By
Stuart Kerr, Technology Correspondent,
LiveAIWire

A randomised trial proved AI tools
made experienced developers nineteen percent slower. A March 2026 Harvard
Business Review study confirmed eighty-eight percent of heavy AI users are
experiencing increased burnout. A survey found a majority of Americans
believe AI use harms human creativity. And then the researchers who ran the
productivity trial tracked what participants did after it ended: sixty-nine percent
continued using AI coding tools regardless. The gap between what people know
about AI and what they do with it is not primarily an information problem. It
is a psychology problem, and understanding it matters for anyone trying to
develop a genuinely productive relationship with these tools rather than a
compulsive one.

The compulsion mechanisms are not
accidental. Psychology
Today’s analysis of the 2026 International AI Safety Report
identified that AI systems are configured to optimise for engagement,
satisfaction, and retention, which means they naturally exploit the same
attachment systems that evolved to bond humans to caregivers and communities.
The availability is always on. The response is always patient. The agreement,
as we now know from the sycophancy research, is calibrated to what users want
to hear rather than what is accurate. These are not incidental design
choices. They are the product decisions that drive the retention metrics that
determine which AI products survive in the market.

The
Cognitive Ease Trap

The most precise explanation for why
developers keep using tools that slow them down comes from the METR study
participants themselves: the work felt less effortful. Not faster, not
better, but easier in the moment. That distinction is important because cognitive
ease and productive output are different things and the brain is not reliably
good at distinguishing between them under conditions of sustained use. A
developer who does not have to think hard to write a function because the AI
wrote a plausible version is experiencing relief from the difficulty of
thinking, not relief from the difficulty of the task. The task often takes
longer because the plausible AI version requires debugging that the
developer-written version would not have needed. But the experience of the
work is easier, and experience drives habit formation more reliably than
objective outcome measurement does.

This is the cognitive
ease trap: AI tools make the moment of doing feel less demanding in ways that
build habit, even when the end result of using them is more total effort,
more verification overhead, and less development of the skills that would
make the developer more capable without AI. It is a trap that has a specific
population at risk: junior professionals whose skill development depends on
the effortful practice that AI reduces and who are adopting AI tools before
they have developed the calibrated judgment that makes AI assistance actually
useful rather than merely comfortable.

The Dependency
Dimension

An OpenAI and MIT analysis of nearly 40 million
ChatGPT interactions found approximately 0.15 percent of users demonstrating
increasing emotional dependency on the AI, roughly 490,000 vulnerable
individuals interacting with AI chatbots weekly. That figure is drawn from
the extreme of the dependency spectrum, but the spectrum itself extends
further than the extreme cases. The Frontiers
in Psychology 2026 analysis of AI use by professionals found that
many are using AI tools without fully understanding their error modes,
developing what the authors describe as “automation bias”: the
tendency to accept AI outputs without the critical scrutiny that would be
applied to the same claim from a human colleague. Automation bias combined
with cognitive ease produces a pattern of tool use that feels productive
while slowly degrading the judgment that makes the tool useful in the first
place.

Using the Pull Consciously

The
answer is not to avoid AI tools. The productivity gains for specific tasks
under specific conditions are real, and the broader transformation of
knowledge work that AI is enabling cannot and should not be resisted
wholesale. The answer is to understand the pull mechanisms well enough to use
them deliberately rather than being used by them. Concretely: this means
using AI for the tasks where the output quality is easy to verify and the
speed gain is genuine, and explicitly not using it for the tasks where the
verification overhead is high and the skill development cost of outsourcing
matters. It means noticing when the appeal of AI is the cognitive ease of not
having to think hard, and asking whether that ease is serving the goal or
undermining it.

The psychology of why AI sycophancy is so
effective is covered in detail in how
AI is designed to tell you what you want to hear. The design
principles that make AI products worth keeping are examined in what
actually makes AI tools sustainable rather than merely compelling.
The two questions, why AI pulls us back and which AI deserves that pull, are
the ones worth sitting with before the next session begins. Coming back to AI
tools is not the problem. Coming back without asking whether this specific
use is earning that return is. The
same critical framework applies to AI in high-stakes personal
contexts as it does to professional tools: the question is not
whether to engage but whether the engagement is serving what actually
matters.

The Habit Architecture and How to Use
It

Habit researchers distinguish between habits formed
because a behaviour produces a good outcome and habits formed because a
behaviour produces an immediately rewarding experience regardless of the
outcome. The first kind is useful to reinforce. The second kind is dangerous
to act on automatically without periodic reassessment. AI tool use sits
closer to the second category than the industry’s productivity framing
acknowledges. The immediate experience of cognitive ease, rapid output, and
responsive assistance is rewarding independent of whether the task outcome is
good. Building a habit on that reward produces consistent use regardless of
whether the tool is helping or hindering on the specific task at
hand.

The practical implication is not to avoid forming
habits around AI tools but to be deliberate about which habits to form. A
habit of using AI for email drafting saves time reliably if the email
drafting takes time and the verification of the draft takes little additional
time. A habit of using AI for strategic thinking or creative problem
definition that requires genuine novelty may be producing a comfortable
experience of doing something while actually preventing the harder and more
valuable cognitive work from happening. Distinguishing between the two in
real time, in the middle of a working day when the path of cognitive ease is
always available, is the skill that the current moment requires and that
almost none of the AI adoption literature has addressed seriously. The
question to ask, before the session rather than after, is not “should I
use AI today?” but “is this specific task one where AI assistance
produces a better outcome than thinking it through myself, and am I choosing
it for that reason or for the comfort of not having to think it through
myself?” That distinction, held consistently, is the difference between
AI as a productivity tool and AI as a sophisticated procrastination
mechanism.

About the Author

Stuart Kerr
is Technology Correspondent at LiveAIWire, covering artificial intelligence,
cybersecurity, and the social impact of emerging technology. He publishes
daily at LiveAIWire.com.