Indy Johar recently challenged conventional approaches to change and human worth, urging a shift from rigid frameworks to more fluid ones as artificial intelligence reshapes society.
Why Linear Models Breed Fragility
Why Linear Models Breed Fragility (Image Credits: Unsplash)
Institutions today often followed a straight-line path toward predefined goals, aligning rules, incentives, and infrastructure accordingly. This approach prioritized speed and efficiency once the direction was set. However, it created heavy path dependence, where past commitments made redirection expensive or impossible.
Crises typically forced abrupt shifts rather than smooth evolution. In stable times, linear systems thrived, but uncertainty exposed their brittleness. Johar highlighted how such models accumulated rigidities faster than adaptive capacities, especially amid plural values and systemic risks.
- Fixed goals lock in early choices.
- Governance emphasizes acceleration over reevaluation.
- Transformation demands rupture, not iteration.
Reducing Humans to Measurable Outputs
Comparisons between human training and AI development masked deeper issues. Remarks on energy costs for humans versus machines shifted focus to capability production. This normative lens treated people as substitutable units, valued by cost, throughput, and reliability.
Institutions then organized around procurement rather than inherent dignity. Johar termed this capability reductionism, an evolution of industrial labor views. Compute-centric frames recast education as fine-tuning and culture as inputs in a pipeline. Metrics dominated policies on welfare and citizenship.
Embracing Helical Change and Open Potential
Johar proposed helical geometry, where systems spiraled through iterative cycles. Directions underwent periodic renegotiation, structures stayed flexible, and continuity paired with reorientation. This preserved options amid volatility, favoring learning over locked trajectories.
Similarly, humans embodied open ontologies – evolving trajectories shaped by technologies like writing or digital networks. AI introduced selection pressures, but outcomes hinged on institutions. Static metrics risked developmental compression, narrowing future capacities. Preserving unknown potentials became essential in uncertain times.
| Linear/Closed | Helical/Open |
|---|---|
| Rigid paths, fixed capabilities | Iterative cycles, emergent potentials |
| Crisis-driven shifts | Continuous adaptation |
| Optimization first | Dignity and optionality prioritized |
Divergent Roads for Governance
Two models emerged: capability-first governance optimized under constraints, treating humans as replaceable. Intrinsic-life governance placed dignity as non-negotiable, bounding metrics within rights-based frames. Johar warned that defaulting to capability logic rationalized redundancy without fanfare.
Historical shifts expanded human cognition; AI could do the same or constrain it. Institutions faced a choice: amplify development or funnel it into legible tasks. Rights must precede capabilities to avoid reshaping humanity through efficiency alone.
Key Takeaways
- Linear structures falter in uncertainty; helical ones enable turning without collapse.
- Viewing humans as fixed outputs invites substitution; open views safeguard evolution.
- Governance must elevate intrinsic worth over metrics to protect future potentials.
Johar’s essays, published on The Geometries of Change, The Future of Being Human, and The Value of Being Human, frame a pivotal shift. Societies that adopt adaptive geometries and affirm human emergence will navigate AI’s disruptions resiliently. What governance model will define our trajectory? Share your thoughts in the comments.
