What Happens When Intelligence Changes Faster Than Our Ability to Understand Ourselves?

How AI can make us more productive while making it harder to recognize what we truly understand.

When intelligence advances faster than our ability to understand ourselves, we risk losing the ability to distinguish what we genuinely know from what our increasingly intelligent tools help us accomplish.

Artificial intelligence can make us faster, more productive, and more effective. Those improvements are measurable. What is harder to measure is what happens to the person using the technology.

Are we becoming better thinkers, or simply producing better-looking thinking? Are we developing stronger judgment, or becoming dependent on assistance we barely notice?

The unsettling possibility is that we could become more productive while becoming less capable of independently explaining, evaluating, or reproducing our own work.

Worse, we might not recognize the difference.

When Better Performance Conceals Weaker Learning

In 2023, MIT researchers Shakked Noy and Whitney Zhang published an experiment in Science examining how generative AI affected professional writing tasks.

The researchers studied 453 college-educated professionals, randomly assigning some participants access to ChatGPT.

Those using AI completed their work approximately 40% faster, while independent evaluators rated their output 18% higher in quality.

The findings demonstrated substantial productivity benefits. They did not establish whether participants developed stronger independent writing or reasoning skills. That was not what the experiment measured.

A 2025 study published in Proceedings of the National Academy of Sciences exposed why that distinction matters.

Researchers examined how generative AI affected mathematics learning among nearly 1,000 high-school students in Turkey.

Students received different forms of assistance. One group used GPT Base, a relatively unrestricted AI interface. Another used GPT Tutor, designed to support learning through hints and teacher-provided guidance. A control group worked without AI.

During practice, both AI groups performed substantially better than students without assistance.

GPT Base users improved their practice scores by 48% relative to the control group. GPT Tutor users improved by 127%.

Then the researchers removed the assistance.

On subsequent independent examinations, students who had used GPT Base scored 17% lower than students in the control group.

GPT Tutor users performed about as well as the control group, with no statistically significant difference.

The technology improved immediate performance in both groups, but the design of the assistance influenced what students could subsequently accomplish alone.

The Students Didn't Know They Were Learning Less

The most revealing finding concerned what students believed about their own learning.

Students using GPT Base did not recognize that their independent exam performance had suffered. Despite performing worse than the control group, they did not report believing they had learned less.

The researchers documented a gap between perceived learning and demonstrated performance.

This gets closer to the heart of our question.

A student completes difficult mathematics problems and experiences success. The answers are correct. The work is finished. The student feels more capable.

Yet when the assistance disappears, the student's performance tells a different story.

The experience of success and the acquisition of skill have become disconnected.

The study does not establish that AI generally makes people less intelligent. It examined particular tools, students, and mathematics tasks over a limited period.

But it demonstrates something consequential: People can mistake improved performance for improved understanding, even when independent testing reveals otherwise.

That is a problem of self-knowledge as much as a problem of technology.

The Skills We Stop Practicing May Be the Ones We Need Most

Human intelligence involves more than producing correct answers.

It includes recognizing suspicious conclusions, identifying missing information, deciding which problems deserve attention, and revising beliefs when evidence changes.

These abilities require practice, and some of that practice is uncomfortable.

Writing exposes gaps in our thinking. Solving unfamiliar problems forces us to confront what we do not understand. Explaining an argument without assistance reveals whether we can defend its underlying logic.

AI can remove much of that difficulty.

Sometimes that is precisely what we want. Few people would argue that manually performing every calculation is necessary for effective financial management.

But there is a difference between eliminating unnecessary work and eliminating the effort through which understanding develops.

The mathematics experiment illustrates that distinction. GPT Base made practice easier without producing stronger independent performance. GPT Tutor preserved more opportunities for students to participate in solving problems, and its users avoided the significant decline observed in the GPT Base group.

The implication extends beyond education, although the research does not establish identical effects in professional settings.

If AI increasingly handles difficult intellectual work, we need to identify which difficulties are worth preserving.

Not because struggle is inherently valuable, but because some forms of effort develop the judgment needed to recognize when a convincing answer is wrong.

We May Be Measuring the Wrong Kind of Progress

Organizations measure what is immediately visible.

How quickly was a report completed? How many customer requests were resolved? How much did production costs decline?

These are useful measures of performance. They tell us considerably less about the independent capabilities of the people involved.

A polished report once provided some evidence of the writer's ability to organize information and construct an argument. A sophisticated analysis suggested some command of the underlying material.

Neither was ever a perfect measure of competence. But AI makes the relationship between finished work and individual understanding more uncertain.

Someone can now produce an impressive analysis without necessarily understanding its assumptions. A correct answer may reveal little about whether the person presenting it could recognize an incorrect one.

This creates a practical challenge for education, hiring, and professional development.

We increasingly need two measures of performance:

• What can a person accomplish with intelligent assistance?
• What can that person understand, evaluate, and accomplish independently?

Both matter. AI-assisted performance reflects an increasingly useful capability. Independent performance matters because people must still recognize errors, respond to unfamiliar circumstances, and exercise judgment when systems fail.

The mistake is treating the first as sufficient evidence of the second.

What Happens When We No Longer Recognize What We Don't Know?

The answer is not to avoid intelligent technology. It is to become more deliberate about which intellectual abilities we preserve.

Students can practice with AI while periodically demonstrating understanding without it. Professionals can use AI to develop recommendations while remaining responsible for explaining their assumptions and identifying weaknesses.

The principle is straightforward: If a capability matters, we should not assume it is improving simply because AI-assisted results are getting better. We should test it.

Artificial intelligence is changing the relationship between intellectual performance and human understanding.

We can now produce increasingly sophisticated work without necessarily possessing the understanding that such work once suggested.

That changes how we recognize expertise, evaluate learning, assign responsibility, and judge our own competence.

The students in the mathematics experiment offer a warning. Their AI-assisted success felt like progress, even when their independent performance revealed a different outcome.

Now consider what happens when that confusion extends beyond mathematics exercises to professional judgments, institutional decisions, and the information people use to understand the world.

We could become increasingly confident in conclusions we are less capable of evaluating.

Perhaps the most important measure of an intelligent civilization will not be how many difficult problems its machines can solve.

It will be whether the people relying on those machines can still recognize which problems they understand, which they do not, and when the difference matters.

🧬 QuestionStrings to Practice: The Capability Test

Use this sequence to determine whether an intelligent tool strengthens your capabilities or simply improves immediate results.

What does this tool help me accomplish?
↓
Which parts still require my independent judgment?
↓
Could I recognize a serious mistake in its output?
↓
What would I struggle to do without its assistance?
↓
Which abilities should I deliberately continue practicing?

The goal is not to preserve every manual skill. It is to decide which capabilities are worth keeping.

---

Explore more questions about artificial intelligence, human judgment, and the future of learning through QuestionClass's Question-a-Day.

← Home