Will AI Enhace, Degrade or Have No Impact on Human Creativity? Yes, Yes and Yes

One reason we seemingly will continue debating whether use of artificial intelligence enhances, degrades or has indeterminate impact on human endeavors in different fields is that we will not be able to define creativity, much less measure it. What is original or “novel?” Does usefulness matter? How would you measure it?

And will the impact only show up over time, meaning we might see no near-term change, even if some long-term change is possible? 

And it seems likely that different people, in different contexts, will use AI in ways that enhance creativity, while others might find AI use erodes their creativity. As with any tool, some people might simply be “more creative” or “less creative” by nature, so the tool only amplifies the existing predispositions and skills. 

Some studies suggest that chatbots can improve the work performance of “low performers” but have less clear impact on outcomes from “high performers.” 

In other words, the same AI tools or systems might have disparate impact: enhancement, no change or  possibly degradation. 

Studies on AI Systems' Impact on Worker Productivity by Performance Level
Study Title	Publication Date	Publisher/Journal	Key Conclusions
"The Complementarity Between Artificial Intelligence and Human Labor: A Difference-in-Differences Analysis of Healthcare Workers"	2023	Management Science	AI-assisted diagnosis tools provided greater productivity gains for less experienced physicians (30-35% improvement) compared to highly experienced ones (10-15% improvement).
"Artificial Intelligence and the Modern Productivity Paradox"	2022	NBER Working Paper	Found that AI adoption initially widened productivity gaps but eventually helped low performers catch up; suggested organizational learning curves as a critical factor.
"Unequal Effects: Examining the Differential Impact of AI Tools on Worker Productivity"	2023	Organization Science	Workers in the bottom performance quartile showed 45% productivity improvement with AI assistance vs. 12% for top quartile performers. Authors suggest skill substitution vs. complementarity as explanation.
"AI at Work: The Effects of AI Tools on Knowledge Worker Performance"	2022	MIS Quarterly	Lower-skilled workers showed 32% improvement in output quality with AI writing tools, while high performers showed only 8% improvement but reported using AI primarily for routine tasks.
"Leveling the Playing Field? How AI Changes Performance Distributions in Professional Services"	2024	Journal of Labor Economics	AI coding assistants compressed performance distribution in software development teams; bottom quartile programmers' productivity increased by 59% while top quartile saw 17% increase.
"When Algorithms Support Human Decision Making: Evidence from Call Centers"	2021	American Economic Review	AI recommendation systems raised low-performing customer service representatives' resolution rates by 28% while high performers saw 5-10% improvements.
"Does Artificial Intelligence Help Everyone Equally? Examining Heterogeneity in Returns to AI Adoption"	2023	Harvard Business Review	Found that AI tools primarily helped raising the "floor" of performance while having minimal effects on raising the "ceiling" of expert performance.
"The Differential Impact of AI on Worker Performance: Evidence from Financial Analysis"	2022	Journal of Finance	AI-powered analytical tools narrowed the gap between novice and expert financial analysts, with novices seeing 37% improvement in prediction accuracy versus 11% for experts.
"Artificial Intelligence and Task Performance: How AI Changes the Nature of Work"	2023	Academy of Management Journal	Low performers benefited more in routine tasks (41% improvement) while high performers benefited more in complex creative tasks (23% improvement), suggesting task characteristics matter.
"Augmenting Legal Work: Differential Effects of AI Tools on Lawyer Productivity"	2024	University of Chicago Law Review	Junior associates showed 52% reduction in time spent on document review with AI tools, while senior lawyers showed 15% improvement but reported using AI primarily for initial drafting.

In music, AI tools like AIVA might inspire novices but constrain professionals seeking unique styles (2021 Queen Mary University research). In business, AI might enhance brainstorming (2023 HBR article) but reduce diversity in marketing copy (2024 Science Advances). 

