When a technology reaches billions of users, negative events will inevitably occur among its user base. The crucial analysis isn't just counting incidents, but determining if the technology increases the *rate* of these events compared to the general population's base rate, thus separating correlation from causation.
A technology like Waymo's self-driving cars could be statistically safer than human drivers yet still be rejected by the public. Society is unwilling to accept thousands of deaths directly caused by a single corporate algorithm, even if it represents a net improvement over the chaotic, decentralized risk of human drivers.
The most pressing AI safety issues today, like 'GPT psychosis' or AI companions impacting birth rates, were not the doomsday scenarios predicted years ago. This shows the field involves reacting to unforeseen 'unknown unknowns' rather than just solving for predictable, sci-fi-style risks, making proactive defense incredibly difficult.
An intensely aggressive email from Marc Andreessen to Ben Horowitz during a high-stakes Netscape launch is framed not as a relationship-ending event, but as the foundation of a resilient partnership. This suggests that in high-pressure startup environments, radical and even harsh honesty can be recoverable and ultimately build trust.
Unlike hardware launches where users can keep their old device, forced software updates like OpenAI's GPT-4o replacing 4.0 take something away from users. This sunsetting aspect creates a sense of loss and resentment, especially for users who have formed a deep attachment to the previous version, violating typical launch expectations.
The Instagram study where 33% of young women felt worse highlights a key flaw in utilitarian product thinking. Even if the other 67% felt better or neutral, the severe negative impact on a large minority cannot be ignored. This challenges product leaders to address specific harms rather than hiding behind aggregate positive data.