Why ChatGPT will be the next big growth channel (and how to capitalize on it) | Brian Balfour (Reforge)

Why A New Distribution Platform Matters Now

Brian Balfour walks through a clear, repeatable pattern in how major distribution platforms emerge, expand, and then tighten control. Against a backdrop of shrinking organic channels—declining clicks, algorithm changes, and accelerating competition—Balfour argues the conditions are right for a fresh platform to reshape how products find customers. His working prediction: ChatGPT, or a ChatGPT-style chat platform, is the most likely candidate to become the next major distribution layer because of its retention curves and growing contextual memory.

The Four-Step Cycle Of Platform Emergence

Balfour lays out a four-step lifecycle that repeats across eras: (0) market conditions and consensus on a new category, (1) discovery of a defensible moat, (2) platform opening that invites third-party developers and creators, and (3) platform closing for control and monetization. This arc explains Facebook’s canvas boom and later contraction, Google’s gradual appropriation of search real estate, iOS’s app store dynamics, and smaller examples like LinkedIn and Udemy.

Why Context And Memory Become The Moat For Chat Interfaces

Unlike past channels where pure reach mattered most, these chat-first platforms win on context: the more personal data, connectors, and memory they hold about a user, the better their outputs and the stronger their retention flywheels. That personalized feedback loop—context improves output, output encourages usage, usage creates more context—is the defensible moat Balfour highlights as central to who will dominate.

What Founders And Product Teams Should Do Today

There is a practical playbook embedded in this diagnosis. For late-stage companies the recommendation is to place multiple, measured bets across promising platforms. For startups with scarce resources it is the opposite: choose one platform and place a focus bet. That means building early integrations, experimenting with context connectors, and designing for quick adoption should a platform offer preferred-partner programs. Balfour cautions that timing matters: platform cycles are shortening, so the window to capitalize is narrow.

Long-Term Exit Thinking And Durable Moats

Building on a new platform is a two-phase game: enter quickly to capture distribution, and simultaneously develop an exit strategy that preserves long-term value. Exit plays include owning a specific part of the user workflow, accumulating proprietary contextual data that a larger platform lacks, or engineering micro network effects that persist even after platform monetization tightens.

How Teams Can Move Faster With AI Adoption

Balfour also explains why some organizations adopt AI faster: they create hard constraints and measurable adoption targets, assign explicit owners, tie incentives and ladders to AI usage, and remove slow bureaucratic bottlenecks like procurement, legal, and IT. Companies that make those hard decisions see outsized productivity gains because they remove the slowest constraint in the system.

• Play the game: Don’t opt out of emerging platforms; your competitors will join.
• Focus bet for startups: Place concentrated resources on one platform rather than splitting effort.
• Design for exit: Build product features that will survive or even thrive when platform gates close.

In short, Balfour frames today’s AI moment as both opportunity and imperative: a familiar platform lifecycle is unfolding and companies that understand the steps, move quickly to capture initial distribution, and design durable moats around context and workflow will be positioned to win. The practical takeaway is immediate and concrete—assess where your users live, evaluate platforms by retention and monetization potential, and choose a focused bet paired with a clear exit plan to avoid being left behind.