AI and the Red Queen Effect (Part 2): The Queen Comes to Life
Key Points (For Readers on the Go)
New research shows AI often intensifies work rather than reducing it
A real-world study found employees worked faster, took on more tasks, and extended their workday
Efficiency gains are quickly absorbed into rising expectations
This reflects the Red Queen effect—running faster just to maintain baseline performance
Practitioners can take concrete steps to protect their time, role, and workload boundaries
Introduction: From Theory to Evidence
This post builds directly on my earlier work on the Red Queen effect, including my article “The Red Queen Effect of AI in a Rapidly Evolving Technological Landscape” (March/April 2025, NASP Communiqué). The full article can be accessed through NASP’s website.
If you haven’t read the original blog post, you can find it here:
https://lockwoodconsulting.net/blog/the-red-queen-effect-why-ai-might-not-save-us-timeyet
In that piece, I argued that AI might not save us time because increased efficiency often leads to increased expectations.
We are now starting to see empirical evidence supporting that claim.
What the Research Actually Found
A recent Harvard Business Review article—AI Doesn’t Reduce Work—It Intensifies It—reports findings from an eight-month field study conducted in a U.S.-based technology company with approximately 200 employees.
The researchers used:
In-person observation multiple days per week
Internal communication analysis
Over 40 in-depth interviews across roles (engineering, product, research, design, operations)
Their conclusion is straightforward:
AI did not reduce work. It intensified it.
Employees:
Worked at a faster pace
Took on a broader range of tasks
Extended work into more hours of the day
And importantly, this occurred without a formal mandate to use AI.
The Red Queen Comes to Life
This is where the connection becomes clear.
In the original blog and NASP article, the concern was that:
AI increases efficiency
Efficiency raises expectations
Expectations absorb the gains
That is exactly what this study demonstrates in practice.
What looks like progress at the individual level becomes pressure at the system level.
Efficiency Expands Work—It Doesn’t Eliminate It
One of the most important findings is how workers used the time AI “freed up.”
They didn’t:
Work less
Reduce workload
Reclaim time
Instead, they:
Took on new responsibilities
Filled gaps across roles
Attempted tasks they previously would not have attempted
AI increased capacity, and that capacity was immediately consumed.
Three Mechanisms of Intensification
The study identified three consistent patterns:
1. Task Expansion
Workers moved beyond their traditional roles:
Non-specialists taking on technical work
Professionals expanding into adjacent responsibilities
Tasks that once required collaboration becoming individual work
2. Blurred Boundaries
Work extended into:
Breaks
Evenings
Transitional moments
Because interacting with AI feels quick and informal, work becomes easier to continue—but harder to stop.
3. Increased Multitasking
Workers managed:
Multiple AI threads
Parallel tasks
Ongoing monitoring of outputs
This creates a constant sense of juggling—even when productivity increases.
How the Red Queen Cycle Emerges
This pattern creates a self-reinforcing loop:
AI speeds up tasks
Faster work becomes visible
Expectations rise
Workers take on more
AI becomes necessary to keep up
Over time:
Productivity increases
But workload does not decrease
That is the Red Queen effect in action.
From Individual Choice to System-Level Pressure
This shift does not require policy changes.
It begins with individual behavior:
“I’ll use AI to do a bit more”
But quickly becomes collective:
“Everyone is doing more”
And eventually becomes structural:
“This is now expected”
This is how norms shift—quietly and quickly.
What This Means for School Psychology and Education
These dynamics are already emerging in applied settings.
We are seeing:
Faster report writing leading to higher expectations for turnaround
AI-assisted data analysis expanding what is expected in evaluations
Increased efficiency translating into expanded roles or caseloads
Faster communication creating expectations of constant responsiveness
The risk is not just doing more work.
It is that the definition of adequate work shifts upward, often without explicit discussion.
How Practitioners Can Avoid the Red Queen Trap
The research suggests that intensification happens by default—not by design.
That means practitioners need to be intentional about how AI is used.
1. Define What “Good Enough” Looks Like
AI makes it easy to keep improving outputs.
Set limits:
What is required vs. optional
How many revisions are appropriate
More is not always better.
2. Protect Time Savings
When AI saves time, decide where that time goes.
Use it for:
Consultation
Direct services
Prevention work
Or protect it as buffer time.
If you don’t decide, the system will.
3. Avoid Expanding Your Role by Default
AI makes new tasks possible.
Pause and ask:
Is this actually my responsibility?
Am I replacing a role that should still exist?
Capability does not equal obligation.
4. Set Boundaries Around “Micro-Work”
Small actions add up:
Quick prompts
Minor edits
Constant checking
Create limits:
No AI use during breaks
Defined stopping points
Sustainable work requires real recovery time.
5. Limit Multitasking
AI encourages parallel work.
Instead:
Work in sequence
Limit active tasks
Build in pauses
Attention—not just time—is the critical resource.
6. Make Expectations Explicit
AI changes what others assume is possible.
Clarify:
Turnaround times
Workload expectations
Quality standards
If expectations are not discussed, they will still change.
7. Maintain Human Connection
AI can make work more isolated.
Protect:
Consultation
Collaboration
Professional dialogue
These are not inefficiencies—they are safeguards.
Ethical Considerations
This is not just about productivity—it is about professional integrity.
Key concerns include:
Workload increases without transparency
Equity issues in access and training
Pressure to prioritize speed over quality
Long-term sustainability and burnout
AI should support professional judgment—not quietly reshape it.
Final Takeaways
AI does not automatically reduce workload—it often expands it
The Red Queen effect is now supported by real-world evidence
Efficiency gains are quickly converted into new expectations
Work is becoming more continuous and cognitively demanding
Practitioners must actively define boundaries to ensure sustainable use
Original Blog: https://lockwoodconsulting.net/blog/the-red-queen-effect-why-ai-might-not-save-us-timeyet
AI Use Disclosure - Portions of this post were drafted with the assistance of an AI writing tool and revised by the author for accuracy, clarity, and professional judgment.