Platform Cycles Driven by Competitive Incentives, Not Evil Intent

The emergence of new distribution platforms follows a predictable four-step cycle that creates temporary but powerful growth opportunities for companies who recognize and act on them early. This pattern has repeated across Facebook, Google, mobile, and other platforms—and is about to happen again with AI, most likely through ChatGPT.

Brian Balfour has observed that building great products is necessary but insufficient for success. The real separation between winners and losers comes from distribution advantages. The game of startups is about gaining distribution before incumbents can copy you, but this has become increasingly difficult as organic channels like SEO decline and incumbents copy faster.

When new distribution platforms emerge, they follow a consistent pattern: first, competitive market conditions form with multiple players battling for dominance; second, a leader identifies their moat and opens their platform to third parties to accelerate growth; third, they provide distribution incentives to attract developers and content creators; and finally, they close the platform for control and monetization.

This closing phase happens through shutting down access, developing first-party applications that absorb the highest-value use cases, or artificially depressing organic distribution to push users toward paid mechanisms. The cycles are getting shorter, giving companies less time to capitalize on opportunities.

For leaders, the implications are clear: you must play this game because your competitors will. As Brian puts it, "It ends up being a prisoner's dilemma—there is no opting out of the game." When evaluating which platform to bet on, prioritize retention and engagement depth over user numbers, consider user quality and monetization potential, analyze the value exchange being offered, and factor in scale.

The approach differs based on company stage. Late-stage companies can afford to place multiple bets and wait to see which platform wins before committing fully. Startups, however, must choose one platform and go all-in due to limited resources. Most critically, while entering the game, companies must immediately begin planning their exit strategy—how they'll maintain value when the platform inevitably closes.

This cycle creates opportunities for disruption. Just as Zynga grew massive by capitalizing on Facebook's platform, new companies can achieve escape velocity by being early adopters of emerging AI distribution channels. The window for this opportunity appears to be opening in the next six months, making this a crucial moment for companies to prepare their strategies.

Evaluating AI Adoption Within Organizations

Beyond platform strategy, Brian shared insights on organizational AI adoption. The companies making the most progress share several characteristics: they create hard constraints (like limiting headcount until AI solutions are proven), they're willing to make difficult personnel decisions with employees who resist change, and their executives stay connected to ground-level implementation rather than assuming adoption is happening naturally.

The most successful organizations recognize that AI adoption isn't merely about new tools—it's a fundamental culture change requiring alignment across the company. They understand that output is constrained by the slowest part of the system, addressing bottlenecks in IT, legal, and procurement that often limit adoption speed.