Two Wharton researchers want to add a third system to the classic dual-process theory. You can argue with the label, but the 40-percentage-point gap in people's accuracy when given a right AI versus a wrong one is harder to discount.
Steven Shaw and Gideon Nave (The Wharton School) call the underlying behavior cognitive surrender, meaning accepting an AI's answer with little or no scrutiny.
Below, we summarize what Shaw and Nave found, then give our own read of it: where we think the theory overreaches, what other readers pushed back on, and what the results mean for organizations that treat a human sign-off as a safeguard.
The paper in 200 words
Shaw and Nave propose Tri-System Theory. Dual-process theory describes two kinds of thinking, System 1 (fast and intuitive) and System 2 (slow and deliberate). The authors add System 3: artificial reasoning that happens outside the brain and can supplement or replace the other two. Their paper's key prediction is cognitive surrender, meaning adopting AI outputs with little scrutiny and overriding both the intuition of System 1 and the deliberation of System 2.
The authors ran three experiments with their analysis plans fixed in advance. About 1,400 people answered close to 9,600 questions from an adapted Cognitive Reflection Test, a set of puzzles designed so that the first answer that comes to mind is wrong. The researchers secretly controlled whether an AI assistant gave right or wrong answers, and participants chose to consult it on most questions. Compared with people working without AI, accuracy rose 25 percentage points when the AI was right and fell 15 points when it was wrong. The authors classify this as a large effect and treat it as the behavioral signature of cognitive surrender. Using the AI also raised participants' confidence, including after errors.
Time pressure (Study 2) and per-question rewards with feedback (Study 3) shifted performance, but did not eliminate the pattern. An accurate AI softened the cost of time pressure and increased the gains from rewards, while a faulty AI reduced accuracy in every condition. People who trusted AI more surrendered more. So did people who scored lower on two measures: need for cognition, meaning how much someone enjoys effortful thinking, and fluid intelligence, meaning the ability to reason through unfamiliar problems.
The paper was posted to the Social Science Research Network (SSRN) on February 2, 2026, as a Wharton School research paper. It is dated January 11, 2026, and was last revised February 10, 2026. When this article was written, on August 13, 2026, it was still a preprint and had not completed peer review.
Incentives and time pressure did not break the pattern
That's the paper as the authors present it, but what does it all mean in practice? Managers should look first at Studies 2 and 3, because they test the two solutions that organizations usually reach for first; the findings show that paying people to be careful and telling them how they did was not enough to stop them following a confident wrong answer.
However, not every finding can (or should) be mapped to real-world scenarios as exactly. For example, the research found that people who trusted AI more surrendered more, and so did people lower in need for cognition or fluid intelligence. While that reads easily as a story about other people being gullible, the results more plausibly describe a disposition that varies a lot between people and that nobody screens for at the moment someone is handed a tool.
behavior change 101
The third system may be older than it looks
Our main disagreement with the authors is over the theory, which doesn't hold up as well as the data behind it. By the paper's own definition, outside reasoning that supplements what happens in our minds can include some very old technology, and it's not obvious that re-numbering the theory adds much beyond pizzazz.
How others are reacting
We were not the only readers with doubts about the label. When the paper was shared, most of the people who argued with it ended up sharpening the idea underneath it. Their replies fell into four threads, and we add our own view where we have one.
The most common interpretation amongst our audience was that System 3 lives inside System 2, rather than beside it. A design researcher at a brand strategy consultancy described it as :System 2 moved partly outside the head." In their view, it changes how deliberation works without becoming a separate kind of thinking.
They makes a fair historical point too; humans have always offloaded memory to marks, knots, ledgers and writing, and navigation to maps and GPS. They end on responsibility: something has to stay answerable for the result, and in their view that something is a person.
This reading strengthens the paper's empirical claim while weakening its theoretical one. The 40-point gap happened either way, and only the numbering goes away.
A product director at a software company put the mechanism plainly. When the output comes back fully formed, it bypasses the friction that usually forces System 2 to wake up. Delegation is old, and what feels new to them is delegation this smooth. Psychologists call this processing fluency: information that is easy to take in tends to feel true.
That fits the confidence result closely, since the assistant in these studies delivered wrong answers in the same assured tone as right ones. It also puts part of the problem in the interface, which is easier to change than anyone's judgment.
A governance lead at a large consultancy made the institutional version of the argument, and noted that it predates AI by decades. Committees have always deferred to the most experienced person in the room. When they do, the reasoning lives in one head and walks out of the building with it. AI delegation differs because it is invisible at the seams. With a person, you at least know who decided, whereas with AI the behavioral signature is the only evidence that anything was surrendered at all.
They argue that if you cannot tell afterwards which decisions were reasoned and which were simply adopted, you have a provenance problem that no AI policy will solve. By provenance, they mean a record of where a decision came from. He wants each decision logged with whether it was reasoned or adopted, along with the criteria behind it.
A policy advisor at an AI governance nonprofit framed the same gap as authority and consequence coming apart. People carry the cost of errors made by systems they cannot audit. That raises a harder question about why so many workflows now require an opaque tool to get anything done.
A clinical psychologist offered the individual version and called it learned helplessness, the state in which someone stops trying to act because past efforts made no difference. The comparison was to an overbearing caregiver who takes on so much of a person's mental load that the person loses the footing needed to assert any authority of their own.
A smaller set of replies went after the foundation. A clinician-researcher at a teaching hospital called dual-process models a relic. They saw no reason to accept a third system when he does not accept the first two as described. They extended the same doubt to the idea of working memory and long-term memory as separate compartments. A consultant at a systems-safety firm objected to the idea of AI supporting existing ways of thinking. In their view, once the goal is to back up a conclusion already reached, the result is automated justification.
Both critiques take aim at the theory, and both leave the measurement standing. Participants who followed a confident wrong answer still scored 15 points below people working without AI. Organizations have to manage that however the systems end up being counted.
Most of the disagreement was about where cognitive surrender belongs. Some placed it in a theory of the mind, and others in the design and governance of the tools people use. We think it belongs in both, and that split is part of why no single policy fixes it.
A human sign-off can hide the same surrender
Wherever the "problem" is placed, it has clear consequences for regulation. Many organizations, and new rules like the EU AI Act, count on a person reviewing AI outputs as the main safeguard. However, this research suggests that safeguard is weaker than it looks. A reviewer who is short on time and working with a mostly reliable tool is likely to wave through the occasional wrong answer, and to feel confident while doing it.
One way to find out is to do what Shaw and Nave did: slip a small, known number of deliberately wrong outputs into a review queue and count how many get approved. Few compliance teams will be comfortable putting errors into a process on purpose, but it's one way to know whether the review is actually happening.
