Technical Debt

The more precisely a technological system is engineered—its algorithms finely tuned, its processes deeply automated, its data flows tightly orchestrated—the more space is created for chaotic ambiguity to hide in the seams. In social media, for instance, the apparatus of engagement-metrics, feed-ranking, and viral amplification claim clarity and intent, yet they spawn unpredictable collective behaviour: echo-chambers, viral panics, attention cascades. In warfare, the adoption of precision drones, networked sensors and algorithmic targeting promises surgical control, but often generates cascading failures—rogue feedback loops, unpredictable escalation, unintended collateral damage. Industrialisation too, with its mastery of throughput and efficiency, records remarkable gains in productivity and control—but the thermodynamic cost remains unavoidable: waste heat, unseen emissions, material degradation, ecological disruption. The specificity of the technological ground becomes the generative engine of systemic entropy.

In my study of technologically-mediated communications systems, I’ve seen this pattern repeat: we reduce variation in the centre, build finer interventions, optimise for the typical case, and assume we’ve mastered the field. Instead what shifts is the locus of instability—edges thicken, rare-cases multiply, delay hides in handoffs, trust erodes. Precision becomes a design variable: where do we admit fuzziness, where do we allow human latitude, where do we place the buffer? Because you cannot eliminate the entropic cost—you can only choose where it lands. Systems that pretend otherwise bury the risk; systems that name it, route it, and publish it stay human.