Why ChatGPT will be the next big growth channel (and how to capitalize on it) | Brian Balfour (Reforge)
Brian Balfour, founder/CEO of Reforge and former HubSpot growth leader, predicts ChatGPT will emerge as a powerful new distribution channel within six months. He shares a four-step cycle platforms follow and advises companies on how to leverage this opportunity.
Deep Dive Analysis
16 Topic Outline
The Changing Landscape of Product Growth and Distribution
The Four-Step Cycle of Distribution Platforms
Historical Examples of Platform Cycles
Why Companies Must Play the Platform Game
ChatGPT as the Next Major Distribution Platform
Moat of AI Platforms: Context and Memory
Signals for ChatGPT's Third-Party Platform Launch
Alternative AI Platforms and Niche Opportunities
Timeline for AI Platform Opportunities
Betting Strategies for Startups and Late-Stage Companies
Criteria for Evaluating New Distribution Platforms
Preparing for New Platform Launches
Reforge's Transition to AI-Native Product Tools
Effective AI Adoption Strategies in Companies
Addressing Disconnects in AI Adoption
Identifying and Attacking Bottlenecks in AI Adoption
6 Key Concepts
Distribution Platform Cycle
A recurring four-step pattern that new distribution platforms follow: market conditions met, moat identified, platform opening for third-party developers, and platform closing for control and monetization. Understanding this cycle is crucial for companies to strategize their engagement.
Moat (in AI Platforms)
For AI chat platforms, the primary moat is context and memory. Models by themselves produce similar results, but the one with more user context and the ability to store memory around user interactions produces better, more personalized outputs, creating a powerful flywheel effect.
Prisoner's Dilemma (Platform Adoption)
Companies face a dilemma where opting out of a new distribution platform is risky because competitors will likely adopt it, changing customer expectations and potentially leading to market loss. It's often better to be an early adopter, even with the understanding of future platform changes.
Smile Curve (Retention)
A retention curve where engagement initially drops but then increases over time, indicating a strong network effect or similar dynamic. This is a rare and powerful early indicator that a platform is on a trajectory to achieve escape velocity and become a major winner.
Hard Constraints (AI Adoption)
Strict, non-negotiable rules or limitations imposed by leadership to force AI adoption within an organization. Examples include headcount freezes until AI solutions are proven, or requirements for product reviews to include AI-generated prototypes, driving fundamental behavioral change.
Catalysts, Converts, Anchors
Three groups of people observed during organizational transformations like AI adoption. Catalysts lead the charge, experimenting on their own; Converts adapt with structure and clear plans; Anchors resist change, creating friction and slowing progress.
10 Questions Answered
Startups face increased difficulty in gaining distribution and achieving escape velocity because incumbents can copy faster, organic distribution channels have shrunk, and AI has led to an infinite increase in competition.
The cycle involves: 0) market conditions met with fierce competition and no clear winner, 1) a player identifies a moat for defensibility, 2) the platform opens to third-party developers with incentives like distribution, and 3) the platform closes for control and monetization, often by restricting organic distribution or absorbing use cases.
Companies must play the game because if they don't, competitors will, changing customer expectations and potentially leading to market loss. It's a prisoner's dilemma where being early offers a significant, albeit temporary, growth opportunity.
Brian Balfour predicts ChatGPT will be the next major distribution platform, driven by its strong retention, focus on context and memory as a moat, and signals indicating an upcoming third-party platform launch.
The primary moat for AI chat platforms is context and memory, as the actual difference in output comes from the model's ability to store and utilize personalized user context, leading to better results and a strong flywheel effect.
Late-stage companies can afford to place multiple bets and wait for a clear winner before committing significant resources. Startups, with scarce resources, must make a focused, 'all-in' bet on a single platform to maximize their chances of success.
Companies should prioritize platforms with strong user retention and depth of engagement, high user quality and monetization potential, a clear and favorable value exchange for developers, and significant scale and momentum.
The most impactful strategy is to implement hard constraints, such as limiting new hires until AI solutions are explored or requiring AI-generated prototypes for product reviews, forcing teams to actively integrate AI into their workflows.
