An AI like ChatGPT struggles to provide tech support for its own features because the product changes too rapidly. The web content and documentation it's trained on lag significantly behind the current software version, creating a knowledge gap that doesn't exist for more stable products.

Dramatically increasing feature output via AI creates a new bottleneck: user attention. A user receiving 100 new features per month lacks the time or inclination to discover, learn, and adopt them, meaning most of the accelerated development effort is ultimately wasted.

An AI product's job is never done because user behavior evolves. As users become more comfortable with an AI system, they naturally start pushing its boundaries with more complex queries. This requires product teams to continuously go back and recalibrate the system to meet these new, unanticipated demands.

Users frequently write off an AI's ability to perform a task after a single failure. However, with models improving dramatically every few months, what was impossible yesterday may be trivial today. This "capability blindness" prevents users from unlocking new value.

AI models improve in significant step-changes monthly, making a user's prior experience an unreliable guide. Users must adopt a "beginner mindset" and continually re-test tasks that the AI previously failed at to fully leverage its evolving capabilities.

Onboarding users to complex AI capabilities through articles or tutorials is ineffective. The key to mass adoption is designing the product to 'show' its power in the moment, tailored to the user's specific context and needs. This makes the product itself the primary driver of discovery and education.

Because AI capabilities improve so quickly, users often form a fixed, outdated impression based on their first interaction. This creates a "discovery problem" where companies like OpenAI must constantly re-engage users and market specific new use cases to overcome the "first-mover disadvantage" of a stale perception.

AI models experience sudden, discontinuous jumps in specific capabilities—a "jagged edge." The product role must shift from following a predictable roadmap to actively discovering these new, often unexpected, abilities and rapidly building product experiences around them.

Early AI products face a unique challenge: millions of users form a lasting impression based on an early, less-capable version. As the AI rapidly evolves, the company must overcome this outdated perception by proactively demonstrating new use cases and capabilities to re-engage its massive initial user base.