People Who Thrive in the AI Era: How to Build Productivity and Thinking Skills Together
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As AI makes intellectual work faster and cheaper, the crucial difference may lie less in intelligence than in the willingness to keep exploring difficult problems and verify the results. Using AI as a tutor, critic, and research assistant rather than as a stand-in for drafting can enhance both productivity and human capabilities.

- Because the time saved by AI may be filled with additional work rather than rest, increased productivity and a reduced workload are not the same thing.
- The need for cognition is not the same as intelligence; it refers to the tendency to voluntarily engage in and enjoy complex thinking activities.
- If AI is entrusted with defining problems and making judgments, short-term output may increase, but knowledge formation, error detection, and independent problem-solving abilities may weaken.
- Creating your own draft first and then asking AI to find counterexamples, errors, and omissions makes it easier to retain control of the thinking process.
- Schools and organizations should assess autonomy, competence, relatedness, verification processes, and the ability to work independently, rather than focusing only on the amount of AI use.

Generative AI is rapidly lowering the cost of intellectual tasks such as summarization, translation, writing, analysis, and coding. Yet as the ability to produce answers becomes abundant, the capabilities humans need do not disappear. Rather, the value of the ability to decide what to ask, verify answers, and pursue difficult problems to the end may increase.
The central point of this article is not that we should use AI less. It proposes that we use AI while considering not only short-term output but also how it affects users’ knowledge, judgment, curiosity, and ability to perform independently.

AI Changes Work Density More Than Working Hours
Even if AI reduces the time required for a task, we cannot assume that the total amount of work in a day will decrease at the same rate. Organizations may fill the time saved with more emails, analyses, reports, code revisions, and meetings. Individuals may also begin handling small tasks themselves that they would previously have abandoned or outsourced.
The following changes may occur:

The number of tasks completed in the same amount of time increases.
People simultaneously supervise the results of multiple AI tools or agents.
Short tasks spill over into evenings, travel time, and weekends.
More context switching fragments periods of deep concentration.
Output increases, but so does the volume of work that must be reviewed.

Productivity, therefore, should not be measured simply as the “ability to finish faster.” Time spent correcting errors, the number of task switches, the ability to perform independently, fatigue, and recovery should all be considered. The extent to which AI increases work density varies by role, organizational culture, and performance standards, so this phenomenon does not affect all workers equally.

Why Volition Matters More as Intelligence Becomes Abundant
AI already provides fluent drafts and plausible analyses instantly in many fields. In this environment, it becomes difficult to judge a person’s understanding or expertise based only on the appearance of the output. Differences emerge in the following process:

Select a problem worth solving.
Define the problem’s conditions and success criteria independently.
Verify the facts, logic, and sources in AI responses.
Explore counterexamples and alternative explanations.
Ensure that a person takes responsibility for the conclusion.
Explain or reproduce the core content even without AI.

This attitude is related to the psychological concept of need for cognition. Need for cognition refers to a relatively stable tendency to engage in and enjoy complex thinking activities. It is not the same concept as ability measured by intelligence tests. A person with strong reasoning ability may avoid mental effort, while someone of average ability may review and learn persistently.
A high need for cognition does not always lead to the correct conclusion. People who have thought deeply may become strongly attached to the explanations they have created. Persistent thinking must therefore be accompanied by falsifiability, external review, and a willingness to revise one’s thinking.

Three Conceptual Types for Understanding AI Users
The following classification is not a validated personality test or clinical diagnosis, but a conceptual model for explaining AI usage habits. The same person may behave as different types depending on the task and situation.

Type
How They Use AI
Short-Term Effect
Long-Term Risk or Opportunity

Productive passenger
Broadly delegates everything from problem definition to final expression to AI
Output and speed increase
Internal knowledge and the ability to perform independently may weaken

Reluctant optimizer
Tries to think independently but gradually delegates because of deadlines and fatigue
Reduces the burden and meets deadlines
If convenience becomes the default habit, the verification stage may shrink

Mental marathoner
Thinks first and asks AI for hints, counterexamples, and review
Initial speed may be slower
May improve productivity, learning, and judgment together

The key distinction is not whether AI is used. What matters is which parts of the thinking process were delegated and what the person came to understand after using it.

Productive Passengers and Cognitive Debt
Even if an AI-generated result works properly for now, future maintenance costs may accumulate if the user does not understand its internal structure. The term “cognitive debt” can be used to describe this.
For example, if a developer continually merges AI-generated code without fully understanding it, the following problems may arise:

It becomes difficult to narrow down the cause when an error occurs.
They cannot explain security or performance assumptions.
It becomes difficult to modify the structure to meet new requirements.
They lack the criteria needed to determine whether an AI response is wrong.
The entire team may become unaware of how the system works.

