Generative AI can boost coding speed, but it can also create dangerous technical debt. In this brief video, learn when your teams should be using GenAI and when they should avoid it altogether.

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.

https://www.youtube.com/watch?v=4kXxgpZo7lE

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.”