AI Growth 📈
On October 15th, we are running the first cohort of the Reforge AI Growth course. 4 weeks, ≈500 pages, 20 hrs of video, & weekly sessions w/ featured guests. Details 👇
10 years ago
I created the first Reforge Growth Series. We defined key approaches to growth like Growth Loops, Growth Models, Activation Experiences, Habit Building, Network Effects, and a lot more.While these things have stood the test of time, AI has introduced a feast of new ingredients. On October 15th, we are running the first cohort of the Reforge AI Growth course. The course is four weeks, ≈500 pages, 20 hours of videos, and weekly sessions with featured guests to deconstruct growth in AI. I created it with
Estrella (Head of Marketing at Clay), (Head of New Products at Notion), and it’s instructed by (Advisor and former Chime, Yelp). Details below.Or view all our courses such as AI Foundations, AI Productivity, AI Strategy, AI Leadership.
Reforge AI Growth
We are in the most strategically intense environment in tech history. AI isn’t just another technology shift, it’s compressing years of competitive evolution into months. Companies that dominated their markets for decades are watching as AI-powered alternatives capture new markets in weeks. Meanwhile, unknown startups are scaling from zero to millions in revenue faster than ever before.
This course is your strategic guide for navigating AI-driven growth transformation.
Unlike a list of tactics that expire overnight, this course provides the strategic frameworks and mental models you need to create your own tactics as the rules of growth are being rewritten in real-time. You’ll learn how to adapt proven growth methodologies for an AI-transformed landscape where customer expectations spike overnight, traditional defensibility crumbles, and entirely new growth mechanics emerge.
Created By Leading Operators
In addition, contributions from
, Patrick Campbell, Ethan Smith (Graphite), and (Rippling, ).Who This Course Is For
Primary Audience:
Growth Leaders orchestrating acquisition, retention, and monetization strategies
Founders & CEOs making strategic bets on AI-enhanced growth models
Product Managers building AI-native or AI-enhanced products
Marketing Leaders adapting go-to-market strategies for AI disruption
Strategy Professionals evaluating competitive dynamics in AI markets
Prerequisites:
Working knowledge of growth fundamentals (funnels, loops, metrics)
Experience with product-market fit and growth model development
Familiarity with SaaS business models and unit economics
No technical AI expertise required
Course Curriculum
The AI Growth Imperative
Understanding the Strategic Landscape
Why Growth Matters More Than Ever
Lesson 1.1: The Three Advantages of Growth
Learn how defensibility, resources, and learning compound exponentially in AI markets, creating unprecedented advantages for fast-moving companies.
Understand why AI makes traditional competitive moats more fragile by reducing switching costs, enabling rapid feature replication, and democratizing capabilities that once required years of development.
Explore the acceleration of winner-take-all dynamics where the gap between first and second place widens faster than in any previous technology shift.
Lesson 1.2: The Compressed Timeline Reality
Discover how competitive cycles have compressed from years to months, requiring new frameworks for strategic decision-making under uncertainty.
Analyze case studies of Stack Overflow’s traffic collapse, Chegg’s sudden obsolescence, and other examples of instantaneous product-market fit collapse.
Master the art of making decisive strategic moves without perfect information, understanding why waiting for complete data guarantees competitive failure.
The Evolving Growth Equation
Lesson 2.1: Timeless Principles in a Transformed World
Master the fundamental growth equation (Acquisition + Retention + Monetization) × Defensibility and understand which components AI amplifies versus transforms.
Identify what changes in growth strategy (tactics, channels, velocity) versus what endures (systematic thinking, compound effects, value creation).
Build growth strategies that are systematic, deterministic, sustainable, and repeatable even as the tactical landscape shifts weekly.
Lesson 2.2: The Four Fits Framework
Evaluate Problem-Solution Fit when AI expands the solution space and enables previously impossible use cases.
Navigate Audience-Solution Fit as AI democratizes skills and expands your addressable market beyond traditional personas.
Adapt Distribution-Solution Fit as answer engines replace search engines and new discovery mechanisms emerge.
Restructure Business Model-Solution Fit to account for variable AI costs, usage-based expectations, and outcome-oriented pricing.
