The geometries of change and the value of being human

I pass on and also archive for myself the following three ChatGPT 5.2 summaries of three recent Johar essays:

Summary of The Geometries of Change by Indy Johar

Core premise
Johar argues that every system of organisation—institutions, economies, governance—rests on an underlying “geometry,” meaning a structural logic that determines how change can occur, what is adjustable, and when transformation becomes disruptive rather than gradual. Geometry defines governability: what can evolve smoothly versus what requires rupture.

Linear geometry and its limits
Modern institutions are built around a linear model of change:

• A direction or goal is fixed first.

• Structures (roles, rules, incentives, infrastructure) are then aligned to that direction.

• Ongoing governance focuses mainly on speed and efficiency rather than revising direction.

Over time, this produces heavy path dependence. Investments, regulations, identities, and incentives lock systems onto a trajectory, making course correction costly and rare. When change finally occurs, it often comes through crisis, collapse, or replacement rather than continuous adaptation. Linear systems work in stable environments but become brittle under uncertainty and complexity.

The problem of contemporary conditions
Johar contends that the assumptions supporting linear organising—predictable futures, centralized authority, singular legitimacy—no longer hold. Today’s environment is marked by plural values, deep uncertainty, and systemic risks. Under these conditions, linear models accumulate commitments faster than they build adaptive capacity, narrowing the range of viable futures.

Helical geometry as an alternative
The essay proposes a “helical” model of change—spiraling through time rather than progressing in a straight line. In this geometry:

• Direction is not permanently fixed; it can be periodically re-negotiated.

• Institutional structures remain adjustable rather than locked to one trajectory.

• Change occurs through iterative cycles that preserve continuity while enabling reorientation.

The aim is to keep the future reachable: systems must allow for turning, not just acceleration. Helical organising supports learning, plural legitimacy, and ongoing adaptation instead of forcing transformation to occur through rupture.

Overall argument
Johar’s central claim is that the key question is not simply what actions to take, but what geometry of organising makes adaptive transformation possible. Linear models prioritize efficiency and stability but generate fragility in volatile contexts. A helical geometry—cyclical, revisable, and temporally layered—offers a framework for steering collective systems amid uncertainty without requiring breakdown as the mechanism of change.

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Here is a structured summary of “The Future of Being Human, Quietly Being Defined?” (Indy Johar, February 22 2026) based on the full essay:

1. Trigger and framing
The essay begins with a reference to Sam Altman’s remark about how much energy and time it takes to train a human compared with an AI model. Johar says the comment is superficially about energy fairness but structurally shifts the frame toward what counts as the unit of comparison in evaluating humans and machines.

2. Commensurability as a hinge
Johar distinguishes two kinds of “commensurability”:

• Descriptive, which measures energy and inputs across systems;

• Normative, which uses those measurements to justify comparisons and trade-offs.
  Altman’s claim, if read normatively, encourages interpreting humans and AI as functionally comparable capability systems. That framing quietly turns human beings into units of capability production.

3. Reduction of humans to capability outputs
Once humans are legible mainly in terms of cognitive capability as service output, several outcomes follow:

• Humans are considered substitutable if non-human systems can deliver similar outputs.

• Human value is recast in optimization terms: cost, throughput, reliability.

• Institutions begin organizing around procurement and compliance rather than intrinsic human worth.
  Johar calls this capability reductionism: a more refined but still reductive continuation of industrial labour reductionism that flattened humans into units of labour.

4. Compute-centric reference frames
If training becomes the shared frame, computing infrastructure becomes the reference class for intelligence and governance:

• Human education becomes “fine-tuning.”

• Civility and culture are reframed as priors in a cognitive pipeline.
  This shift influences what is measurable, fundable, normative, and thus shapes policies, welfare, schooling, and citizenship around capability output.

5. Structural fork in governance
Johar outlines two divergent models of governance that emerge from this framing:

1. Capability-first governance, where comparability and optimisation are central under constraint;

2. Intrinsic-life governance, where human dignity and irreducibility are first-order, non-tradeable commitments.
   He argues that if capability becomes the default grammar of society, human redundancy can become administratively rational without ever being declared explicitly.

