PART I — THE INTELLIGENCE QUESTION
From Having Intelligence to Growing Intelligence
Chapter 1 — Why Grow Intelligent?
AI is becoming more capable. The more important question is: Are we?
For centuries, human beings built tools to extend their physical abilities.
The wheel extended movement.
The telescope extended sight.
The microscope extended vision into worlds too small for the naked eye.
The calculator extended our ability to perform computation.
The internet extended access to information.
Now we have created something different.
We have created tools that can participate in activities we normally associate with thinking.
Artificial intelligence can explain, summarize, translate, compare, classify, predict, generate, critique, brainstorm, write software, analyse documents and engage us in conversation.
That represents an extraordinary expansion of human capability.
But it also creates a question that deserves far more attention:
As our machines become more intelligent, what happens to us?
This book begins with that question.
1. The New Situation
Imagine a student in 1995 who does not understand photosynthesis.
She might open her textbook.
She might consult an encyclopedia.
She might ask her teacher the following day.
She might discuss it with a classmate.
Each route involves some effort.
Now imagine the same student today.
She can ask:
“Explain photosynthesis to me.”
Seconds later, she has an explanation.
If she still doesn't understand, she can ask:
“Explain it as if I were twelve.”
Then:
“Use an analogy.”
Then:
“Draw the process as five steps.”
Then:
“Quiz me.”
Then:
“Explain what I got wrong.”
This is not simply faster access to information.
It is something qualitatively different:
interactive access to generated explanation.
That can be enormously valuable for learning.
But the same student can also type:
“Write my answer about photosynthesis.”
The technology has not changed.
The relationship with the technology has.
In the first case, AI is being used to support learning.
In the second, it may simply be used to produce the required output.
This distinction will run throughout this book.
2. Having Intelligence Is Not Enough
We commonly speak about intelligence as though it were something we possess.
“She is intelligent.”
“He is very smart.”
“She has a high IQ.”
But everyday life demands more than possessing some level of cognitive ability.
We have to continually use and develop capabilities such as:
understanding unfamiliar information,
identifying patterns,
solving changing problems,
evaluating evidence,
questioning assumptions,
learning from mistakes,
adapting previous knowledge to new situations,
and making decisions under uncertainty.
These are not merely academic concerns.
They are capabilities required in ordinary adult life.
The OECD's 2023 Survey of Adult Skills assesses adults in literacy, numeracy and adaptive problem solving—the ability to work toward goals when circumstances and information can change. Across participating OECD countries and economies, 29% of adults were at the two lowest proficiency levels in adaptive problem solving. (OECD)
That statistic points toward something important.
Finishing our formal education does not mean that the development of our capabilities is finished.
Intelligence has to meet a changing world.
3. The World Does Not Stand Still
Consider someone who graduated as an engineer twenty-five years ago.
Much of the mathematics and science learned then may remain useful.
But the environment in which that engineer works has changed.
New materials have appeared.
Software has changed.
Design tools have changed.
Manufacturing has changed.
Data has become central to many engineering processes.
Automation has expanded.
And now AI is entering engineering workflows.
The degree did not suddenly become worthless.
But neither could the engineer reasonably say:
“I completed my education in 2001. Therefore, my learning is complete.”
The same applies to teachers, doctors, accountants, managers, lawyers, designers, entrepreneurs and almost every other profession.
The World Economic Forum's Future of Jobs Report 2025, based on its employer survey, places analytical thinking as the most frequently cited core skill, identified by 69% of surveyed employers. Creative thinking, technological literacy, curiosity and lifelong learning, and AI and big data also feature among the reported core skills. (World Economic Forum)
Technology changes.
Jobs change.
Problems change.
Knowledge changes.
Therefore:
The ability to continue learning becomes part of intelligence itself.
4. Then AI Arrived
Generative AI changes this situation again.
Previously, when we encountered something difficult, effort often stood between the question and the answer.
We searched.
We read.
We compared.
We calculated.
We drafted.
We revised.
We asked someone.
AI can now remove portions of that effort.
That is often wonderful.
Suppose a small-business owner receives a fifty-page government document explaining a new regulation.
AI may help summarize it.
A programmer may use AI to locate a bug.
A researcher may use AI to organize possible themes.
A teacher may use AI to generate examples for a difficult concept.
A manager may use AI to compare several proposals.
There is no virtue in performing unnecessary work simply because it used to be difficult.
But we should distinguish between two kinds of effort.
Effort that merely consumes time
and
Effort through which capability develops.
Removing the first can increase productivity.
Automatically removing the second may sometimes reduce opportunities for learning.
5. The Calculator Lesson
We have encountered versions of this problem before.
Consider calculators.
Suppose you need to know:
7,843 × 6,291
Using a calculator makes perfect sense.
There is little reason to spend several minutes performing long multiplication if the objective is simply to obtain the result.
Now consider a seven-year-old learning multiplication.