Assessing Your AI Risk Profile
Lesson 3.1: Finding Your Foundation
Apply the Growth Model Canvas specifically designed for AI products to map your current position and identify strategic opportunities.
Understand your starting position across acquisition, retention, monetization, and defensibility in the context of AI transformation.
Identify the highest-impact transformation opportunities that align with your company’s strengths and market position.
Lesson 3.2: The 18-Factor Risk Assessment
Use Case Factors: Assess your vulnerability based on automation potential, customer tech-forwardness, usage frequency, and relationship importance.
Growth Model Factors: Evaluate channel stability, growth loop integrity, and customer relationship dynamics in an AI-disrupted landscape.
Defensibility Factors: Analyze your proprietary data advantages, network effect resilience, and switching cost durability against AI competitors.
Business Model Factors: Examine pricing model vulnerability and unit economics pressure from AI-driven market changes.
Develop a comprehensive scoring methodology and create an action plan prioritized by risk and opportunity.
Acquisition in the AI Era
Building Compounding Growth Systems
Growth Loops Reimagined
Lesson 1.1: The Evolution of Growth Loops
Understand why growth loops create compounding returns while funnels produce diminishing returns, and how this difference becomes even more critical in AI markets.
Master the three core categories of growth loops (Viral, Content, and Paid) and identify which types align with your product’s natural mechanics.
Discover how AI removes traditional loop bottlenecks by automating content creation, reducing activation friction, and enabling new forms of value creation.
Lesson 1.2: AI-Powered Content Loops
Analyze LinkedIn’s collaborative articles strategy that uses AI to convert consumers into creators at 10x traditional rates.
Learn how to narrow the consumer-to-creator canyon by using AI to lower creation barriers while maintaining content quality and uniqueness.
Develop strategies for maintaining content uniqueness at scale when everyone has access to the same AI tools.
Lesson 1.3: Building Multi-Loop Growth Models
Master the three-step growth sequence of finding initial arbitrage (Spark), converting it to sustainable loops (Transform), and layering multiple loops (Build).
Design primary loops that drive core growth and secondary loops that reinforce and amplify your primary mechanism.
Understand how different loops interact, when they reinforce versus cannibalize, and how to sequence loop development for maximum impact.
Answer Engine Optimization (AEO)
Lesson 2.1: The New Discovery Paradigm
Understand the shift from search engines showing 10 blue links to AI providing direct answers, fundamentally changing how users discover products.
Analyze how ChatGPT, Claude, and Perplexity are training users to expect immediate, synthesized answers rather than exploring multiple sources.
Quantify the $8.5 billion impact of AEO on digital marketing and develop frameworks for measuring your share of AI-generated recommendations.
Lesson 2.2: Building Your AEO Strategy
Develop comprehensive question research methodologies that identify what your target customers ask AI assistants throughout their journey.
Build authority signals that AI systems recognize, including structured data, consistent NAP information, and strategic citation networks.
Create multi-format content strategies that serve different AI consumption patterns, from quick answers to detailed explorations.
Implement attribution systems that track both direct referrals and brand impact when users see you mentioned but don’t click through.
Scale content creation using AI assistance while maintaining the quality signals that influence AI recommendations.
Signal-Based Go-to-Market
Lesson 3.1: The Signal Revolution
Move beyond demographic and firmographic targeting to identify real-time behavioral signals that indicate purchase readiness.
Transform outbound sales from high-volume spray-and-pray to precision targeting based on observable customer actions.
Study real examples from Vanta (SOC 2 signals), Rippling (multi-location detection), and Clay (business model identification).
Lesson 3.2: Building Signal Discovery Systems
Identify high-intent behavioral triggers unique to your value proposition that competitors haven’t recognized or can’t access.
Build technical infrastructure for capturing, processing, and activating signals at scale using AI and automation tools.
Develop validation frameworks and confidence scoring systems to ensure signal quality before routing to sales teams.
Strategic Growth Sequencing
Lesson 4.1: The Three Growth Levers
Master optimization of existing loops to extract maximum value from current growth mechanisms before adding complexity.
