Podcast with Riccardo Sanz on machine consciousness and control engineering

What happens when engineered systems become too complex for humans to understand, let alone control? Riccardo Sanz argues that the path forward requires machines capable of controlling themselves , and that this leads, perhaps inevitably, toward machine self-awareness. Subscribe for more from the Convergent Science Network podcast series. Riccardo Sanz approaches consciousness not from philosophy or neuroscience, but from the hard edge of control engineering. In this interview, he explains why traditional control theory breaks down when the controller itself becomes so complex that it can fail in ways no human operator can diagnose. Modern countrywide electrical grids, flight control systems, and computing infrastructures already exceed human comprehension during failure states , leading to blackouts, crashes, and cascading breakdowns. Sanz's provocative claim is that the only scalable solution is to give these systems the capacity to model and manage themselves. This is not, he insists, an attempt to mimic human consciousness. Instead, his research group arrived at concepts of self-awareness and self-modeling from purely technical requirements for robust, adaptive control. The convergence with consciousness research was discovered after the fact, when they found that the competences they needed, self-monitoring, self-repair, cognitive flexibility, overlapped with properties that consciousness researchers attribute to sentient systems. The distinction matters: Sanz argues that copying the human brain would reproduce its evolutionary limitations, whereas extracting the underlying principles of self-awareness could yield systems that far exceed human capabilities in speed and information integration. The conversation probes the risk of infinite regress , if a controller needs a meta-controller, what controls that? Sanz proposes that each successive layer of self-representation compresses complexity, collapsing into increasingly compact models until the system converges on a unified self-description. He draws parallels to industrial process control, where hierarchies of control loops ultimately reduce to a single variable like profitability, but notes that current systems lack the self-awareness to handle their own failures. On the question of existential risk from superintelligent machines, Sanz is sanguine. He believes that by the time engineering reaches the sophistication needed to create deeply self-aware systems, the technology for bounding their behavior will be equally mature. His core message is a call for rigor: the fragmentation of control engineering, neuroscience, and philosophy into separate communities with incompatible vocabularies is the real barrier to progress.