Cognitive debt is related to technical debt, but the two concepts are not identical. Technical debt primarily refers to future costs arising from system design and implementation. Cognitive debt focuses on a condition in which a person or organization has not sufficiently developed the ability to understand and make judgments about that system.
One might argue that the problem can be solved by having AI analyze it again. However, evaluating the analysis and comparing it with the actual system still requires domain knowledge and judgment. The more important the system, the more necessary it is for people to understand who is responsible, the key assumptions, testing methods, and manual recovery procedures.

How AI Dependence Can Weaken Learning

Desirable Difficulty Disappears
Learning requires retrieving information from memory, forming hypotheses, and correcting failures. If AI provides a complete answer from the outset, users can skip this process. The feeling of understanding something by reading the output is not the same as the ability to reproduce it independently from a blank page.

Fluency Is Mistaken for Accuracy
Generative AI can express incorrect information in natural and confident language. If users lack knowledge of the field or verification procedures, they can easily mistake fluency of expression for strength of evidence.

Curiosity and the Scope of Exploration Decline
Educational research has suggested that directly teaching the prescribed use of an object can limit free exploration. This cannot be generalized directly to AI, but it suggests that if a chatbot always presents one method and conclusion immediately, users may have fewer opportunities to test other possibilities.

Easy Work Becomes the New Standard
When tasks performed with AI are extremely fast and smooth, tasks done without AI may feel relatively slow and tedious. If this happens repeatedly, people may spend less time grappling with uncertain problems and become less motivated to begin independently. However, AI’s effect on motivation varies according to the type of task, user experience, tool design, and evaluation method, so it should not be interpreted as a uniform causal relationship.

Cautions When Reading Research Findings
Research on AI and human thought is growing rapidly, but in many areas it is still too early to draw long-term conclusions.

Self-report surveys may fail to distinguish between the effort users feel they have made and actual changes in ability.
Results from short experiments cannot be assumed to translate directly into years of learning or changes in professional capabilities.
A single physiological indicator, such as EEG connectivity, cannot by itself establish the quality of learning or “declining brain function.”
Even when a correlation is found between frequent AI use and lower critical thinking, it is difficult to determine which one is the cause.
Results may vary depending on tool performance, prompts, task difficulty, and participants’ prior knowledge.

The conclusion that “using AI inevitably weakens thinking ability” is therefore excessive. A more accurate question is: In which tasks, in what ways, and to what extent does AI use change which abilities in which people?

A Verification Process for Avoiding Cognitive Surrender
“Cognitive surrender” is a metaphorical expression describing a state in which people prioritize AI’s answers over their own judgment without verification. It is not a standardized psychological diagnosis.
Important responses can be reviewed through the following process:

Separate the claims. Distinguish facts, inferences, predictions, and value judgments.

Check the evidence. Read the original source to confirm that it actually exists and supports the claim.

Check the date and scope of applicability. Determine whether the information is current and whether material from another country or set of conditions has been misapplied.

Request counterexamples. Ask AI to generate opposing arguments, then have a person evaluate their validity.

Calculate independently. Reproduce numbers, code, and logical conclusions using a separate method.

Designate a responsible person. Decisions related to healthcare, law, finance, hiring, and safety should be reviewed by a qualified person.

AI risk management is not merely the task of checking output accuracy. It must also specify who reviews the output, how errors will be detected, and who is responsible for the final decision.

How to Use AI as a Mental Training Tool

1. Ask for Step-by-Step Hints Rather Than a Complete Answer
You can make a request like this:

Do not give me the correct answer immediately. Give me only the necessary concepts and the first hint. After I answer, point out any logical errors and provide the next hints one at a time.

A hint-based approach leaves room for users to retrieve memories and form hypotheses. However, in a field where they have no foundational knowledge, they should first consult reliable introductory materials and identify the key terms.

2. Face the Blank Page First
Before opening AI, briefly write down the following:

The problem I am trying to solve
The facts I currently know
My tentative conclusion
Areas of uncertainty
Expected counterarguments
Sources that need to be checked

Then ask AI to identify missing perspectives, counterexamples, logical leaps, and items requiring verification. This lets AI critique a structure created by the user instead of determining the starting point of the thought process.