Many executives are disconnected from the actual ground-level AI adoption, often believing it's happening naturally, while in reality, only a small percentage of employees are actively using new AI tools, leading to slower overall transformation.
Employees typically fall into three groups: Catalysts, who lead experimentation; Converts, who adapt with clear structure and permission; and Anchors, who resist change and can slow down the entire transformation process.
13 Actionable Insights
1. Play the Emerging Distribution Game
Don’t trick yourself into thinking you can opt out of new distribution platforms like ChatGPT, as competitors will leverage them and customer expectations will change, making it essential to participate.
2. Act Early on New Platforms
Recognize that new distribution cycles are getting shorter, meaning you have a smaller window to capitalize on emerging platforms before they become saturated or close down.
3. Startups: Go All-In on One Platform
If you are a startup with scarce resources, choose one emerging distribution platform and commit fully to it, rather than spreading your bets across multiple channels.
4. Late-Stage Companies: Place Multiple Bets
If you are a late-stage company, you can afford the luxury to place multiple bets on emerging distribution platforms and wait to see which one becomes the clear winner before fully investing.
5. Anticipate Platform Closure
Understand that new distribution platforms will eventually close for control and monetization; plan an exit strategy to avoid being trapped by their restrictions or costs.
6. Evaluate Platform Potential
When choosing a new distribution platform, prioritize those with high user retention and depth of engagement, good user quality and monetization potential, a clear value exchange, and significant pure scale.
7. Identify AI Platform Moat
Focus on platforms that prioritize context and memory as their moat, as these enable better, more personalized outputs and drive a flywheel of increased usage and retention.
8. Prepare for Platform Launch
Be ready to pivot your strategy on a dime and go all-in when a new distribution platform officially launches its third-party developer program, as quick capitalization is key.
9. Form Preferred Relationships
If possible, try to establish preferred relationships with emerging platform owners (e.g., OpenAI) to gain early access and insights into their third-party developer programs.
10. Implement Hard AI Constraints
Drive AI adoption by implementing hard constraints within your company, such as benchmarking team sizes to be one-fifth of competitors or requiring AI proof before new headcount.
11. Address AI Adoption Anchors
For employees resistant to AI transformation, set clear deadlines for adaptation or plan for their exit, as a cohesive culture is crucial for successful company-wide change.
12. CEOs: Understand AI Adoption Reality
As a CEO, actively get to the ground floor to understand the true state of AI adoption and usage within your company, as executive mandates often don’t translate into widespread implementation.
13. Identify & Attack AI Bottlenecks
Ruthlessly identify and address the slowest parts of your AI adoption system (e.g., IT, legal, procurement, or specific roles like product managers) to ensure that accelerating one part doesn’t just shift the bottleneck.
6 Key Quotes
Building a great product is one of those things that's necessary, but not sufficient. And actually, the separation is between those that build really great distribution.
Brian Balfour
The AI technology shift has been a technology shift that has not come with a distribution shift yet.
Brian Balfour (quoting Casey Winters)
There is no opting out of the game. You have to play the game.
Brian Balfour
The broad trend is that the cycles seem to be getting shorter and shorter and shorter and shorter. So you actually have a smaller amount of time to play the game.
Brian Balfour
It was never the person who had the biggest distribution at the moment of time. It was the one that had the best retention and engagement.
Brian Balfour
The slowest your output is, uh, constrained by the slowest part of your system.
Brian Balfour (quoting Fareed Masavat)
1 Protocols
Distribution Platform Cycle
Brian Balfour- Step 0: Market conditions met, with consensus on a new huge category but no clear winner, leading to fierce competition among 5-7 major players.
- Step 1: A player identifies a 'moat' (defensibility) and needs to press this advantage, requiring the help of an ecosystem.
- Step 2: The platform opens to third-party content creators, app developers, and businesses, offering new forms of distribution as a value exchange.
- Step 3: The platform closes for control and monetization, either by shutting down features, developing first-party applications, or artificially depressing organic distribution to push towards paid mechanisms.