This 100-page deck and 30-minute video dissect the inherent problem with global growth loops, from consumers to enterprises and back again. We develop two products in the process: an AI decision engine and an AI paywall backed by Bitcoin Lightning.
This 100-page deck and 30-minute video dissect the inherent problem with global growth loops, from consumers to enterprises and back again. We develop two products in the process: an AI decision engine and an AI paywall backed by Bitcoin Lightning.
Overview
We present a comprehensive overview of Bitcoin's product growth strategy, focusing on AI paywalls and the transition from strategy to product development, while discussing key concepts like atomic decision units and Bitcoin Lightning. We explore the challenges faced by enterprise SaaS applications, emphasizing the need for standardized APIs to enable effective AI agent communication and highlighting the vast market opportunity presented by connecting AI with enterprise software. The discussion concludes with insights into the evolving relationship between consumers, enterprise software, and AI agents, including a proposed future system of paywalls and transactions managed through Bitcoin wallets, along with considerations for ensuring AI decision-making trustworthiness.
Bitcoin AI Paywalls Strategy Update
We present a follow-up on the product growth strategy for Bitcoin, focusing on AI paywalls and transitioning from strategy to product development. He discussed the current state of spatial computing, AI advancements, and Bitcoin's role as a digital currency storage system. We define key terms, including Atomic Decision Units and Bitcoin Lightning, highlighting its advantages for low-cost, high-throughput transactions compared to traditional payment methods.
Standardizing APIs for Enterprise SaaS
We discuss the challenges faced by enterprise SaaS applications, highlighting issues such as complex user interfaces, outdated application logic, and varied API structures that hinder integration with other systems. We emphasize the need to standardize APIs to enable AI agents to communicate effectively across different platforms, aiming to reduce decision-making, save time, and lower costs for both humans and SaaS companies. We also touch on the vast market opportunity, noting the presence of 30,000 SaaS companies and over half a billion users, and suggest that connecting these two domains could lead to increased profitable growth for enterprise SaaS.
AI Integration and Economic Challenges
We discuss the challenges of integrating AI agents with enterprise SaaS applications, highlighting the need for a secure, authenticated, and monetizable gateway to facilitate interactions. We emphasize the scale of the problem, comparing it to zeta-scale phenomena like water molecules and Bitcoin network hash power, and note that machine use will significantly surpass human use. Sean also explored the fundamental growth issues, identifying that the primary blocker lies with consumers who are facing eroded earnings and increased prices, making it difficult to balance the global economy.
AI Agents: A New Economic Model
We discuss the evolving relationship between consumers, enterprise software, and AI agents. He explained how consumers currently pay for enterprise software to improve efficiency and manage data, while enterprise software companies pay consumers to build software. With the emergence of AI agents, we propose a new model where consumers would pay to use AI agents, which in turn would pay for enterprise software and handle more complex tasks. He emphasized the need to differentiate between consumer and enterprise AI agents, as they serve different purposes and have varying capabilities, security protocols, and privacy considerations.
AI Payment System Protocol Development
We talk through a potential future where AI agents and consumers interact through a system of paywalls and transactions, with microscopic payments managed through a Bitcoin wallet and an MP server. We outlined a workflow involving API access, payment verification, and data access, emphasizing the need for a standardized protocol. We then highlight the potential for growth through better task handling, consistent decisions, private workflows, and new business models for enterprise SaaS, while noting that this system could benefit from a dedicated product to streamline implementation.
Trustworthy AI Decision Engine
Finally, we raise the challenges of ensuring AI decision-making trustworthiness, particularly in the context of hallucinations and unpredictable outcomes. We propose a deterministic decision engine that uses six key inputs to produce a single output, forming what he calls an "atomic decision unit." We then explain that this system can be inspected, edited, and improved over time, with the ability to track and understand decision-making processes. We invite feedback and collaboration, offering to share more information.
