Vision: Laymanization of Systems and Economies

Vision: Laymanization of Systems and Economies

Systems

Going back to the 19th century, we started with energy systems, e.g. a car transforms chemical energy (fuel) into mechanical energy (motion). We then moved on to information systems, and now, we are transitioning to intelligence systems.

Laymanization

Upon meeting certain performance, cost and user criteria, a technology is laymanized (usable without expertise, affordable, and personalized), e.g. Ford’s Model T (1913), IBM’s PC (1981), and very recently, OpenAI’s ChatGPT (2022).

Infrastructure

Subsequently, more users translate to a higher demand for infrastructure, which in turn further laymanizes the product, e.g. inter-state highways, global Internet, and surely next, ValereNet* (a network where value flows with little to no friction).

Reactions, Interactions, and Transactions

In parallel, the layman MVP acts as a blueprint to further expand into systems focused on other reactions (e.g. chemical (coal) to electrical (electricity) energy), and interactions (e.g. transforming information across manufacturing, financial and insurance sectors). We believe history will repeat itself, and we will soon start seeing laymanized MVPs from intelligence systems focused on other transactions, e.g. robots and physical intelligence, or scibots and scientific intelligence.

Execution: Funding Transactions for Entrepreneurs

Funding Intelligence System

In this context, we’re building an AI-native, full-stack intelligence system, focusing on funding transactions (our MVP) for entrepreneurs (our ICP, including sole props and SMBs), initially targeting friends & family funding (our beachhead, including self-funding). Next, we will expand into other debt & equity funding (e.g. bank and SBA loans), as well as sales (e.g. M&A, asset sales) and estate (e.g. succession, inheritance) transactions. Our mission is to establish the entrepreneur ecosystem as a new asset class.

Research

Our research is under three themes: adaptive agency (foundation models & agents), laymanization (systems & applications), and intelligence theory (models & metrics).

Development

Our platform employs an architecture similar to the OSI model, including layers such as an “adaptive” Funding Operating System (FOS), Funding Foundation Model (FFM), and bots & agents specialized for various users (e.g. entrepreneurs, investors, partners, third parties), services (e.g. institutionalization, monetization, templatization, tax strategies), intelligence products (e.g. securitization, derivatization, intelligence engineering), and commercial functions (e.g. sales, marketing, pricing).

Commercialization

Our monetization strategy for our platform is two-fold: one based on transaction-based products, and the other on intelligence-based products, with each track targeting both layman users (B2E: entrepreneurs and investors) and institutional users (B2B2E: partners and third parties).