Have your engineering teams embraced generative AI for coding projects? If not, they likely will soon. But there’s a paradox: While these tools can deliver real productivity gains, they can also create hidden risks that could cripple your systems months or even years later.

In this video, MIT Sloan Management Review features editor Kaushik Viswanath breaks down the dangers of technical debt — the future work required to address today’s poor-quality code — which can quickly accrue from AI-assisted software development in brownfield environments. He shares practical advice on how to use GenAI to accelerate development cycles while keeping it out of situations where the risks outweigh the benefits.

Viswanath interviews Dartmouth College professor Geoffrey Parker and Culture Amp founder and CTO Doug English, whose company has clear directives and guardrails for engineers using AI tools for coding.

For a deeper dive into how to limit the technical debt that code developers may inadvertently introduce when using AI tools, read the full article that inspired this video, “The Hidden Costs of Coding With Generative AI.”

Video Credits

Geoffrey Parker is the Charles E. Hutchinson ’68A Professor of Engineering Innovation at Dartmouth College and faculty director for the Arthur L. Irving Institute for Energy and Society. He is also a research fellow at the MIT Initiative on the Digital Economy.

Doug English is cofounder and CTO of Culture Amp.

Kaushik Viswanath is features editor at MIT Sloan Management Review.

M. Shawn Read is the multimedia editor at MIT Sloan Management Review.