3. Alternate Between AI and Non-AI Tasks

After using AI to summarize materials, check the originals and write the outline yourself.
After viewing code examples with AI, close the screen and implement the core functionality from scratch.
Ask AI for counterarguments, but write the final conclusion in your own words.
Analyze the cause of a failure first, then compare it with AI’s diagnosis.
Explain to someone else, without tools, what you learned with AI.

This approach is useful for distinguishing “performance achieved with assistance” from “ability actually acquired.”

4. Configure the Chatbot as a Tutor, Not an Answer Machine
A good AI tutor checks the user’s current level through questions, presents one problem at a time, and does not reveal the complete answer immediately after an incorrect response. The following prompt can be used:

Act as a tutor teaching this topic. First, ask me one question to assess my level. If I am wrong, do not give me the correct answer immediately; tell me which premise or concept I need to reconsider. At the end of each step, have me explain it in my own words.

5. Distinguish Tasks to Delegate from Tasks to Perform Yourself

Tasks Easily Delegated to AI
Tasks That Should Be Led by People

Format conversion and spelling checks
Defining problems and goals

Initial organization of repetitive documents
Important value judgments

Routine data classification
Setting strategy and priorities

Drafting functional emails
Writing in which personal experience and perspective are central

Drafting repetitive code
Final decisions involving safety, law, healthcare, and finance

Exploratory summaries of lengthy materials
Evidence verification and accountable approval

The distinction is not simply between creative and non-creative work. The question should be: Will directly performing this process develop the judgment needed in the future?

6. Request an Intellectual Map Rather Than a Conclusion
Instead of asking AI directly “what is right,” request the following:

Major scholars and theories that have studied the issue
Competing explanations
Representative counterarguments
Primary sources to consult
Key assumptions behind each perspective
Issues on which no consensus has yet been reached

AI can be used like a research assistant or librarian, but whether the authors, papers, and quotations it presents actually exist must be verified separately.

AI Entropy and Human Uniqueness
“AI entropy” is less a standardized technical term than a metaphor describing the tendency of large volumes of AI output to converge toward average and similar forms. Because generative models create outputs based on patterns that frequently appear in training data, ordinary prompts are likely to produce familiar structures and conventional expressions.
People can resist this leveling effect by adding the following elements:

Specific facts observed firsthand
Personal memories and failures
Tacit knowledge gained in the field
Clear value criteria and reasons for their choices
Points of disagreement with existing explanations
The process and limitations involved in verifying the results

Human uniqueness does not arise merely from typing the sentences oneself. It comes from what people choose and take responsibility for, based on their experiences and standards.

Environments That Schools and Organizations Should Design
Education in the age of AI should not abandon the transmission of knowledge, but expand it to include the ability to apply and verify knowledge. Without foundational knowledge, it is difficult to detect AI errors or formulate good questions.
Self-determination theory identifies autonomy, competence, and relatedness as key conditions for understanding sustained motivation and well-being.

Condition
Meaning
Example of Design in an AI Environment

Autonomy
Feeling that one has choices regarding goals and methods
Let learners determine their research questions and the scope of AI use

Competence
Feeling that one’s abilities improve through practice
Provide appropriately challenging tasks, step-by-step feedback, and opportunities to try again

Relatedness
Feeling connected to and respected by others
Include mentor review, peer discussion, and collaborative problem-solving

Schools and organizations can evaluate the following items together:

Can the person explain core concepts without AI?
Did they disclose the materials used and the verification process?
Do they revise their conclusion when opposing evidence emerges?
Did they appropriately indicate uncertainty?
Did they complete a complex task sustained over a long period?
Did they critically review and improve the work of peers?

Apprenticeship relationships are also important. Experts convey not only skills but also criteria for selecting good problems, methods for interpreting failure, a sense of how to judge quality, and professional responsibility. This tacit knowledge is difficult to learn by receiving only finished answers.

Operating Principles for Reducing Cognitive Polarization
AI can serve as a springboard for tackling more difficult problems on one side, and as a means of skipping thought on the other. If this difference accumulates, it may lead to disparities not only in output but also in judgment and autonomy.
Individuals and organizations can reduce the risks through the following principles:

Record which stages of thinking were delegated, rather than merely whether AI was used.
Establish human review points and approvers for important tasks.
Regularly perform reproduction tasks without AI.
Include time for checking sources and exploring counterexamples in the performance process.
Measure not only speed but also error rates, correction costs, and learning outcomes.
Protect focused time and long-term projects that do not require AI use.
Have experts review not only novices’ results but also their thinking processes.