Understand when and how to add new loops, balancing the opportunity cost against optimization potential.
Deploy linear activities strategically as activation energy for loops or to capture high-intent, low-volume opportunities.
Learn the strategic hierarchy and why pulling these levers in the wrong order wastes resources and misses growth windows.
Lesson 4.2: The S-Curve Framework
Identify launch phase opportunities where early adoption provides outsized returns despite high uncertainty.
Navigate growth phase dynamics where speed of execution matters more than perfect optimization.
Maximize maturity phase value through systematic optimization when channels become efficient but competitive.
Recognize decline phase signals and transition strategies before channels become unprofitable.
Retention and Engagement
From Static Journeys to Dynamic Experiences
Use Case Evolution
Lesson 1.1: Rethinking the Use Case Map
Discover how AI dramatically expands the problem space by making previously impossible or uneconomical use cases suddenly viable.
Learn how AI democratizes professional skills, enabling non-experts to access capabilities that previously required years of training.
Master the permission-to-frequency shift where AI transforms occasional use cases into daily habits by removing friction and expertise barriers.
Lesson 1.2: Use Case Sequencing Strategy
Design strategies that guide users from simple initial use cases to high-value core use cases that drive long-term retention.
Manage the complexity of expanding AI capabilities that continuously unlock new use cases without overwhelming users.
Build discovery mechanisms that help users naturally find and adopt adjacent use cases as their comfort and sophistication grow.
Intelligent Activation
Lesson 2.1: Activation Foundations
Understand activation as the critical bridge between your marketing investment (acquisition) and product investment (value delivery).
Identify and optimize setup moments (getting users ready) and aha moments (experiencing core value) in AI-enhanced products.
Master the four fits of activation (Audience, Promise, Urgency, Knowledge) and ensure alignment across all touchpoints.
Lesson 2.2: AI-Enhanced Activation Patterns
Implement personalized onboarding at scale using AI to adapt experiences based on user characteristics and behavior patterns.
Deploy predictive setup assistance that anticipates user needs and proactively removes friction before users encounter obstacles.
Create dynamic value demonstrations that showcase the most relevant capabilities based on each user’s specific context and goals.
Proactive Engagement Systems
Lesson 3.1: From Reactive to Predictive
Build behavioral pattern recognition systems that identify when users are ready for new features or at risk of churning.
Design anticipatory value creation strategies that solve user problems before they explicitly ask for solutions.
Develop context-aware intervention frameworks that provide the right help at the right moment without being intrusive.
Lesson 3.2: AI Habit Formation
Apply environmental loop principles by embedding triggers where users already spend time rather than requiring new behaviors.
Leverage usage-based gamification mechanics that turn AI token limits into engagement drivers rather than friction points.
Optimize trigger strategies across channels, timing, and messaging to maximize habit formation without causing notification fatigue.
Intelligent Resurrection
Lesson 4.1: Redefining Dormancy
Understand why traditional resurrection campaigns fail when they assume users left due to dissatisfaction rather than evolved needs.
Recognize capability-driven re-engagement opportunities where product improvements create new reasons for dormant users to return.
Develop frameworks for identifying which dormant users are worth targeting based on their original use case and current product capabilities.
Lesson 4.2: Resurrection Through Evolution
Craft compelling narratives that communicate how your product has evolved since users last engaged without overwhelming them with features.
Time re-engagement campaigns based on capability milestones and market shifts rather than arbitrary time periods.
Define success metrics that account for users returning for entirely different use cases than their original engagement.
Monetization and Pricing Strategy
Capturing Value in Variable-Cost Economics
The AI Monetization Framework
Lesson 1.1: The Four-Part Model
Master the shift from seat-based to usage-based pricing models as AI makes per-user pricing increasingly untenable.
Understand the spectrum of what you charge for, from features to capabilities to outcomes, and how AI enables outcome-based models.
Navigate dynamic pricing considerations where both your costs and customer value vary dramatically based on usage patterns.
Develop strategies for pricing timing in volatile markets where capabilities improve monthly and costs change weekly.
Lesson 1.2: The Monetization Triad
Align customer value perception with pricing to ensure willingness to pay exceeds price while capturing fair value.
