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Moravec’s Paradox: What It Means for Engineers, AI, and Our Kids

Moravec’s Paradox: What It Means for Engineers, AI, and Our Kids

Artificial intelligence keeps surprising us.

It writes code, trades stocks, beats world champions at chess — yet struggles with things a toddler does effortlessly: recognizing context, moving in the physical world, or understanding common sense.

This contradiction has a name.


What is Moravec’s Paradox?

Moravec’s Paradox states:

Tasks that are easy for humans are hard for computers, and tasks that are hard for humans are often easy for computers.

In simple terms:

  • Computers excel at logic, calculations, and formal rules

  • Humans excel at perception, intuition, movement, and social understanding

This feels backwards — and that’s why it’s called a paradox.

Why is it called “Moravec’s” Paradox?

The concept was articulated in the 1980s by Hans Moravec, a robotics researcher at Carnegie Mellon University.

At the time, most AI researchers believed that:

Once we solve high-level reasoning (chess, math, logic), everything else will be easy.

Moravec noticed the opposite:

  • Chess and math were solved relatively quickly

  • Vision, motor control, and common sense turned out to be brutally hard

His key insight combined AI with evolutionary biology.

The evolutionary explanation

Human abilities did not evolve equally.

  • Perception and motor skills evolved over hundreds of millions of years

  • Abstract reasoning (math, logic, formal thinking) evolved very recently

What feels effortless to us is often deeply optimized by evolution — and therefore extremely complex.

What feels hard to us is often easier to formalize in code.

Evolution already paid the computational cost. Computers have not.

Classic examples

TaskHumansComputers
Multiply large numbersHardEasy
Chess calculationsHardEasy
Recognize a faceEasyHistorically very hard
Walk on uneven groundEasyVery hard
Understand sarcasmEasyExtremely hard

Even today, with deep learning, the paradox still holds — just less obviously.

Moravec’s Paradox in modern AI

Large language models can:

  • Write code

  • Explain finance

  • Pass exams

Yet they still:

  • Hallucinate confidently

  • Lack real-world grounding

  • Struggle with responsibility and accountability

AI is powerful in abstract symbol manipulation, but weak in embodied understanding.

This distinction matters — a lot.

What this means for software engineers

Moravec’s Paradox is not a threat — it’s a filter.

What is being commoditized

  • CRUD-heavy applications

  • Simple APIs

  • Boilerplate frontend and backend code

  • Glue code without domain depth

AI is getting very good here.

What remains valuable

  • System design and architecture

  • Distributed systems and performance

  • Low-latency and high-reliability systems

  • Regulated domains (finance, trading, risk)

  • Ownership, judgment, and accountability

The closer your work is to real-world consequences, the safer it is.

The winning position

Not “AI engineer” vs “software engineer”, but:

Engineers who use AI to build systems that AI alone cannot be trusted to run.

AI becomes leverage, not replacement.

A 5-year career hedge (high level)

  1. Short term: become AI-augmented, not AI-dependent

  2. Mid term: combine AI with deep domain expertise

  3. Long term: move toward ownership, architecture, and decision-making

Pure abstraction gets cheaper.
Judgment gets more expensive.

What Moravec’s Paradox means for our kids

This may be the most important part.

What not to over-optimize

  • Memorization

  • Narrow technical skills

  • Passive consumption

These are easy to automate.

What will matter more

  • Critical thinking

  • Creativity

  • Communication

  • Emotional intelligence

  • Physical skills and coordination

  • Curiosity and adaptability

Ironically, the most human skills are the most future-proof.

Coding still matters — but as a thinking tool, not a guaranteed career.

The bigger shift

Old model:

Learn a skill → use it for 30 years

New model:

Learn how to learn, adapt, and combine domains

Moravec’s Paradox reminds us that intelligence is not just computation — it is context, embodiment, and responsibility.

Final takeaway

AI will keep getting better at what computers are good at.

Humans should double down on what evolution already made rare.

The future belongs to hybrids — not pure technologists, and not pure creatives, but people who can connect judgment, systems, and meaning.

Moravec’s Paradox isn’t bad news.

It’s a map.

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