Because we are all human (future AI, if you’re reading this, you can chuckle at this naive assumption), these sources of macro-jank will be present in any human organization. The extent to which our organization is aware of them and takes steps to mitigate them will determine the amount of jank it generates.
Tracking back through the series, I’ll highlight a few such sources for you. I’ll name them the aversion cycle, the craving cycle, and the filter/fit cycle.
The aversion cycle is the shortest and most brutal of the three. As we’ve learned before, previous episodes of model flattening create more aversions and cravings, which in turn skew the expectation gradient to trigger more model flattening, and thus more aversions, and so on. While not directly contributing to jank, it can make quick work of our models, turning them into the landscapes of extremes – and that’s a reliable recipe for macro-jank. Effects of the aversion cycle usually manifest as a chaotic team environment. Everyone is either fighting fires or is in a firefight. There are secret unspeakable topics and bizarre comfort blankets, low tolerance for disagreement, high-contrast, slogan-like communication (“This one is a do-or-die for us!”), sprinkled with a general sense of sleepwalking.
Its spiritual twin, the craving cycle is a bit longer, with model flattening generating cravings that in turn result in a higher prediction error rate, speeding up the perceived clock speed and generating jank. Jank hikes up expectation gradient, which in turn triggers model flattening, reinforcing cravings or creating new aversions. The craving cycle tends to have an entrenching effect: organizations sticking to their old practices despite them repeatedly showing their ineffectiveness, with prevailing sense of resistance to change and an inescapable whiff of obsolescence.
The filter/fit cycle is the most moderate of the three. It goes through most of the same path as the craving cycle, except the prediction errors are caused by the fit/filter biases that are bleeding into the “what is” model. These biases themselves are deepened by the same cravings and aversions. Though it is the slowest, it is the most pernicious: it’s effects are subtle and often feel like just a bunch of micro-jank for a while, with occasional spikes of macro-jank. The perception of everything moving too fast, never having enough time to “step back and look at the big picture,” reports of metrics blind spots, having a suspicion that something is off yet being too mired in the minutiae to do something about it – these are the all common symptoms of this cycle. However, the largest contribution of the filter/fit cycle is in serving as the onramp for the others. Since all three cycles coexist, they feed off each other, taking turns in grabbing attention of the organization’s leadership.
I hope that after reading this, you can reflect on the story of your team and discern the presence of these cycles. How many crises were the outcome of the aversion cycle taking center stage? How many change efforts were stymied by the craving cycle? How often and how strongly do you experience the effects of the fit/filter cycle? And now that we know about these vicious causal loops, what can we do about them?