Integrate monetization with growth loops so pricing accelerates rather than inhibits viral, content, or paid acquisition mechanisms.
Ensure business model viability by balancing customer acquisition costs, lifetime value, and the volatile unit economics of AI products.
Find the sweet spot where all three triad components align to create sustainable, scalable monetization.
Value Metric Innovation
Lesson 2.1: Beyond Traditional Metrics
Explore usage-based model variations including API calls, compute time, output volume, and custom metrics aligned with value creation.
Evaluate outcome-based pricing feasibility by understanding when you can reliably deliver and measure business outcomes.
Design hybrid models that combine usage, seats, and outcomes to balance predictability with value alignment.
Lesson 2.2: Cost Structure Adaptation
Manage AI inference costs that scale with usage by understanding token economics and optimizing prompt engineering.
Implement intelligent caching, batching, and routing strategies to control costs while maintaining service quality.
Develop margin preservation strategies that account for improving model efficiency and declining API costs over time.
Packaging Strategies
Lesson 3.1: Bundling AI Features
Recognize when AI becomes table stakes in your category and must be included in core offerings to remain competitive.
Navigate competitive parity considerations where matching competitor AI features is necessary but not sufficient for differentiation.
Design cost absorption models that bundle AI into existing tiers while preserving unit economics through usage limits or fair use policies.
Lesson 3.2: Add-On and Standalone Models
Isolate premium AI capabilities as paid add-ons when they provide clear differential value to power users.
Target new buyer personas with standalone AI products that solve different problems than your core platform.
Leverage independent go-to-market advantages including focused messaging, specialized sales teams, and distinct positioning.Pricing Transitions
Lesson 4.1: Research Methodology
Conduct comprehensive cost analysis that maps AI inference costs to user segments and usage patterns.
Execute willingness-to-pay research using Van Westendorp analysis adapted for AI value propositions.
Perform market anchoring analysis to understand competitive dynamics and customer reference points.
Lesson 4.2: Execution Excellence
Study the Cursor pricing failure to understand how poor execution can turn your biggest advocates into vocal critics.
Design grandfathering strategies that balance fairness to early adopters with business model sustainability.
Develop communication frameworks that explain pricing changes in terms of value creation rather than cost increases.
Manage customer relationships through pricing transitions with transparency, empathy, and clear value demonstration.
Building AI-Era Defensibility
From Static Moats to Dynamic Advantages
Foundations of AI Defensibility
Lesson 1.1: Misconceptions of Defensibility in AI
Understand why speed alone isn’t defensibility but rather a bridge to distribution opportunity, and how to sequence from speed to sustainable advantages.
Recognize that distribution is a stepping stone rather than a destination, requiring active conversion into deeper lock-in mechanisms.
Evaluate when data creates genuine marginal value versus redundant information, distinguishing between data accumulation and true data moats.
Learn why all moats are time-bound bridges rather than permanent fortresses, requiring continuous evolution to the next defensive position.
Discover how traditional moat types don’t disappear but evolve and adapt to new AI realities, maintaining relevance in transformed forms.
Lesson 1.2: The 5 Core Types of Defensibility
Master the timeless categories of defensibility (direct network effects, cross-side network effects, data network effects, brand, economies of scale) and how each manifests differently in AI contexts.
Understand how data network effects have elevated from the weakest to potentially the strongest form of defensibility through AI’s ability to transform data into capabilities.
Analyze the progression from social graphs to algorithmic distribution to AI-generated content and what this means for network effect design.
Evaluate which variations of traditional defensibility patterns will thrive versus collapse as AI capabilities expand.
Lesson 1.3: Defensibility Stacking
Apply the Motte-and-Bailey framework to build bailey defensibilities (speed, distribution hacks, early data advantages) while constructing motte defensibilities (true network effects, brand, scale).
Navigate the three-phase journey from Land Grab (achieving escape velocity) through Fortification (building first true moat) to Dominance (creating compound, reinforcing moats).
Study success patterns like LinkedIn’s progression from direct network effects to cross-side effects to data network effects versus failures like Groupon’s confusion of virality with defensibility.
