Why the Harari Narrative Is Comforting — and Wrong

Yuval Noah Harari has become one of the most effective interpreters of technological anxiety for the educated public. Not because he explains technology with technical precision, but because he translates fear into something emotionally manageable.

His core message is now familiar: technology evolves faster than institutions, artificial intelligence will surpass human decision-making, control will migrate to machines, and humanity must prepare for a loss of agency.

It sounds bleak. It is, paradoxically, comforting.

Because it relocates responsibility away from the present.

This text is not primarily about Harari as an individual. It is about the narrative he articulates particularly well, and that many institutions are eager to adopt. Which is convenient, because it allows admiration without obligation.

The seduction of inevitability

The Harari-style narrative frames AI as an external force. Something that happens to us. A geological process rather than a political one.

If AI is inevitable, then:

• no one chose this trajectory
• no one is accountable for its outcomes
• resistance becomes naïve rather than moral

Loss of control becomes fate. Delegation becomes prudence. Abdication becomes wisdom.

This is not a side effect of the narrative. It is its core utility.

Inevitability dissolves responsibility more effectively than any conspiracy ever could.

What the narrative carefully avoids

What rarely appears in this framing is the dull, uncomfortable truth:

AI does not arrive with goals or values of its own. Those are supplied by humans.

This does not mean AI systems are simple executors of human intent. Modern machine-learning systems routinely discover correlations, strategies, and internal representations that their designers did not explicitly foresee. They can behave in ways that surprise, confuse, and even outperform their creators within defined domains.

But this is not autonomy in the moral or political sense.

Every AI system still operates within boundaries that humans define:

• objectives selected by institutions
• constraints encoded in loss functions and training regimes
• trade-offs approved by management
• incentives shaped by markets and power

Emergent behavior expands how a system reaches outcomes. It does not choose which outcomes matter.

There is no autonomous drift toward machine rule. There is human choice, layered, distributed, and then obscured.

It is far easier to say “the algorithm decided” than to say “we defined the objective, accepted the trade-offs, and deployed the system anyway.”

A concrete example: credit scoring

Consider a modern credit-scoring system.

Humans decide:

• that risk minimization outweighs access maximization
• that repayment probability is the primary objective
• that historical data is an acceptable proxy for future behavior
• that fairness constraints will be limited to what is legally required

Engineers design:

• model architectures
• feature selection pipelines
• training and validation regimes

Management approves:

• deployment thresholds
• acceptable error rates
• appeal mechanisms (or their absence)

The model then produces scores that no single human can fully explain at the individual level. This opacity is real. Causality becomes distributed. No one can trace the full chain from data to outcome.

But this does not eliminate responsibility.

The moral decision was made before the model ran: which objectives mattered, which harms were tolerable, and which populations would absorb error.

When a loan is denied, the language shifts:

“The system flagged the applicant.”

AI absorbs the blame. Human judgment disappears from view.

Responsibility is being automated faster than decision-making.

From tragedy to alibi

Classical tragedy involved hubris followed by accountability. The modern version removes both.

“We had no choice.”
“The system demanded it.”
“The model recommended it.”

This language now governs hiring, credit, content moderation, predictive policing, and warfare.

AI does not remove human control. It absorbs human blame.

That is why it spreads. Not because it is intelligent, but because it is useful.

The strongest objection — and where it fails

Harari could reasonably respond: once systems become sufficiently complex and interconnected, no one can trace the causal chain. Control is not maliciously abandoned; it is emergently lost.

This is partially true — and ultimately insufficient.

Complexity can make control difficult. It does not make responsibility meaningless. Distributed control is not the same as moral vacuum.

When no single agent can oversee the whole system, responsibility does not vanish. It must be reconstructed institutionally. The failure is not that causality is complex, but that societies accept complexity as an excuse to stop assigning accountability.

Emergence explains difficulty. It does not justify abdication.

The real danger is smaller, duller, and closer

The risk is not superintelligent machines ruling humans. The risk is that humans will continue to rule while pretending they no longer do.

AI will not abolish power. It will launder it.

Behind every “objective” system sits:

• a target someone approved
• a constraint someone accepted
• a trade-off someone signed

The language of inevitability ensures that no one feels responsible for these decisions.

Looking back at the steering wheel

History offers no shortage of examples where responsibility dissolved into process:

• late Roman bureaucratic fragmentation
• early modern colonial administrations governed “by procedure”
• industrial disasters justified by compliance with standards rather than judgment

These systems did not fail because no one was in charge. They failed because being in charge became invisible.

Judgment is slow, visible, and risky. Optimization is fast, clean, and deniable. AI wins not because it is superior, but because it allows decisions without ownership.

Final note

The Harari narrative comforts because it tells us we are passengers. Passengers who still enjoy arguing about the route, as long as no one asks them to drive.

The truth is harsher: we are still driving.

Looking at the steering wheel today means:

• identifying who defines objectives
• naming who approves trade-offs
• refusing inevitability language
• rebuilding responsibility even when causality is distributed

AI is not destiny. It is an alibi.

And civilizations that mistake alibis for inevitabilities do not collapse dramatically. They drift — efficiently, procedurally, and without anyone clearly at fault.