6. Hierarchy of values
The essay proposes a normative ordering: rights first, capability second. Johar says that doesn’t mean rejecting metrics, but keeping them bounded within a framework that protects intrinsic human worth rather than letting efficiency metrics displace rights as constraints.

7. Core concern
The deeper issue isn’t whether training humans takes energy—it’s that if civilisation adopts a grammar defining humans primarily through capability and contribution, then optimising and replacing them becomes a rational endpoint. That is not just a labor-market calculation; it reshapes what it means to be human in governance and valuation systems.

Overall thesis
Johar’s essay warns that the emerging default comparison between humans and machine capabilities is not neutral. It quietly reshapes governance logic, reduces humans to tradable capability vectors, and opens a path where humans become redundant in an optimisation-driven system unless society explicitly protects intrinsic rights and dignity before metrics.

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Here is a structured summary of “The Value of Being Human” by Indy Johar (Feb 22, 2026):

1. Core philosophical choice
Johar identifies a foundational question beneath debates about AI, labour, and productivity: whether we conceive of humans as fixed bundles of capabilities or as open, developmental systems. This ontological framing — closed versus open — determines how value is understood and how institutions and policies are designed.

2. Closed ontology: humans as defined capability sets
In the dominant contemporary frame, humans are treated as collections of measurable functions (reasoning, creativity, coordination, etc.). Once human capacities are specified and benchmarked, comparison with machines becomes straightforward, and substitution decisions appear rational and objective. This reinforces a logic where humans are valued only for defined, quantifiable contributions.

3. Open ontology: humans as evolving trajectories
Johar contrasts this with the idea that humans are not static but evolving. Throughout history, major technological shifts (writing, printing, industrialisation, digital networks) have reshaped human cognition, behaviour, and capacities. Under transformative technologies like AI, future human capacities may emerge in ways that cannot be entirely predicted or pre-specified.

4. Dangers of governance by measurement
Measuring performance is not inherently flawed; the issue arises when measurable metrics become the primary basis for governance, allocation, and institutional incentives. When metrics become targets, systems reorganise around them, and what is measurable becomes what is rewarded. This exerts “selection pressure” that narrows the space of human development to what is legible and comparable.

5. Developmental compression and its risks
Treating humans as static inventories of capability risks “developmental compression,” where alternative developmental trajectories are under-supported or foreclosed entirely. Institutions optimising for present metrics may inadvertently narrow the range of future human capacities and forms of becoming.

6. Value of the unknown
Johar emphasises that unknown future capacities carry structural value. In contexts of deep uncertainty, preserving human developmental possibility (optionality) is a prudential imperative. Static valuation frameworks that assume completeness risk mispricing long-term potential.

7. AI’s role as selection pressure
AI itself does not dictate whether human capacities decline or expand; instead it introduces a selection pressure. Its effect on human development depends on the institutional frameworks in which it is embedded. AI can either amplify human development or compress it into narrow optimisation around measurable tasks.

8. Closed vs. open ontology: institutional implications

• Closed ontology: humans are defined, measurable, and replaceable; institutions orient toward substitution and optimisation.

• Open ontology: humans are emergent and partially unknowable; institutions should prioritise preserving developmental possibility over optimisation.

9. Central question re-framed
The key issue is not whether humans outperform machines at specific tasks, but whether we treat human nature as still emergent and indeterminate. Acceptance of a closed ontology leads logically to substitution and optimisation; acceptance of an open ontology implies designing systems that safeguard the conditions under which new human capacities can emerge in the future.

Summary thesis
Johar’s argument reframes the “value of being human” not in terms of current comparative performance with machines, but in terms of preserving human developmental potential. He warns that collapsing humans into static capability sets for measurement and optimisation risks narrowing the future of human becoming and misvaluing what is uncertain but potentially crucial.