TL;DR AI is flattening basic skills: the barriers to programming, writing, and translation are approaching zero. Yet the same force is creating a deeper divide. When what you can do is no longer scarce, what you can judge becomes the true dividing line. This is not equality first and polarization later, but two sides of the same coin appearing at once.
A junior programmer, only three months into the job, uses AI to write code and completes in one week a feature that once took a month. The code runs, the logic is coherent, and the project manager is pleased. Their output is almost indistinguishable from that of a colleague with three years of experience.
At the same company, a senior data analyst also uses AI. It completes data processing that used to take three days. Yet before submitting her report, she spends two extra hours checking whether the output is logically consistent, verifying the reliability of data sources, and judging whether the conclusion holds in the business context. Those two hours are precisely the hard-to-name difference between her report and a junior analyst’s.
Both scenes point to the same conclusion: AI lowers the threshold for basic skills while widening the gap in higher-order cognitive capabilities. What once separated people was whether they could write code. Now it is the level of problems they can solve with code.
This is not a temporary imbalance. It follows from AI’s technical structure.
What AI Compresses Is Basic Skill
Why does AI flatten basic skills before higher-order capabilities? The answer lies in how it works.
One useful way to understand large language models (LLMs) is as compression. They encode statistical regularities and expressive patterns from vast amounts of human knowledge into model parameters, forming a huge probabilistic prediction system. Given a request, they do not simply “create”; they produce the most likely combination based on existing patterns. This mechanism is naturally good at copying and imitation—programming conventions, writing structures, and translation patterns—which are the core of basic skills.
What it does not handle as well is equally clear: creating new structures, judging subtle differences between situations, and making decisions with incomplete information. Those are the core of higher-order ability, precisely where pattern matching reaches its limits.
So AI’s flattening of basic skills is not accidental; it is determined by its technical structure.
A 2025 randomized experiment by the National Bureau of Economic Research (NBER) offers more direct evidence. It divided 1,174 adults into two groups for the same business problem-solving task—one with an AI assistant and one without. AI improved everyone’s performance, but the improvement was significantly larger for people with lower educational attainment. Without AI, the gap between higher- and lower-educated participants was 0.548 standard deviations; with AI, it narrowed to 0.139—closing roughly three quarters of the initial gap. That does not mean underlying capability disappeared; AI temporarily leveled it at the execution layer.
In essence, AI packages the best practices of highly capable people into a tool, allowing novices to borrow that experience. It is a force of regression toward the mean: lifting the bottom rather than pushing the top higher.
A GitHub Copilot randomized controlled trial offers evidence from another angle: developers using AI-assisted programming completed tasks more than 50% faster. With AI, a junior developer can approach the output of an intermediate developer.
If such augmentation can be accessed evenly, it could become the largest equalization of cognitive capability in human history. Someone who has never learned to program can use AI to write runnable code; a non-native English speaker can write a fluent English report; someone without a statistics background can complete complex data analysis. Skills that once required years of training become on-demand tools.
That is the reality behind the flattening of basic skills, and the optimistic vision many people hold for the AI era. Its value is helping more people cross the entry threshold. Its limit is that the world beyond that threshold is far more complex than the threshold itself.
After the Threshold Flattens, Gaps Emerge from Deeper Places
Between being technically available and being used evenly in practice lies a gulf.
Digital-inequality research has a classic framework: van Dijk’s 2020 four-level model asks whether people are motivated to use technology, have access to it, have the skills to use it, and can use it effectively. The first three are questions of access; the fourth is a question of quality of use. AI is rapidly addressing the first three, while the gap in the fourth is only beginning to emerge.
This pattern has recurred throughout technological history. The “Year of the MOOC” in 2012 was a classic preview: platforms such as Coursera and edX promised free access to elite education for anyone worldwide, but follow-up research found that the greatest beneficiaries were still those who had already received a good education. New technology did not automatically reduce inequality; it reproduced it in a new form.
AI is replaying the script. A 2025 PNAS study by Humlum and Vestergaard, based on large-scale Danish survey and registry data, found that ChatGPT adoption itself systematically reproduces existing inequality. Even after controlling for occupation, industry, and demographic characteristics, use gaps between groups remained. This is not theoretical speculation, but measurable reality.
Where do differences arise? Even if AI is free and everyone knows it exists, gaps still emerge in four dimensions: the ability to write effective prompts; the ability to assess AI output; the ability to integrate that output into a workflow; and enough domain knowledge to guide and verify AI.
The first layer of equalization solves the question of whether one has access. The second-layer divide comes from differences in whether people know how to use it and how effectively they use it. Surface gaps are erased; deeper ones appear.
Cognitive Offloading Is Changing How We Think
There is a deeper question: even when someone has every condition needed to use AI effectively, the process itself changes how they deploy cognitive ability.
