In our recent conversation with theoretical neuroscientist, entrepreneur, and author Vivienne Ming on the “Learning Machines Podcast,” we considered a question at the heart of our prior columns on AI, cognition, uncertainty, prediction, and human agency, namely, what humans we are becoming in the age of AI and whether the accelerating convenience of AI is gradually reshaping the habits of our minds.
Hidden within this acceleration is a parallel transformation that receives far less attention: the gradual erosion of our willingness to engage deeply with thought itself.
As a result, “the future may depend less on the capabilities of machines than on the kind of users we become,” Ming notes. In that respect, “AI is less a window than a mirror. It is showing ourselves back at scale,” she says.
While GenAI has been in general use for only a few years, users now range from passive consumers who increasingly outsource thought reflexively to highly engaged users who integrate AI deeply without surrendering intellectual agency.
As Ming observes, “the vast majority of even smart people shift into this ‘automator’ process by which their cognition largely shuts down.” They feel like they have thought about something but really have not. Brain scans corroborate this — the level of brain activity approaches that of TV watching.
In between are validators who do not outsource thinking entirely; however, they do outsource skepticism. They approach AI not as a mechanism for challenging assumptions, but as an instrument for reinforcing conclusions they already wish to believe. In this dynamic, conversational fluency becomes psychologically seductive because affirmation begins to feel indistinguishable from accuracy.
At the other extreme, Ming describes the “cyborg” user as an individual capable of integrating AI deeply into life and work without surrendering agency, skepticism, or intellectual independence. The main difference between a passive user (Automator) and an active user (Cyborg) is the level of cognitive effort—the latter is deeply engaged, pushing back, in a setting in which AI becomes the loyal opposition.
Slowly at first, and then all at once
The shift toward cognitive delegation did not begin with GenAI. The process began over 25 years ago with the advent of the internet; information became radically easier than at any other point in human history. GPS may have been among the first widely adopted technologies that routinely made decisions on our behalf with minimal cognitive participation. Research on navigation and spatial memory has long suggested that over-relying on automated direction weakens active cognitive engagement.
Before search engines or calculators, acquiring knowledge required deliberate effort through libraries, long-form reading, archival research, and the painstaking synthesis of multiple perspectives over time. One could not simply consume information passively but had to wrestle with it, contextualize it, and determine its relevance independently.
What has occurred during the last several years with LLMs and increasingly agentic AI represents something fundamentally different. The transition is no longer about retrieving information efficiently but about outsourcing increasingly sophisticated forms of cognitive labor altogether—and even delegating the decisions to be made after this outsourcing.
Is more friction the answer?
The question then is how we preserve discernment and improve outcomes in environments optimized for ease. This is where friction becomes indispensable.
A concept increasingly referred to as “friction maxxing” has emerged within behavioral science and digital culture to describe the intentional reintroduction of resistance, effort, and deliberate inconvenience into environments increasingly optimized for speed and immediacy.
While the phrase itself carries the aesthetic language of internet culture, the principle beneath it reflects something far older and far more fundamental, grounded in the recognition that the human brain was never designed to flourish in an environment where every question is answered instantly, and every cognitive burden can be externalized without effort.
READ: Sreedhar Potarazu and Carin Isabel Knoop | Heuristics or statistics in AI: How humans and machines actually decide (April 23, 2026)
Friction, in this sense, represents the cognitive tension necessary for discernment. It is the pause between receiving information and accepting it. It is the willingness to interrogate an output before internalizing it as truth. Friction, therefore, becomes a form of cognitive self-defense. It forces interruption where automation encourages passivity. It creates space for skepticism in environments optimized for immediacy. It compels individuals to ask not merely whether an answer sounds persuasive, but whether the reasoning beneath it can withstand scrutiny. Friction preserves the possibility of independent judgment precisely because it slows the automatic acceptance of polished, effortless outputs.
We previously explored the dangers of misunderstanding both machines and ourselves. The consequences of assuming that access to information is equivalent to understanding, as well as the interpretive gaps that emerge when humans and machines falsely assume mutual comprehension.
GenAI intensifies that risk because most users have little understanding of the data on which models are trained, the biases embedded within them, or the probabilistic architecture through which outputs are generated. For the average individual, evaluating the “ingredients” of a model is nearly impossible, leaving many users with little choice but to trust what appears authoritative.
At the same time, many individuals continue to interact with AI as though it were simply a more advanced search engine, even though these technologies operate in fundamentally different ways. Search engines retrieve indexed information while generative models synthesize language patterns based on training data, statistical relationships, and contextual inference.
This distinction becomes even more important when viewed through the lens of neuroscience. Human cognition operates differently under conditions of resistance than under conditions of effortless consumption. Rapid, low-effort decision-making relies heavily on automatic cognitive pathways optimized for efficiency and pattern recognition, conserving energy at the expense of scrutiny.
Learning itself follows the same principle. Information encountered without effort is often retained superficially, whereas information wrestled with through challenge, ambiguity, and sustained engagement becomes more durable and deeply integrated into memory. Resistance strengthens cognition in much the same way resistance strengthens muscle.
As Ming points out, “information encountered without effort is often retained superficially, whereas information wrestled with through challenge, ambiguity, and sustained engagement becomes more durable.”
Resisting the temptation of flattery
This phenomenon becomes particularly problematic when combined with one of the most powerful dynamics in human psychology, namely our discomfort with being wrong. Increasingly, users do not approach AI as a mechanism for challenging assumptions but as an instrument for validating preexisting beliefs.
READ: Sreedhar Potarazu and Carin Isabel Knoop | Opening up the AI peephole: Toward not misunderstanding each other (April 8, 2026)
Prompts are unconsciously framed to generate desired conclusions, and models optimized for engagement frequently exhibit forms of sycophancy that reinforce rather than interrogate user perspectives. The smoother and more affirming the interaction becomes, the greater the risk that confidence begins to detach itself from truth.
The consequence is not simply convenience but a subtle transformation in how we interact with knowledge, where conclusions are increasingly consumed without participating in the reasoning that produced them.
What begins to erode is not intelligence itself but discernment, the capacity to distinguish between what feels true and what is true, between persuasive fluency and grounded understanding, between affirmation and accuracy.
In a world increasingly populated by synthetic language capable of generating persuasive narratives at unprecedented scale, discernment may become one of the most important cognitive skills. The real danger of a frictionless world, then, is not that it makes life easier but that it gradually makes thinking optional.
User discretion advised
The “cyborg” framework implies a balanced relationship with AI that is neither dependency nor rejection but disciplined symbiosis. The goal is not to compete with machines at tasks they perform exceptionally well, nor to surrender human judgment to algorithmic outputs, but to integrate augmentation without forfeiting agency. The individuals who thrive in this environment will not necessarily be those who move fastest, but those who remain capable of resisting the seductive ease of passive cognition long enough to think independently.
Vivienne Ming’s latest book is titled “Robot-Proof: When Machines Have all the Answers, Build Better People.” In our next column, we will consider how to deploy the “cyborg” principle in our daily lives and in education.
Listen to the full episode on Spotify: Learning Machines Podcast with Vivienne Ming
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