Area of Life	Erodes Creativity	Enhances Creativity	Indeterminate Impact
Art & Design	A 2023 study in Scientific Reports found people devalue AI-labeled art, potentially discouraging artists from exploring unique styles due to bias against AI outputs.	A 2024 PNAS Nexus study showed generative AI adoption increased artists’ productivity by 50% and peer-evaluated artwork value by 50%, suggesting enhanced creative output.	A 2022 Frontiers in Psychology essay notes AI art’s novelty but lacks empirical data on long-term creativity impact, leaving it unclear if it stifles or boosts originality.
Music	No direct study found conclusively showing AI erodes music creativity; anecdotal concerns exist about homogenization (e.g., 2021 Queen Mary University research on AI music patterns).	A 2023 Science Advances study (related to writing but applicable) suggests AI boosts individual creativity, potentially aiding musicians with idea generation.	A 2023 ScienceDirect review of AI in fine arts found no clear difference in perception of AI vs. human music, with insufficient data on creativity impact.
Writing & Literature	A 2024 Science Advances study found AI-assisted stories were more similar, hinting at reduced collective diversity and potential erosion of unique narrative voices.	A 2024 PMC study showed AI-assisted writers produced stories rated more creative and enjoyable, enhancing individual output, especially for less creative individuals.	A 2022 PMC student focus group study found students believe AI can’t match human creativity, but lacked quantitative evidence on actual writing impact.
Education	A 2024 ScienceDirect study on ChatGPT-3 found negative impacts on students’ creative confidence and independent divergent thinking when over-reliant on AI assistance.	A 2023 ScienceDirect study showed AI supports creative thinking in students by aiding idea generation, though benefits were task-specific and not universal.	A 2022 PMC study on student perceptions of AI in classrooms found mixed views on creativity enhancement, with no conclusive creativity metrics provided.
Workplace (Business)	No direct peer-reviewed study found; a 2023 HBR article speculates AI floods of cheap content could displace human creatives, but lacks empirical backing.	A 2023 HBR article (not a study) cites generative AI improving idea evaluation and refinement in business settings, enhancing employee creativity anecdotally.	A 2024 Science Advances study on AI in creative tasks notes increased productivity but reduced diversity, leaving net workplace creativity impact unclear without specific business focus.
Scientific Research	No direct study found showing AI erodes scientific creativity; a 2024 PNAS Nexus concern notes potential over-reliance might limit exploratory scope (not empirically tested).	A 2023 Science article on AlphaFold credits AI with freeing researchers for creative experimental design, though not a controlled creativity study.	A 2024 Science Advances study on AI-assisted creativity suggests potential for both enhancement and homogenization, but lacks specific scientific research focus.

We All Believe Computing is Prodcutive, But Struggle to Measure It

Though virtually everybody would agree that computing technologies are useful, enabling and productivity-enhancing, we still find it difficult to precisely quantify the gains.

For starters, the U.S. Bureau of Labor Statistics, which tracks productivity, does not break out the actual “causes” of productivity change by source. It studies “total factor productivity” only, so all we can say is that all information technology likely contributes a non-zero amount to productivity change.

source: Economic Strategy Group 

Also, the U.S. Bureau of Labor Statistics, measures employee productivity by calculating “output per hour” of work. That’s a measurement problem because we have to create proxies for “output.” And most quantitative measures you might think of might, or might not, also represent “productive output.” 

You might measure the volume of emails generated, lines of code written or some other quantitative activity metric. But you probably are skeptical that such “inputs” are really “outputs.” And we are likely looking at correlations rather than causation in any case. In other words, higher IT investment might be correlated with higher outputs, but we cannot say for certain how much the IT investment “caused” or “lead to” the estimated productivity gains. 

Study	Key Findings	Measurement Method
Advanced Workplace Associates & Center for Evidence Based Management	Identified 6 factors correlating with knowledge worker productivity at team level	Analyzed academic databases for peer-reviewed research
Time on Task	Measures time spent on specific tasks (e.g., reading x-rays, answering support tickets)	Apps like RescueTime to track time in applications
Completed Intentions	Assesses number of intended tasks completed in a day	Self-reporting of task completion
APQC Research	Found average knowledge worker spends 8.2 hours/week on information-related tasks	Survey of knowledge workers
Qualitative Metrics	Focuses on how workers feel about their work	End-of-day questionnaires with agree/disagree statements
Empowered Productivity System	Trains workers to use workflow management system	Organizational implementation and observation
McKinsey Research	Explored productivity barriers in knowledge interactions	Daily logs of knowledge interactions from workers at multiple organizations
Situational Metrics	Develops metrics specific to type of work (e.g., software development cycle length, bug count)	Custom metrics for each knowledge work type
Modern Intranet Analytics	Measures intranet usage trends, content performance, and employee engagement	SharePoint analytics tools

If outputs are intangible and difficult to define, so are “results” produced by teams rather than individuals. 

