Flipping the Bozo Bit: How Dismissing Others Prevents Learning from Tech Incidents

I'm too young to have seen Bozo the Clown myself, but I'm old enough to get the references. "Flipping the bozo bit" is an expression from the software world that describes a moment when you reach a point where you simply stop respecting the opinion of a particular person, most likely a co-worker. From that point forward, you disregard what they say, effectively marking them as not worth listening to—a bozo.

There's a related phenomenon that occurs when we hear about incidents or failures that happened to others. Our immediate conclusion is that this outcome occurred because, well, that person is a bozo. This thinking becomes particularly relevant when examining technology incidents, where the temptation to dismiss others as incompetent prevents us from extracting valuable lessons.

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The recent AI-related incident that befell PocketOS illustrates this tendency perfectly. In a Twitter post titled "An AI Agent Just Destroyed Our Production Data. It Confessed in Writing," PocketOS founder Jer Crane detailed how an AI agent they were using accidentally deleted their production database. The post garnered significant attention online, with many reactions along the lines of "wow, was this guy ever a bozo." This immediate dismissal of the PocketOS team as incompetent misses the crucial opportunity for learning that such incidents provide.

The technical term for this phenomenon, introduced by American resilience engineering researchers Richard Cook and David Woods in their 2006 paper "Distancing Through Differencing: An Obstacle to Organizational Learning Following Accidents," is precisely what I'm describing. The paper, from the collection "Resilience Engineering: Concepts and Precepts," examines how organizations fail to learn from incidents when they focus on differences rather than similarities.

Cook and Woods explain: "By focusing on the differences, they see no lessons for their own operation and practices." When people hear about an incident and respond by concluding "an incident like that would never happen to us; that happened to those workers over there because they are clearly not as careful as we are," that's distancing through differencing in action. The overall conclusion becomes that the incident "couldn't happen here."

The researchers illustrate this with a case study of a chemical fire at an American manufacturing plant. Similar fires had occurred previously at the company's overseas plants, but American employees concluded there was nothing to learn from those incidents because such accidents couldn't happen to them in the U.S. After all, those overseas workers were perceived as less skilled, less motivated, and less careful—in short, different.

Ironically, after the chemical fire at the American plant, other workers at that very same facility also exhibited distancing through differencing. Workers in the same plant, working in the same area where the fire occurred but on different shifts, attributed the fire to the lower skills of the workers on the other shift.

This tendency to focus on differences between "us" and "them" when incidents happen to others leads us to miss aspects of the system that we actually have in common with them. By focusing on the differences, we miss the opportunity to learn from their experiences because the seductive belief becomes that there's nothing for us to learn here.

Cook and Woods emphasize: "do not discard other events because they appear on the surface to be dissimilar. At some level of analysis, all events are unique; while at other levels of analysis, they reveal common patterns."

Returning to the PocketOS incident, if we conclude that PocketOS employees were simply using AI irresponsibly and that we are more responsible than that, we learn nothing from their experience. The reality is that many organizations experimenting with AI agents face similar challenges around system design, safeguards, and understanding the limitations of these systems.

I was heartened to see that Railway, the vendor used by PocketOS that exposed the delete API, has made changes to improve safety after the incident. Their post "Your AI wants to nuke your database. Guardrails fix that" demonstrates how proper organizational learning can lead to concrete improvements that benefit the entire ecosystem.

Stepping back, this isn't the last AI-related incident we're going to see in our industry, not by a long shot. The technology is evolving rapidly, and organizations are still figuring out how to safely integrate AI systems into their operations. The next time you read about such an incident, if your immediate reaction is "they should have known not to do X," then you've fallen into the distancing through differencing trap.

(As an aside, "they should have known..." is an incoherent sentence. It's one thing if somebody deliberately took on excessive risk. But it's another thing if they unknowingly took on excessive risk. How can you blame a person for not knowing something?)

When organizational learning moves past the obstacle of distancing through differencing, the response changes. After all, "there but for the grace of God go we all." The PocketOS team made a mistake, but the conditions that allowed that mistake to happen likely exist in many other organizations experimenting with AI. By focusing on the commonalities rather than the differences, we can create systems that are more resilient for everyone.

The challenge for technology organizations is to cultivate a culture where incidents are treated as learning opportunities rather than occasions to assign blame. This requires moving beyond the simplistic "bozo bit" mentality and embracing the complexity of system failures. When we understand that most incidents result from systemic factors rather than individual incompetence, we can begin to design systems that are more robust and forgiving.

As we continue to integrate AI and other complex technologies into our systems, the temptation to flip the bozo bit will only grow. The organizations that thrive will be those that resist this temptation and instead focus on the shared patterns that connect seemingly different incidents. Only then can we hope to build systems that are truly resilient in the face of inevitable failures.