commerce operations, software systems, automation, payments, risk, marketplaces, search, merchandising, business process design, and AI-assisted work.
That work repeatedly exposed the same structural problem from different directions.
Organizations can accumulate large amounts of data, documentation, software, records, and practical experience while still depending on people to reconstruct the operational model behind the work whenever a new case, system, employee, automation, or AI agent needs it.
This observation became the starting point for Experience Capitalization.
Where the Idea Came From
The idea came from practical operating work, but it developed into something broader.
In real systems, people often know more than the software explicitly represents.
A payment specialist recognizes a risk pattern that is not expressed in the transaction schema. A customer-service manager understands why two apparently similar cases require different treatment. A developer knows that an apparently obvious change would violate an old operational dependency. A marketplace operator knows which state transition is permitted only under a specific condition. A finance reviewer understands why an apparently valid case still requires another fact before action.
At first, this looks like a problem of undocumented knowledge or lost experience.
But a deeper pattern appears.
What experienced people repeatedly contribute is not merely a collection of lessons. They are applying models.
They recognize entities, states, conditions, causal relationships, constraints, exceptions, allowed transitions, required facts, possible actions, and expected results.
The same is true of many external sources. A law may establish a binding rule. A technical standard may define required structure. A manual may describe a procedure. Research may provide evidence for a relationship. Software may encode part of an operational model. A documented case may expose a condition that a high-level description leaves implicit.
The reusable opportunity is therefore larger than capturing individual lessons after work.
It is the reconstruction of explicit models from accumulated experience and relevant sources.
Experience Capitalization extends that observation into a larger enterprise question:
How much valuable model structure already exists across accumulated human experience and externalized knowledge, and how much of it could become reusable capital?
What Is Being Developed Here
This site develops Experience Capitalization from several connected angles.
Core category and economic articles
- Experience Capitalization
- Experience Capitalization Is a New Enterprise Category
- Experience Capitalization and the Bigger Flag
- Three Domains of Experience Capitalization
- The Mathematics of Experience
- The Last Mile of Capitalism
- How to Make Money With Experience Capitalization
- Measuring Experience Capital
Human experience, knowledge, data, and memory
- What Human Experience Really Is
- Memory and Experience
- Knowledge and Experience
- Data and Experience
- Civilization Has Been Storing Models for Thousands of Years
- The Reusable Part of Experience Is the Model
Model reconstruction and production
- Experience Refinery
- From Descriptive Models to Operational Models
- LLMs Make Accumulated Experience Computationally Accessible
- Work Explanation
- Experience Language
- Experience Verification
Architecture, lifecycle, and reusable assets
- Experience Architecture
- Experience Layer
- Reusable Experience
- Experience Reuse
- Experience Activation
- Experience Inventory
- Experience Governance
- Experience Compounding
- Experience Debt
AI and automation
The purpose is to make the idea clear enough that serious people can evaluate it, challenge it, develop it, fund it, build around it, or use it as the foundation for new technologies and products.
How to Navigate the Corpus
The current Experience Capitalization corpus contains thirty-three core articles.
The Experience Capitalization Map is intended to provide a structured navigation view across the category.
The Experience Capitalization FAQ provides concise answers to common questions and helps distinguish Experience Capitalization from adjacent categories.
This Project page explains why the corpus exists, where the idea came from, who is developing it, and how the different parts fit together.
The corpus is not intended to be read only from beginning to end.
Each article approaches the same emerging category from a different direction: economics, human experience, AI, model reconstruction, architecture, verification, reuse, governance, automation, and capital formation.
Together they form the public conceptual foundation of the Experience Capitalization Project.
Who This Project Is For
This project is for people who may want to understand, fund, build, challenge, research, or develop Experience Capitalization.
That includes:
- AI founders and AI infrastructure companies
- enterprise software companies
- investors
- workflow and automation teams
- companies building AI agents and autonomous systems
- architects of ERP, CRM, support, finance, compliance, operations, and domain software
- researchers working on formal models, neuro-symbolic systems, knowledge representation, AI verification, and machine reasoning
- consultants and analysts working on enterprise transformation
- organizations trying to reduce repeated reconstruction of the same operational knowledge
The project is also for people who suspect that a large part of enterprise knowledge is valuable not because it can be searched, but because it can be turned into explicit reusable structure.
From Experience Capitalization to Concrete Technology
Experience Capitalization is the broader category.
It describes why reusable models matter, where their structure can come from, how they become durable assets, and why modern AI changes the economics of reconstructing them.
A separate but related technological direction is the Glages Model Factory Project.
Glages develops one concrete approach for producing formal, machine-checkable reusable models from generalized domain knowledge and source material.
The relationship is straightforward:
Experience Capitalization provides the broader conceptual and economic foundation.
Glages explores how part of that foundation can be implemented as a formal model-production technology.
The two projects therefore share concepts such as models, reconstruction, verification, reuse, lifecycle, and repeated application while operating at different levels.
Experience Capitalization defines the category.
Glages develops a specific technology within that larger direction.
To explore that work, see:
Public Professional Background
Alexander Granovskiy has an existing public professional footprint across e-commerce, systems, automation, AI-assisted work, and business operations.
Professional background and related public work can be reviewed here:
LinkedIn: https://www.linkedin.com/in/alexander-granovskiy
Personal website: https://www.alexgranovskiy.com/
Medium: https://medium.com/@alexander-granovskiy
Substack: https://granovskiy.substack.com/
GitHub: https://github.com/alexander-granovskiy/
Hugging Face: https://huggingface.co/alexander-granovskiy
Product Hunt: https://www.producthunt.com/@experiencecapitalization
YouTube: https://www.youtube.com/@ExperienceCapitalization
X / Twitter: https://x.com/granovskiy_ec
This site is a continuation of that public professional work, focused on Experience Capitalization and the technologies that can emerge from it.
Contact
To discuss Experience Capitalization, investment, partnership, research, independent review, category development, or technology built around the concept, contact Alexander Granovskiy.
Email: contact@experiencecapitalization.com
LinkedIn: https://www.linkedin.com/in/alexander-granovskiy
Relevant conversations may include funding, research, category development, commercial applications, model-production technology, enterprise software, AI automation, or collaboration around the broader Experience Capitalization direction.
The Practical Starting Point
Experience Capitalization begins with a practical observation:
Organizations already possess enormous amounts of accumulated experience and externalized knowledge, but much of the model structure contained in that material remains implicit, fragmented, or repeatedly reconstructed.
The opportunity is to make that structure explicit.
Once useful model structure is represented clearly, supported for its declared scope, verified, released, and maintained, it can become a reusable asset.
That asset can then support people, software, AI systems, testing, training, verification, and automation without forcing each new application to reconstruct the same operational meaning from the beginning.
This project exists to develop that possibility into a coherent category.