A critique of the MIT AI and cognitive debt study - confusion of "cognitive debt" with "role confusion"

Here I am passing on a commentary on and critique of the work pointed to by the previous MindBlog post. It was written by Venkatehs Rao in collaboration with ChatGPT4o":

I buy the data; I doubt the story. The experiment clocks students as if writing were artisanal wood-carving—every stroke hand-tooled, originality king, neural wattage loud. Yet half the modern knowledge economy runs on a different loop entirely:

    delegate → monitor → integrate → ship

Professors do it with grad students, PMs with dev teams, editors with freelancers.
Neuroscience calls that stance supervisory control. When you switch from doer to overseer, brain rhythms flatten, attention comes in bursts, and sameness is often a feature, not decay.

The Prompting-Managing Impact Equivalence Principle 

For today’s text generators, the cognitive effects of prompting an LLM are empirically indistinguishable from supervising a junior human.

Think inertial mass = gravitational mass, but for AI.
As long as models write like competent interns, the mental load they lift—and the blind spots they introduce—match classic management psychology, not cognitive decline.

Sameness Cuts Two Ways

    Managerial virtue Good supervisors enforce house style and crush defect variance. Consistent voice across 40 blog posts? Process discipline.

    Systemic downside LLMs add an index-fund pull toward the linguistic mean—cheap, reliable, originality-suppressing (see our essay “LLMs as Index Funds”).

    Tension to manage Know when to let the index run and when to chase alpha—when to prompt-regen for polish and when to yank the keyboard back for a funky solo.

Thus the EEG study’s homogeneity finding can read as disciplined management or proof of mediocrity. The difference is situational judgment, not neurology.

Evidence from the Real World

    Creators shift effort from producing to verifying & stewarding (Microsoft–CMU CHI ’25 survey)

    60 % of employees already treat AI as a coworker (BCG global survey (2022))

    HBR now touts “leading teams of humans and AI agents” (Harvard Business Review, 2025)

Across domains, people describe prompting in manager verbs: approve, merge, flag.

So Why Did the Students Flop?

Because freshman comp doesn’t teach management.
Drop novices into a foreman’s chair and they under-engage, miss hallucinations, and forget what the intern wrote. Industry calls them accidental .managers

The cure isn’t ditching the intern; it’s training the manager:

    delegation protocols

    quality gates

    exception handling

    deciding when to tolerate vs. combat sameness

A follow-up study could pit trained editors, novice prompters, and solo writers against the same brief—tracking error-catch speed, final readability, and EEG bursts during oversight moments.
 

Implications

    Education – Grade AI-era writing on oversight craft—prompt chains, fact-checks, audit trails—alongside hand-wrought prose.

    Organizations – Stop banning LLMs; start teaching people how to manage them.

    Research – Use dual baselines—artisan and supervisor. Quiet neural traces aren’t always decay; sometimes they’re vigilance at rest.
 

Closing Riff

The EEG paper diagnoses “cognitive debt,” but what it really spies is role confusion.

We strapped apprentices into a manager’s cockpit, watched their brains idle between spurts of oversight, and mistook the silence for sloth.

Through the lens of the Prompting-Managing Equivalence Principle:

    Sameness ⇢ quality control

    Low activation ⇢ watchful calm

    Real risk ⇢ index-fund homogenisation—a strategic problem, not a neurological cliff.

Better managers, not louder brains, are the upgrade path.