Developer productivity and GenAI code: Demystifying the connection

Mar 17, 2024
min read

If you’re an engineering leader or executive, you may have come across various studies stating that developers are happier and more productive using GenAI code. Why is that, and why does it matter?

We’ve put together this short blog post to help you better understand the connection.

It’s about people

Let’s start with a discussion about human psychology.

Among knowledge workers and engineers in particular, there’s a productivity concept known as flow state, which is the experience of focusing on a single task.

When developers reach flow state, the task of programming feels like instinct rather than work. Coding becomes an all-engrossing, mentally consuming experience. Keep it mind that while in flow state,  it is tough for developers to context-switch or break their focus.

With this focus on human interest, McKinsey has found that developers using GenAI based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow due to the automation of grunt work.

How GenAI code supports flow state

One way that GenAI supports flow state is by automating drudge work — the high-friction processes that developers enjoy least about their work.

GitHub reported last year that 92% of developers were already using AI-powered coding tools in their work. Meanwhile, a November 2023 report from JetBrains found that given the opportunity, 56% of respondents would let an AI assistant write code comments and documentation, with 46% sharing that they are using Copilot.

Deeper analysis

Just as humanity shapes technology, the reverse of the statement is also true — technology shapes humanity. There is a relationship between the two, which some researchers suggest may be symbiotic.

One joint study from Harvard Business School (HBS) and Boston Consulting Group (BCG) has associated GenAI  productivity gains to behavior shifts in which GenAI functions as a “second brain” for technologists. Specifically, according to the BCG and HBS study, here are some patterns for how highly skilled knowledge-workers are integrating GenAI into their workflows:

  • Centaurs are dividing and delegating their solution-creation activities between the AI and themselves.
  • Cyborgs are integrating their task flows within the AI and continually interacting with the technology.

Both approaches, the centaur and the cyborg, demonstrate an ability to augment human ingenuity. It’s no surprise that knowledge workers are experiencing higher levels of confidence and fulfillment at work, as a result of being able to accomplish more.

Final thoughts

When evaluating applications of GenAI code in the software development lifecycle, it’s easy to get caught up in productivity metrics and systems. Remember that it’s people using the code and ultimately determining how GenAI gets implemented. It will be worthwhile to follow the research. 

About Sema Technologies, Inc. 

Sema is the leader in comprehensive codebase scans with over $1T of enterprise software organizations evaluated to inform our dataset. We are now accepting pre-orders for AI Code Monitor, which translates compliance standards into “traffic light warnings” for CTOs leading fast-paced and highly productive engineering teams. You can learn more about our solution by contacting us here.


Sema publications should not be construed as legal advice on any specific facts or circumstances. The contents are intended for general information purposes only. To request reprint permission for any of our publications, please use our “Contact Us” form. The availability of this publication is not intended to create, and receipt of it does not constitute, an attorney-client relationship. The views set forth herein are the personal views of the authors and do not necessarily reflect those of the Firm.

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