Giving the child a calculator for every multiplication exercise creates a different situation.
The same technology can be:
an intelligent extension of an existing capability
or
a substitute for developing that capability.
The difference is not inherent in the calculator.
It lies in why, when and how we use it.
AI magnifies this issue enormously because it can assist with much more than arithmetic.
It can participate in writing.
Reasoning.
Explaining.
Planning.
Coding.
Analysing.
Questioning.
Generating ideas.
These are activities through which human capability can itself develop.
6. Cognitive Offloading: Useful, but Not Free
Psychologists use the term cognitive offloading for situations in which we use external actions or tools to reduce demands on our own cognitive processes.
Writing a shopping list is a simple example.
Instead of remembering fifteen items, we store them on paper or a phone.
This is useful.
Human beings have always extended cognition through external tools: notebooks, calendars, maps, calculators, libraries and computers.
Research does not support the simplistic conclusion that cognitive offloading is inherently bad. A 2025 review in Nature Reviews Psychology describes real performance benefits from offloading memory demands, while also identifying possible costs and circumstances where dependence on external information can impair performance when that information becomes unavailable. (Nature)
So the lesson is not:
Never offload thinking.
That would be absurd.
The better question is:
What should I offload, and what should I continue exercising?
With AI, that question becomes central.
7. Ravi and the Monthly Report
Consider Ravi, a manager who produces a monthly performance report.
Before AI, preparing the report takes four hours.
He collects data.
He calculates changes.
He creates charts.
He writes descriptions.
He corrects grammar.
With AI and automation, the same process takes forty minutes.
Excellent.
Ravi has recovered more than three hours.
But what should he do with the saved cognitive capacity?
One possibility is simply to produce more reports.
Another is to spend the time asking better questions:
Why did this region outperform the others?
Is the improvement sustainable?
Which indicator might be misleading us?
What changed compared with six months ago?
What should we do next?
Now AI has not merely increased Ravi's productivity.
It has allowed him to move from:
report production
toward:
interpretation and judgment.
That is a much more interesting use of automation.
Use AI to remove lower-value cognitive load so that human attention can move toward higher-value thinking.
8. The Danger of the Beautiful Answer
There is another problem.
AI can produce answers that sound remarkably convincing.
Imagine asking AI:
“Why are customers leaving my company?”
It might produce five beautifully organized explanations.
Pricing.
Service quality.
Competitor activity.
Changing expectations.
Poor onboarding.
All are plausible.
None is necessarily the actual cause.
An inexperienced manager might mistake:
plausibility
for:
evidence.
An intelligent response therefore requires more than obtaining the answer.
The manager needs to ask:
What evidence supports each explanation?
What evidence contradicts it?
What information is missing?
What assumptions are being made?
How could we test them?
That is why growing intelligent in the AI era cannot simply mean learning how to prompt AI better.
It must also mean becoming better at:
questioning, evaluating, verifying and judging.
9. AI Can Be a Crutch — or a Gym
Think about physical exercise.
A machine can help lift a heavy object so that you do not strain yourself.
Wonderful.
But if your objective is to strengthen your muscles, eliminating all resistance defeats the purpose.
Something similar can happen cognitively.
Suppose you are learning programming.
You encounter an error.
Approach A
“Fix this code.”
AI generates corrected code.
You paste it.
It works.
Task completed.
Approach B
“Don't fix this yet. Give me one hint about what category of error I should investigate.”
You investigate.
Then:
“Here is what I think is wrong. Challenge my reasoning.”
Eventually you solve the problem.
The first interaction may optimize completion.
The second deliberately includes development.
Neither is universally correct.
If a production system has failed at 2:00 a.m., you may want the fastest reliable solution available.
If you are learning programming, however, preserving some productive struggle can be valuable.
Therefore, before using AI we should know which objective we are pursuing:
Do I need accomplishment right now, development right now—or both?
10. Does Intelligence Really “Grow”?
This question needs careful treatment.
The phrase Grow Intelligent should not be interpreted as a scientific claim that every dimension of intelligence can be increased without limit simply by adopting the right attitude.
Psychological research on “growth mindset” provides an instructive warning against exaggerated claims.
Some research has suggested benefits from growth-mindset interventions in particular populations and circumstances. Other systematic reviews and meta-analyses have found average effects on academic achievement to be very small, null under higher-quality evidence, or highly dependent on context and implementation. (PubMed)
Therefore this book uses grow intelligent in a broader and more practical sense.
Can you learn something you did not know yesterday?
Yes.
Can you become better at recognizing patterns in your profession?
Yes.
Can deliberate practice improve a skill?
Yes.
Can experience help you distinguish an important signal from irrelevant noise?
Often, yes.
Can you learn to ask better questions?
Yes.
Can you develop domain expertise?
Yes.
Can you improve your ability to use evidence, tools and feedback?
Yes.
Can you become better at a particular kind of problem solving through learning and practice?