The key variable is cognitive offloading—handing parts of thinking over to AI. The issue is not offloading itself, but what is offloaded and how.
A 2025 PNAS experiment by Bastani and colleagues provides causal evidence stronger than correlation. In a randomized high-school mathematics experiment, students who used AI directly to obtain answers saw their independent problem-solving ability decline. Students required to attempt reasoning first and use AI afterward retained their learning gains. The issue is not AI itself, but the usage pattern: unconstrained cognitive offloading erodes foundational ability, while structured use can protect or even strengthen learning.
EEG research from the MIT Media Lab provides a neuroscientific clue. It found that people writing with AI showed changed patterns of brain activity: networks associated with memory and creativity were less active, while the cognitive load associated with judgment and verification increased. This is not simply a decline in ability, but a migration in how cognitive activity is distributed.
A key distinction helps explain this shift. Earlier technologies—calculators, GPS, search engines—outsourced “nouns”: storage, retrieval, and calculation, while humans still supplied reasoning. AI outsources “verbs”: synthesis, evaluation, and judgment. When the task changes from thinking for oneself to deciding whether AI has thought correctly, the center of cognition undergoes a quiet migration. Low-value repetitive reasoning is compressed, while higher-order judgment, verification, and integration become more important.
Its value is freeing lower-level cognitive load so people can focus on higher-level judgment. Its risk is that deciding when not to rely on AI is itself the easiest judgment to outsource. The more one relies on AI, the less one can judge when not to. This creates a cycle.
Two Forces Work at the Same Time
Putting these clues together reveals a complete picture.
AI applies two opposing forces to the distribution of intellectual capability.
The first is a compressive force: it turns programming, writing, translation, and data analysis from skills requiring years of training into on-demand tools, narrowing basic-skill gaps. The second is a stretching force: once basic skills are no longer scarce, the valuable capabilities become asking good questions, judging answer quality, integrating fragmented information, and deciding under uncertainty. These capabilities are distributed far more unevenly than basic skills.
The compressive force creates the appearance of equality; the stretching force creates the substance of differentiation.
This is the deeper meaning of the NBER experiment. Lower-educated participants greatly narrowed their gap with higher-educated participants under AI assistance, but that does not mean the latter group’s core advantage was compressed. Its value shifts away from completing routine tasks quickly and toward areas AI struggles to replace: knowing when an answer is untrustworthy, spotting implicit bias in model output, and making trade-offs in complex contexts.
When the acquisition threshold for a skill is flattened, that skill loses its power to distinguish people. What once mattered was whether you could write code; now it is what level of problem you can solve with it.
Think of It Like Driving
An analogy can connect the preceding arguments.
AI adoption resembles the adoption of cars. Cars made everyone move faster—what once took a whole day from Beijing to Tianjin now takes two hours by car. In that sense, cars achieved “mobility equality”: the speed gap from point A to point B between ordinary people and professional racers is far smaller than in the era of walking.
But cars also created a new gap. A racer’s value no longer lies in simply driving fast, but in controlling a vehicle under extreme conditions. Most drivers remain at the level of reaching their destination; that flattening makes the scarcity of professional drivers more visible.
The correspondence is clear: cars flatten the gap in whether one can get somewhere, just as AI flattens the gap in whether one can do something. The racer’s value shifts to control at the limit, just as the value of people with higher-order capability shifts to judging and verifying AI output.
There is a subtler layer. People who rely on cars for a long time may lose their ability to walk well. This does not mean cars are bad; it means tool substitution and capability decline are two sides of the same process. AI is similar: it narrows the gap in what most people can do, while widening the gap in the judgments they can make.
Basic skills—coding, writing, translation, and data analysis—are driving. Higher-order skills—judgment, aesthetic sense, metacognition, and complex decision-making—are racing. AI equalizes the former while increasing the scarcity of the latter.
Declining walking ability is perceptible: you get out of breath after a short distance. Declining cognitive ability is not: it is hard to notice that you are thinking less. That is the warning of the EEG research—the brain changes where you cannot see it.
As Thresholds Disappear, Barriers Rise
Return to the two opening scenes.
The junior programmer finishes a feature with AI, but their ceiling remains at being able to write code with AI. The senior analyst uses AI to process data, but her judgment, domain intuition, and critical validation are what actually deliver value.
AI adoption is not a simple act of intellectual equalization. It silently rewrites the standards of selection. The old distinction—what you know how to do—is losing force. New distinctions are forming: what questions you can ask, when you can refuse to trust AI, and what kind of judgment you can form from fragments.
These abilities are much more unevenly distributed than programming and writing skills. AI’s real impact on intellectual gaps is neither simple narrowing nor widening, but restructuring: it erases one old layer of inequality and opens a new one at a deeper level.