Human Factors and Work Ergonomics for AI Use

Why AI should extend human intelligence without replacing the faculties for which we remain accountable.

1. Humans Build Tools to Extend Themselves

Steve Jobs described the computer as a “bicycle for our minds.”

His point was bigger than computing.

Human beings are tool builders.

A stick extends the hand. A wheel extends the foot. A telescope extends sight. A telephone extends hearing. Writing extends memory. A calculator extends arithmetic. Computers extended our ability to manipulate information.

AI extends something still more intimate:

the reach of cognition itself.

A person may compare ten documents carefully. AI can compare hundreds. A person may generate five hypotheses. AI can generate fifty. A specialist may know one field deeply; AI can rapidly expose that specialist to concepts from several others.

Jobs’s bicycle metaphor captures the right relationship. The bicycle amplifies the cyclist. It does not become the cyclist. 

This also helps explain why parameter-count comparisons with the brain need care.

Large AI systems are moving into the trillion-parameter era; research literature already discusses architectures exceeding a trillion parameters. But an AI parameter is not equivalent to a biological synapse. The two systems learn, store information and compute in profoundly different ways. 

The human brain contains roughly 86 billion neurons and estimates on the order of 100 trillion synaptic connections, with some literature giving ranges reaching toward one quadrillion. Those connections are dynamic, chemically heterogeneous, embedded in a living body and continuously altered through development and experience.

So even if an AI architecture someday contains 10 trillion parameters, it would be misleading to say:

10 trillion AI parameters versus 100 trillion human “parameters”.

They are not the same unit.

The more useful lesson is that two radically different architectures can produce overlapping capabilities.

Edsger Dijkstra’s famous analogy applies: asking whether computers think is like asking whether submarines swim.

A submarine and a fish both move through water.

They do not do it the same way.

AI is exceptionally strong at scale, speed, retrieval, pattern recognition and repetition.

Humans possess something different: embodied experience, social context, motivations, values and responsibility.

AI can optimise an objective.

Humans still have to decide whether the objective deserves optimisation.

That makes AI an ergonomics problem.

Traditional ergonomics asks:

What does this workstation do to my body after eight hours?

What does this workflow do to my mind after eight years?

A 2025 CHI study of 319 knowledge workers found that greater confidence in generative AI was associated with less self-reported critical-thinking effort. AI also shifted cognitive work toward checking and integrating machine output. The danger is therefore not simply machines becoming more capable. It is humans becoming less cognitively engaged with what the machines produce.

The design question is:

Which burdens should AI remove—and which human faculties should become stronger because AI exists?

2. The Fish-Hook Problem: The Most Human Answer May Be to Reject the Question

Consider this prompt:

I........

© The Times of Israel (Blogs)