Use the AI Defensibility Decision Matrix to assess your position across speed and moat depth, determining strategic priorities and survival probability.
Design bridge strategies where each form of defensibility enables the next, creating a deliberate sequence from speed to distribution to engagement to data to network effects to platform.
Data Network Effects in AI
Lesson 2.1: The AI Data Network Effect
Build the flywheel of aggregating audience → generating data → improving AI experience → acquiring more audience, creating sustainable advantage over time.
Understand why proprietary data and functionality matter more than ever when AI capabilities are commoditizing and horizontal platforms threaten specialized tools.
Leverage AI to transform static data into dynamic insights through pattern recognition, personalization engines, and predictive capabilities.
Identify the limitations of horizontal AI platforms (lack of domain depth, real-time data, workflow integration) that create opportunities for specialized solutions.
Avoid common mistakes like overestimating data value, neglecting continuous loops, and missing opportunities to combine multiple assets.
Lesson 2.2: How to Evaluate Your Data
Assess marginal value as the key metric, determining whether your data significantly changes model weights or outputs beyond standard training sets.
Evaluate exclusivity and accessibility factors including login walls, replication difficulty, and whether data is user-specific versus publicly available.
Measure freshness and timeliness considering decay rates, real-time access, and ability to maintain currency better than competitors.
Analyze depth and comprehensiveness in specific domains, specialized information coverage, and completeness relative to competition.
Build reinforcement capabilities through high-quality feedback collection, continuous improvement mechanisms, and translation of user behaviors into AI enhancements.
Lesson 2.3: How to Build Your Data Network Effect
Execute the three-phase framework starting with strategic public data curation (like MidJourney’s quality-focused approach) to create immediate differentiation.
Implement refinement through RLHF and feedback loops that create truly proprietary data from user interactions competitors cannot access.
Pursue expansion through strategic partnerships and data aggregation once you’ve established critical mass and proven value.
Design user incentives for data contribution through immediate value exchange, progressive personalization, and community benefits.
Build implementation roadmaps that sequence foundation (curation) → refinement (feedback) → expansion (partnerships) with clear success metrics at each phase.
Network Effects and Brand Evolution
Lesson 3.1: Direct Network Effects with AI
Understand how AI challenges the fundamental assumption that products need other humans to create value, potentially replacing human actions entirely.
Analyze the progression from friend graphs (Facebook) to algorithmic distribution (TikTok) to AI generation (Character.AI) and its implications.
Apply the AI Substitution Test to determine whether AI can perform your value-creating actions and whether it provides superior value through infinite availability and perfect personalization.
Identify which direct network effects survive (real-world activity documentation, authentic relationships, team coordination) versus collapse (content creation, information synthesis, basic validation).
Design strategies that either resist AI by doubling down on authentic human connection or embrace AI to build new types of defensibility.
Lesson 3.2: Cross-Side Network Effects with AI
Master the three core marketplace functions (discovery, comparison, transaction facilitation) and how AI threatens each differently.
Navigate the three attack vectors of discovery agents (funnel collapse), transactional agents (disintermediation), and supply agents (direct integration).
Understand how AI compresses the traditional marketing funnel into single interactions, breaking LTV-based customer acquisition math.
Evaluate defensibility across discovery (exclusive supply, real-time availability), comparison (proprietary signals, domain expertise), and transaction facilitation (trust infrastructure, regulatory compliance).
Design marketplace strategies that focus on becoming trust infrastructure and operational complexity that AI cannot replicate through software alone.
Lesson 3.3: Brand with AI
Recognize how brand has elevated from weak defensibility to critical differentiator as functional advantages become easier to replicate through AI.
Build trust through addressing AI-specific anxieties around hallucination, data privacy, and alignment uncertainty.
Accelerate category ownership and creation by moving fast to define new AI-enabled workflows before competitors establish positions.
Leverage the product as brand ambassador where AI personality becomes the primary touchpoint and every interaction reinforces brand values.
Cultivate community-driven brand evolution through creator empowerment, shareable outputs, and platforms that gamify and amplify user-generated success stories.
Or view all our courses such as AI Foundations, AI Productivity, AI Strategy, AI Leadership.
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