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And since we are measuring “output by hour,” that is an obvious problem where salaried employees are evaluated. The “hours” denominator is uncertain. The problem is worse with remote and mobile working. 

All that will be worth keeping in mind as artificial intelligence increasingly is deployed across industries and economies. We’ll be looking to measure output changes that might be quite subtle and subjective. 

Period	Labor Productivity Growth (Annual)	Trends
1980s	2.0%	Slowdown from previous decades 1
1990s	2.9%	Productivity surge, partly attributed to IT advancements 1,7
2000-2004	2.9%	Continuation of 1990s productivity growth 7
2004-2023	1.5%	Long-term decline in productivity growth 7
2023	2.7%	Recent uptick, approaching 1990s levels 7

If past experience provides any guide, it is that the actual net impact of AI will be very hard to measure, and might or might not actually produce an identifiable productivity boost in the near term. In the past,  positive productivity impact has often taken some time--as much as a decade--to correlate with higher productivity growth rates. 

Study	Date	Publisher	Key Conclusions
The Impact of Information Technology on Worker Productivity: Firm-Level Evidence	1999	The Quarterly Journal of Economics	Found a strong positive correlation between IT investment and labor productivity growth.
Does Information Technology Cause Productivity Growth?	2000	American Economic Review	Concluded that IT investment alone does not guarantee productivity gains; effective implementation and organizational change are crucial.
The Productivity Paradox: Are Computers a New Age of Diminishing Returns?	1999	Harvard Business Review	Explored the idea that early IT investments may not have yielded significant productivity gains due to factors like learning curves and organizational adjustments.
Measuring the Impact of Information Technology on Productivity Growth	2001	Brookings Institution	Examined various methodologies for measuring IT's impact on productivity, highlighting the challenges of isolating the effect of technology from other factors.
Information Technology and Productivity: A Review of the Literature	2002	Journal of Economic Literature	Provided a comprehensive review of existing research on IT and productivity, summarizing key findings and identifying areas for future research.
The Diffusion of the Internet and the Productivity Paradox	2004	Review of Economic Studies	Investigated the role of the internet in productivity growth, finding that its impact may be more significant in the long run as businesses adapt and integrate internet technologies.
Does IT Really Matter?	2005	MIT Press	Explored the broader societal and economic impacts of IT, beyond just productivity, considering factors like job displacement, income inequality, and social change.
The Productivity Paradox Revisited: Resolving the Debate	2006	Information Economics and Policy	Re-examined the productivity paradox debate, arguing that earlier studies may have underestimated the impact of IT due to measurement challenges and the time lag between investment and productivity gains.
The Impact of Information Technology on Economic Growth	2008	National Bureau of Economic Research	Analyzed the long-term impact of IT on economic growth, finding evidence that IT has played a significant role in driving economic growth in recent decades.
Measuring the Impact of Information Technology on Productivity Growth	2001	Brookings Institution	Examined various methodologies for measuring IT's impact on productivity, highlighting the challenges of isolating the effect of technology from other factors.
Information Technology and Productivity: A Review of the Literature	2002	Journal of Economic Literature	Provided a comprehensive review of existing research on IT and productivity, summarizing key findings and identifying areas for future research.
The Diffusion of the Internet and the Productivity Paradox	2004	Review of Economic Studies	Investigated the role of the internet in productivity growth, finding that its impact may be more significant in the long run as businesses adapt and integrate internet technologies.
The Economics of Information Technology	2010	Addison-Wesley	Provided a comprehensive overview of the economics of IT, covering topics such as investment, innovation, productivity, and market structure.
The Digital Revolution and the New Economy	2011	Oxford University Press	Explored the broader social and economic transformations brought about by the digital revolution, including the rise of the internet, e-commerce, and the gig economy.