Business Model Series — Progress Update (Chapters 1–6 Complete)
#130: From First Principles to Systemic Power in the AI Economy
We just finished Chapter 1 of 6 in the Business Model Series — which means we’ve now built the full foundation for how modern digital business models work, how they compound, and why power increasingly sits in systems (not plans), ecosystems (not pipelines), and flywheels (not funnels).
This post is a collection hub: a single place to navigate everything published so far, in order.
The Arc So Far
Across the first six chapters, we’ve moved through a deliberate sequence:
Chapter 1: The model as a live system (layers + strategy location)
Chapter 2: The shapes of models (archetypes → hybrids → modularity)
Chapter 3: The mechanics of value creation (jobs → experience → multipliers)
Chapter 4: The mechanics of value capture (revenue architecture → pricing → participation)
Chapter 5: The distribution power laws (platforms → aggregators → risks)
Chapter 6: The compounding engine (network effects → flywheels → decay)
If Chapter 1 reframed the business model as an operating system, Chapters 2–6 showed how that OS scales, monetizes, and breaks.
Chapter 1 — Foundations: Business Models as Systems
1.1 — How Modern Business Models Actually Work: A Systems View for 2025
Modern digital firms must be understood as Business Models as Systems, not collections of isolated products, features, or tactics.
1.2 — How Did We Go from Owning Everything to Orchestrating Anything?
The Five-Layer Strategy Stack explains how infrastructure, distribution, interfaces, intelligence, and monetization interact to create durable advantage.
1.3 — Where Does Strategy Live in a Digital Firm’s Stack?
In fast-moving digital markets, Strategy as Architecture replaces static planning with continuous system design and recomposition.
Chapter 2 — Archetypes, Hybrids & Modularity
2.1 — Why Most Successful Business Models in 2025 Combine Multiple Archetypes
In 2025, Hybrid Business Models outperform pure plays by combining multiple value engines into a single compounding system.
2.2 — What Are the Core Shapes of Digital Business Models in 2025?
Modern firms compete by recombining Business Model Archetypes such as subscriptions, platforms, APIs, and AI services.
2.3 — What Does Modularity Mean for Digital Models in 2025?
Model Modularity allows companies to compose, swap, and recombine revenue and distribution layers without breaking the system.
Chapter 3 — The Mechanics of Digital Value Creation
3.1 — Uncover Hidden Customer Needs: Data-Driven JTBD Strategies for 2025
In digital systems, Jobs-to-Be-Done are discovered through behavioral data, not declared through interviews.
3.2 — What Makes a Digital Product Truly Unstoppable?
The Experience Stack explains how products evolve from basic utility to delight, lock-in, and ecosystem value.
3.3 — How Do Digital Products Multiply Their Value Over Time?
Value Multipliers turn usage into compounding advantage through data exhaust, feedback loops, and learning effects.
Chapter 4 — Value Capture & Monetization Design
4.1 — Revenue Architecture: How the Top Digital Businesses of 2025 Capture Value and Scale Growth
Sustainable businesses design a Revenue Architecture that mirrors how value flows through the product ecosystem.
4.2 — Intelligent Pricing: From Static Tags to AI-Driven Strategy
Intelligent Pricing transforms pricing from static tags into adaptive, AI-driven value alignment.
4.3 — Tokenomics, Usage-Based Models, and Soft Paywalls: The Future of the Participation Economy
The Participation Economy replaces rigid paywalls with usage-based pricing, token incentives, and trust-driven conversion.
Chapter 5 — Platforms, Aggregators & Distribution Power
5.1 — What Really Drives Platform Dominance in 2025?
Platform dominance emerges from the Platform Power Loop of network effects, liquidity, governance, and fair value exchange.
5.2 — Platforms, Aggregators & the Power Laws of Distribution
Aggregation Theory shows why controlling demand and interfaces now matters more than owning supply.
5.3 — Platform Risks in 2025: Governance, Data Sovereignty, and the Fragility of AI-Powered Platforms
At scale, Platform Risk emerges from disintermediation, regulatory pressure, and dependency loops.
Chapter 6 — Network Effects & Systemic Flywheels
6.1 — Multidimensional Network Effects: How AI Platforms Really Scale
AI platforms scale through Multidimensional Network Effects that couple users, data, developers, tools, and models.
6.2 — Why Growth in AI Ecosystems Is No Longer Acquired — It’s Engineered
In AI ecosystems, growth is engineered through Growth Flywheels, not acquired through linear funnels.
6.3 — How Scale Can Erode UX, Trust, and Relevance
Network Effects Decay when scale outpaces relevance, trust, and governance.
If you’re building, investing, or advising in digital markets right now, these chapters form a practical lens for diagnosing:
where your strategic control point actually sits,
which archetypes you’re implicitly running (and the risks you’re importing),
how your product generates compounding value (or doesn’t),
whether your monetization is a system or a bolt-on,
what kind of distribution economy you’re really competing inside,
and how to tell if your network is compounding… or quietly decaying.
If you’re new here, start at 1.1 and follow the sequence.
Hit subscribe to get it in your inbox. And if this spoke to you:
➡️ Forward this to a strategy peer who’s feeling the same shift. We’re building a smarter, tech-equipped strategy community—one layer at a time.
Let’s stack it up.
A. Pawlowski | The Strategy Stack
The progression from "model as operating system" to showing howit decays is a useful framework. Most analyses stop at the scaling mechanics and miss that governance layer becoming the choke point once network effects mature. We saw this play out at a SaaS company I consulted for where platform participants started gaming the system faster tahn the governance rules could adapt.