Cognitive Debt in an AI Workplace

Serena Kuang, Tensility Intern and MBA candidate at Northwestern Kellogg School of Management

Wayne Boulais and Armando Pauker, Managing Directors, Tensility Venture Partners

Executive Summary

AI acceleration is reshaping work, but too much offloading comes at a cost.

Cognitive debt builds when speed replaces thought, weakening the critical skills that fuel innovation.
Studies show that heavy AI use reduces neural engagement, weakens independent reasoning, and introduces rework from low-quality AI output.
The path forward requires intentional collaboration balancing AI efficiency with deliberate human oversight and skill development.

Introduction

In our last blog, AI’s Secret Weapon: Turning Usability Into Market Domination, we talked about the stunning growth of this generation of AI products, but this can be a double edged sword. While AI capabilities paired with ease of use can drive exponential adoption, we are observing friction in enterprise adoption of AI. The pressure to achieve speed encourages workers to overuse these tools, resulting in a deeper, cumulative problem: cognitive debt. The issue builds as human workers are skipping the necessary critical thinking and verification steps to use AI outputs correctly.

In this blog we want to highlight cognitive debt, why it occurs, and how it can impact your organization’s long-term health.

Cognitive Debt and AI Adoption

What is Cognitive Debt

Cognitive debt is the hidden mental cost incurred when individuals or organizations increasingly rely on AI for problem solving.

Cognitive Debt is New in the AI Workplace

The use and incurment of debt in the enterprise is not new. Financial debt is well understood and technical debt is a constant concern especially for engineers and product managers when organizations make short term decisions that create future rework problems. However, considering cognitive debt in the workforce is new. See Figure 1 below.

Debt structures, if left unmanaged, can shift a company’s focus from creating value for the future to servicing past choices, crowding out the bandwidth a business needs to be creative and agile. AI can free individuals from mundane tasks, but unchecked overreliance on AI faces a contradiction: the accumulation of cognitive debt impairs the very critical thinking and judgment skills valued for innovation.

Our Motivation

The concept of cognitive debt has been increasingly substantiated by empirical research. These recent studies highlight the measurable cost on humans of over-relying on AI.

MIT Media Lab's study from June 2025, Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, used EEG analysis to show that neural activity and engagement decreased with AI use. Researchers compared three levels of external assistance in essay writing for college students: Brain-only, Search Engine, and LLM (ChatGPT) and found that brain activity systematically decreased with increasing external support, with the LLM group showing weakest neural engagement, lowest sense of ownership, and producing more homogenous writing. In the final session of the study, the brain-only and LLM groups swapped levels of assistance and found that participants who relied on the LLM in the initial sessions had developed a dependence, exhibiting weaker cognitive performance and struggling with memory and ownership of their writing projects without ChatGPT.

The Center for Strategic Corporate Foresight and Sustainability investigated The Impacts of Cognitive Offloading and the Future of Critical Thinking observing that frequent AI tool usage on critical thinking skills across 666 participants showed a strong negative correlation where increased AI reliance is associated with reduced critical thinking. Researchers used a mixed-method approach, combining structured surveys and interviews across diverse age and educational groups. Younger, less educated participants showed the highest dependence on AI and the lowest critical thinking scores. Conversely, higher educational attainment was a protective factor, associated with better critical thinking regardless of AI usage.

Dependence on AI can create a skill deficit that threatens the human capital necessary for high-value work and long-term organizational innovation. The problem is not AI use itself, but overreliance, a behavioral tendency to offload thinking too quickly or too often. Over time, this behavior leads to cognitive debt, a measurable erosion of expertise. This research underscores why it is vital to shift the conversation from simply adopting AI in the enterprise to thoughtfully managing its long-term effects on a workforce.

The Cost of Cognitive Debt

The risk of cognitive debt is tied to cognitive load, the mental effort required to perform a task. When we engage in cognitive offloading, delegating mental tasks to external tools, we risk diminishing the necessary mental strain to build expertise and neural connections required for learning. Offloading this strain to AI adds an additional risk of accumulating AI slop–low-quality AI-generated content that often lacks originality or accuracy. This lack of cognitive strain and unchecked reliance on AI output directly results in:

Erosion of Critical Thinking Skills:

These studies have demonstrated a strong negative correlation between frequent AI tool usage and critical thinking abilities. As we build an increasing dependence on AI-assisted decision-making, it raises the question: what happens if we become unable to problem-solve independently?

