Does GenAI Impose a Creativity Tax?

Generative AI systems that model language have shown remarkable proficiency at a variety of tasks, and employees have embraced them to speed up writing and software development work in particular. The productivity boosts promised by these tools, such as ChatGPT, are leading many managers to incorporate them into workflows. However, our research reveals that the potential efficiency improvements come with a potential downside.

Overreliance on AI may discourage employees from expressing their specific know-how and coming up with their own ideas, and could also result in increasingly homogenized outputs that limit the advantages of employee diversity. In the long term, this could diminish innovation and originality. Managers seeking to gain efficiencies via large language models (LLMs) will need to help employees thoughtfully balance productivity and creativity in their collaboration with AI.

The Trade-Off Between Originality and Effort

AI-generated content impressively mimics the linguistic fluency of human-created content but typically lacks a specific user’s stylistic choices and the original thinking that they would naturally express when accomplishing the task without AI. Aligning AI outputs to successfully capture the intention of human outputs can require iterative and time-consuming prompt refinement that users may decide is not worth it if the AI’s early output is considered good enough. Thus, users face a decision: Invest time in customizing generative AI suggestions to progressively reflect more of their unique style and know-how — a process that can eat up productive time — or settle for somewhat suboptimal first drafts.

Consider a team of software engineers collaborating on a large-scale software project. As they work on the code base, each team member will make coding and documentation decisions that are in line with agreed-upon standards but are also driven by each individual’s own experience and preferences regarding object architecture, function naming, testing choices, and so on. Just as writers of prose aim to craft brilliant turns of phrase, software engineers strive to develop elegant and original solutions to coding problems.

When productivity is prioritized, LLM-based tools such as GitHub Copilot make it easy to quickly generate a draft or autocomplete large blocks of code. This can save a lot of time, given that the tools often write decent code and can quickly improve existing code.