Good Stuff Podcast 4 months ago

# AI Business Strategy: Build vs Buy Decision Framework **Hosts:** Pete and Andy (virtually at the beach with their new cinematic backdrop)

## Core Topic Strategic decision-making in the AI era: whether to build new AI-native businesses or acquire and transform existing ones, examining capital allocation strategies and transformation approaches for different industry contexts. ## Two Primary Strategies ### Build Strategy: AI-Native from Scratch Creating new businesses without legacy constraints, leveraging AI capabilities from inception. **When to Build:** - Incumbent organizations are trapped in "inertia traps" and slow to adopt AI - A beginner's mindset can lead to radically different approaches - Customer acquisition costs can be significantly reduced with AI-native solutions - Service delivery can be fundamentally transformed through AI - Speed to market with AI-native solutions outweighs existing asset value ### Buy Strategy: Acquire and Transform Purchasing established businesses with existing customers and transforming them through AI integration. **When to Buy:** - Customer acquisition and trust-building are expensive or time-consuming - Significant regulatory or compliance barriers exist - Brand and credibility serve as critical differentiators - Distribution networks represent high-value, difficult-to-replicate assets - Existing customer contracts create substantial switching costs ## Key Decision Factors - Industry characteristics and competitive dynamics - Customer switching costs and acquisition expenses - Trust and credibility requirements - Regulatory and compliance complexity - Capital requirements and resource availability ## Strategic Frameworks and Concepts ### The "Truck Size" Analogy *"If I can buy a bucket of cognition for $1 instead of $100,000, why is the truck that big? What changes?"* Historical business processes were designed around humans as the sole source of intelligence. AI enables complete reimagining of processes without human constraints, questioning why systems are sized and structured as they are. ### Chesterton's Gate Principle Understanding the rationale behind legacy systems before redesigning them—recognizing why processes exist in their current form before transformation. ### The "Netflix Model" Incubating new AI-native businesses alongside existing operations, allowing for innovation without disrupting core business functions. ## Transformation Challenges ### Organizational Dynamics - Embedded resistance to change in established businesses - Complex system transitions with interdependent components - Managing stakeholder expectations during transformation - Balancing innovation with operational continuity ### The "Intelligent Assembly Line" Methodology Practical framework for systematic business transformation through AI integration. **Key Insight:** *"This ability to branch at that point always required a human, so you have to have a person in a chair doing that. This implies that you no longer need to put somebody in a chair."* ## Case Studies and Applications ### Duolingo Analysis Exploration of how language learning applications might evolve with AI integration and immersive experience technologies. ### Distribution vs. Technology Value Balancing the worth of existing customer bases against new technical capabilities and AI-driven innovations. ## Market and Investment Considerations ### Competition Dynamics The potential for individual entrepreneurs to create competitive AI applications that challenge established players. ### Long-term Value Creation - Where to build sustainable equity as technical moats erode rapidly - Shifting company lifecycles in public markets (from 60+ years to 15-20 years) - Bitcoin as potential value preservation during industry transformations ## Strategic Implementation ### Acquisition Considerations *"The actual overall cost of that business would include all of those things, effectively as assets. The people involved, the employees are all part of the business that you're buying."* ### Transformation Steps Practical methodologies for companies implementing AI transformation while avoiding the "value trap" of unsuccessful change management. ## Key Takeaway The fundamental question for any business in the AI era is understanding how dramatically reduced cognitive costs change optimal business structure, process design, and competitive positioning.

Good Stuff Podcast 4 months ago

Good Stuff Podcast - Episode 2: The Value Trap Hosts: Andy and Pete Andy and Pete dive into their "Value Trap" framework a visual framework to explain how AI will transform industries and the approach to escape the value trap.

Introduction and reflection on their lo-fi podcast approachExplanation of the "Value Trap" concept Phase 1: Cost reduction through AI implementation Phase 2: Revenue growth and pricing power Phase 3: Competition and mean reversion - Capital allocation strategies for the AI transition - Business characteristics that fare better through this transition - The paradox of technology laggards benefiting most from AI - Strategies for incumbents: The "Netflix model" of business transformation - Buy vs. build approaches for traditional businesses - Risk management during AI transformation - Potential macroeconomic impacts of widespread job displacement - Monetary policy implications and inflation concerns - Buy Bitcoin "This is a renaissance for entrepreneurs. If you're entrepreneurial minded, this is just a huge, an amazing time to be alive."

Good Stuff Podcast 4 months ago

Hosts: Andy and Pete (recorded at City Beach, Perth)

Episode Overview: The inaugural episode explores how AI will transform business models, where value will accrue, and strategic approaches for businesses adapting to AI. Key Discussion Points: Value Shift in AI: The hosts argue that value in AI won't primarily accrue to companies like OpenAI but to traditional service businesses that leverage AI to transform their operations. Transformation of Traditional Businesses: Businesses with language-heavy workflows and high human labor costs can use AI to shift the "unit of intelligence" from humans to scalable AI systems, potentially achieving software-style margins. Hyper-Localization: Pete predicts a future where power and control shift to small businesses that can leverage commodity intelligence, rather than large centralized players. SaaS Evolution: Discussion about whether SaaS business models will decline as AI enables more custom-built solutions specific to individual business needs, reducing dependency on one-size-fits-all platforms. App Players vs. Agentic Workflows: The hosts debate whether there will be an "app player renaissance" or if agentic workflows will eliminate the need for traditional application interfaces. First Principles Thinking: Businesses need to reimagine their processes from first principles rather than simply adding AI tools to existing workflows. Human Role Transformation: A key insight is the shift of humans from being central to business processes to working "at the edge" - where humans become interfaces with the real world while AI handles core processes. The Value Trap: Andy and Pete introduce the concept of the "value trap" - where initial AI efficiency gains create massive value, but competition eventually erodes pricing power, potentially creating challenging transitions. Transformation Strategies: Discussion of whether businesses should create "digital twins" (like Netflix did when moving from DVDs to streaming) or transform their existing operations. Capital Allocation Opportunity: Private equity and venture capital firms are already raising funds to acquire businesses specifically to implement AI transformation strategies. Looking Ahead: The hosts tease a deeper discussion of the "value trap" concept for episode two, promising to show listeners how to navigate this transitional period.Closing Thought: "We spent the last two decades searching for product market fit, and it turned out the valuable thing was just to stick with the companies that already had it."