Yes.
That is sufficient for our purpose.
Growing intelligent means deliberately expanding and refining the capabilities through which we understand, learn, judge, adapt and accomplish.
It does not require believing that human cognitive capacity is infinitely malleable.
11. AI Should Strengthen Human Agency
There is another reason why this matters.
Who decides?
AI can recommend.
AI can generate.
AI can predict.
AI can rank.
AI can simulate.
But human beings and human institutions still have to determine the goals toward which these capabilities are directed.
UNESCO's guidance on generative AI in education and research explicitly advocates a human-centred approach, including the development of human capacity and meaningful use of AI rather than treating technology as an end in itself. (UNESCO)
This principle extends far beyond education.
Consider:
“What is the fastest way to increase sales?”
That is a computationally useful question.
But intelligence also asks:
At what cost?
To whom?
Is it sustainable?
Is it fair?
Does it serve our larger objective?
Should we do it simply because we can?
Greater computational capability does not eliminate the need for human judgment.
It increases it.
12. From Answer Scarcity to Judgment Scarcity
For much of history, obtaining information was difficult.
Today, information is abundant.
AI takes us another step.
Generated answers are becoming abundant.
You can ask for ten ideas.
Then fifty.
Then another fifty.
You can generate five business strategies before breakfast.
The scarce resource therefore begins to shift.
When answers are scarce, obtaining an answer has great value.
When answers become abundant, something else becomes more valuable:
knowing which answer deserves attention.
That requires judgment.
Similarly:
When information is scarce, finding information matters.
When information is abundant, distinguishing reliable information matters.
When content is expensive to produce, producing content matters.
When content becomes cheap to produce, deciding what deserves to exist matters.
AI therefore does not make human intelligence irrelevant.
It changes where human intelligence is most valuable.
13. Three People, One AI
Imagine three people using exactly the same AI system.
Person 1: The Consumer
“Give me the answer.”
Receives it.
Uses it.
Moves on.
Person 2: The Collaborator
“Here is what I am trying to accomplish. Give me some options.”
Compares them.
Chooses one.
Acts.
Person 3: The Grower
“Here is what I currently think. Challenge it.”
“What pattern am I missing?”
“Give me an opposing interpretation.”
“What evidence should change my mind?”
“Ask me a question before answering.”
“Now let me explain what I learned.”
All three have access to the same artificial intelligence.
But they may emerge from the interaction differently.
The third person is trying to obtain two outputs:
the outcome
and
growth in capability.
That is the relationship this book seeks to develop.
14. A New Measure of Productivity
Suppose AI reduces a three-hour task to thirty minutes.
The conventional calculation is:
Time saved = 2½ hours.
That matters.
But there is another measurement worth considering.
After completing the task:
What can I now do that I could not do before?
Perhaps I understand the problem better.
Perhaps I discovered a new pattern.
Perhaps I learned a technique.
Perhaps I can identify a bad solution more quickly.
Perhaps I learned which questions matter.
Perhaps next time I can perform part of the task without assistance.
That gives us two returns from AI:
Return 1 — Accomplishment
What did AI help me produce or achieve?
Return 2 — Capability
What did the interaction help me become better able to do?
The most powerful AI relationship may deliver both.
15. Why Grow Intelligent?
Because the world keeps changing.
Because information is abundant but judgment remains difficult.
Because AI can generate answers faster than we can evaluate them.
Because decisions still have consequences.
Because expertise still matters.
Because context matters.
Because evidence matters.
Because values matter.
Because learning cannot stop when formal education ends.
And because handing every difficult cognitive task to AI would miss one of the greatest opportunities AI offers us.
We have gained access to a technology that can explain something differently when we don't understand.
Challenge an assumption.
Generate counterarguments.
Create practice problems.
Simulate scenarios.
Compare alternatives.
Question our reasoning.
Offer feedback.
Help reveal patterns.
And converse with us almost whenever we want.
Used carelessly, such technology could become an extraordinarily convenient answer machine.
Used deliberately, it can become something much more interesting:
an environment for developing human capability.
The First Principle
The first principle of Think and Grow Intelligent is therefore:
Don't measure your AI use only by what AI produces for you.
Measure it also by what using AI develops in you.
The goal is not to reject cognitive assistance.
It is not to compete with machines.
It is not to prove that humans can perform every task without technology.
Human civilization has always advanced by building tools that extend our capabilities.
The opportunity now is to make sure that our newest tools do more than become increasingly capable around us.
They should help us become increasingly capable with them.
Artificial intelligence will continue to develop.
The question for each of us is:
Will I merely have access to more intelligence—or will I grow more intelligent in the process?
That is where our journey begins.
One Idea to Take With You
Every time AI makes something easier, ask one additional question:
What higher-level thinking does this free me to do?
Don't merely save the effort.
Reinvest some of it in thinking.
That is the beginning of growing intelligent.
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