Accumulation of Functional Rework:

While AI can automate routine tasks that are low-risk and easily validated, the rework is critical in tasks requiring high accuracy or deep critical thinking. A study Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity found while developers thought they were 20% faster with AI tools, they were actually 19% slower overall because the time saved writing was offset by the time spent cleaning up AI-generated code and rewriting prompts for large, complex codebases. In complex contexts, this correction often takes more time than if the original work had been done without AI in the first place, undermining the promised productivity gains.

Every major shift in technology, from industrial automation to digital computing, has changed the skills we value and the ones we automate. However, those past transitions primarily automated execution. The AI era automates cognition, introducing new risks.The challenge for this AI era is not resisting automation, but choosing consciously what to automate and what to preserve.

Human Friction in Enterprise AI Adoption

For most companies, the adoption of AI at the enterprise level is a strategic and functional imperative. Enterprise AI investment has expanded exponentially. According to KPMG on Trust, Attitudes and Use of Artificial Intelligence, 67% of organizations using AI report having a strategy to integrate AI into their business, and a majority of the workforce recognizes the benefits of AI through increases in efficiency, knowledge access, and faster decision-making. There is a strong market belief in the benefits of AI, and companies that fail to integrate AI fear being left behind by competitors.

However, we are seeing significant friction in this rapid path to AI adoption. We have a unique vantage point at Tensility, where we constantly evaluate innovative opportunities in enterprise applications of AI. We have observed that large, non-technical enterprises often struggle even with the basic introduction of first-step AI tools like internal chatbots to their workforce.

We see this tension across enterprises. AI use itself isn’t the issue, it’s uneven adoption and patterns of overreliance that determine whether teams gain efficiency or incur cognitive debt. Workers are excited to leverage AI to assist their workflows, but there is a knowledge gap in how to do so effectively. Many report that they did not receive any AI training or education. This gap in AI literacy results in either some workers abstaining from AI, risking falling behind competition, or others engaging in an over-reliance on AI without authorship and understanding of the materials produced by AI, resulting in cognitive debt.

What can we do?

From Manual to Agentic Workflows: Finding the Human - AI Balance

We are seeing three distinct workflow stages: Manual, AI Assisted, and Fully Agentic. Each stage introduces trade-offs between immediate speed and potential cognitive debt.

As shown in Figure 2 above, the AI assisted workflow currently achieves the most balance between improving speed and enabling informed decision-making while anchoring on a Human-in-the-Loop (HITL). HITL is an approach that deliberately integrates human expertise into an automated system. In an AI Assisted workflow, this is explicitly applied as the human is strategically positioned to review every suggestion the AI makes.

For example, in Q1 2025 Google reported that over 30% of code was written with AI autocomplete, which requires constant human review and approval, a mechanism markedly different from the acceptance of completely prompt-generated code as production-ready. This mandatory checkpoint forces the worker to engage critically with the output. The AI improves speed, but the HITL model ensures human judgment maintains quality and expertise. The agentic workflows envisioned today require the least critical thinking for human workers and come with risks of AI slop, low-quality AI-generated content that may lack originality or accuracy, and the compounding of probabilistic AI errors.

AI and Human Capital

Cognitive debt is a predictable outcome of optimising for fast output instead of quality collaboration between human effort and AI advancement. By understanding, measuring and managing cognitive debt, companies can foster a resilient culture where AI enhances rather than diminishes human expertise.

Successful companies mitigate cognitive debt by embedding control directly into their systems and processes. We suggest the following, as shown in Figure 3 below:

Because cognitive debt is a hidden cost, organizations must actively measure and surface the true impact of AI on productivity and skills. AI is not just transforming how we work, it is reshaping the necessary human capabilities. Organizational leaders must set clear goals for AI initiatives and evaluate results to assess impacts on employee development and balance them against business outcomes. They can pursue unchecked, short-term efficiency, thereby incurring cognitive debt that eats into the bandwidth needed for innovation, or they can adopt a strategic, measured approach. Consider AI integration as a transformation, not just a technology upgrade.

Conclusion

The studies referenced here reveal a pattern of lack of training, high rates of over-reliance, and concern about negative outcomes from AI that result in significant and costly functional debt. This cycle is driven by cognitive debt, but it is compounded by a fundamental disconnect in how companies are approaching the AI transformation. Individual workers are resisting because the current rapid push toward AI is designed to encourage offloading as opposed to upskilling, thereby devaluing human capital. The path to sustainable AI adoption is not about encouraging more use, but about cultivating skilled use. By planning to achieve AI literacy, organizations can ensure their employees become AI-ready, making the technology an asset for enhancement rather than an agent of skill erosion.