Conclusion
The people who thrive in the age of AI will not necessarily be those who use AI the most or the least. They will be those who reduce repetitive work with AI without giving up problem definition, verification, value judgments, and responsibility.
AI can calculate, combine, and predict possible answers. But deciding which goals to pursue, what to consider important, and which difficulties to endure remains the responsibility of humans and human communities. There is no need to choose between productivity and growth. By developing your own thoughts first and positioning AI as a critic, tutor, and research assistant, you can pursue both goals together.

FAQ
Q. Does using AI extensively necessarily reduce critical thinking skills?
A. Not necessarily. The impact varies depending on the task, how AI is used, prior knowledge, and verification procedures. Letting AI handle both problem definition and conclusions may reduce cognitive effort, but creating your own draft first and then asking AI to find counterexamples and errors can make it a tool that supports critical thinking.
Q. Does a high need for cognition mean high intelligence?
A. No. Need for cognition is the tendency to engage in and enjoy complex thinking, and it is distinct from intelligence, which refers to problem-solving ability itself. Even highly intelligent people may avoid mental effort, while people of average ability may think persistently.
Q. Is a mental marathoner someone who does not use AI?
A. No. A mental marathoner is a conceptual term for someone who uses AI while retaining control over problem definition, core reasoning, verification, and final judgment. A typical approach is to request hints, counterexamples, sources, and feedback rather than completed answers.
Q. How can I maintain the benefits of learning while using AI?
A. Before opening AI, it is best to write your own answer or hypothesis first, and after receiving help, try to reproduce the key content without the tool. Compare AI summaries against the original text, reimplement code yourself, and when your answer is wrong, ask for step-by-step hints rather than the correct answer.
Q. What is cognitive debt?
A. Cognitive debt is a term describing a state in which AI-generated code or documents work for the time being, but future decision-making and revision costs increase because the person responsible does not sufficiently understand the underlying principles and key assumptions. Rather than a widely standardized academic metric, it is closer to a concept used to explain the organizational risks of AI dependence.
Q. What kinds of tasks are appropriate to delegate to AI?
A. Routine and easily verifiable tasks, such as format conversion, typo checking, repetitive data classification, and initial organization, are well suited for delegation. By contrast, humans should take the lead in problem definition, strategy, value judgments, creative work where personal perspective matters, and final decisions related to safety, law, medicine, and finance.
Q. How should sources provided by AI be verified?
A. You should confirm that the author, title, publisher, date, and URL actually exist and read the original source yourself. Even if the source exists, you should separately examine whether the relevant passage actually supports AI's claim and whether the study population and conditions apply to the current issue.
Q. Should schools reduce knowledge instruction in the age of AI?
A. Foundational knowledge is, in fact, necessary for verifying AI output and formulating good questions. However, rather than evaluating only memorization and the production of correct answers, schools should also assess long-term projects, the search for counterexamples, source verification, explanation and reproduction without AI, and the process of revising work after failure.
Q. Is AI entropy a scientifically established term?
A. In this article, AI entropy is a metaphor for the phenomenon in which large volumes of AI output converge on familiar, average forms of expression. It should be distinguished from the standard concept involving the rigorous measurement of entropy in information theory.
Q. Should I set aside time to work without AI?
A. It is not necessary for every task, but regular non-AI assignments are useful for assessing core abilities. Explaining what you learned with AI without using the tool, rewriting key code, and independently reaching conclusions can help distinguish actual learning from tool dependence.

Sources
- The Need for Cognition: https://doi.org/10.1037/0022-3514.42.1.116
- Self-Determination Theory: https://selfdeterminationtheory.org/theory/
- The Double-Edged Sword of Pedagogy: Instruction Limits Spontaneous Exploration and Discovery: https://doi.org/10.1016/j.cognition.2010.10.001
- The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers: https://doi.org/10.1145/3706598.3713778
- Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task: https://arxiv.org/abs/2506.08872
- NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework

Images
- Person navigating a complex maze with geometric shapes, an AI robot, analysis icons, and scales: https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NDM4NywicHVyIjoiYmxvYl9pZCJ9fQ==--d2355873076dcf1540d4c7c21227550833e52723/ai-d37dd98a.webp
- Person walking through a garden toward an AI robot beside an automated document conveyor and clock: https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6NDM5MywicHVyIjoiYmxvYl9pZCJ9fQ==--a9fe146f2c1652668777c2dc72d67318d8e62dfc/ai-b140cfe1.webp

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Category: AI Data
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