Article summary: Is AI code sabotaging its own ROI case?

Is AI code sabotaging its own ROI case? is an article on leaddev.com where tech journalist Bill Doerrfeld interviews Charity Majors, CTO of Honeycomb.

Some summary notes below, but the TLDR is that the true value of AI in software development isn’t just writing code faster, but accelerating the entire Software Development Life Cycle (SDLC). Ownership is more important than authorship. Keep focussing on the basics of SLOs, stress-testing, canary deployments, feature flags etc. Leaning on Platform Engineering and AIOps to augment operational work. Real ROI won’t come from generating more code faster – it will come from streamlining the lifecycle around it.

AI coding assistants are great at generating new code, but to truly get a return on investment (ROI) from AI in software development, the whole SDLC must be accelerated, too, including  DevOps-related problems and reducing toil.

Having AI write code is the easy part; the real work is maintaining that code once it’s in production, and making it understandable and discoverable for other engineers. The easy wins of AI today may result in harder-to-maintain code tomorrow.

Code authorship is becoming less important, and code ownership is becoming more critical. Fundamental coding best practices remain key: Short feedback loops and making it easy to test code in production is key; Canary deployments, feature flags, and easy rollbacks. Validate all changes, including AI driven one, in small, safe increments.

Proving the value of AI investments

How do you measure the value of new AI investments from a real-world cost perspective? What indicators are valuable to track? Subjective responses are a start, like gauging an engineer’s trust in AI tools. Measuring service-level objectives (SLOs), along with regularly stress-testing systems, remain important too.

Many teams don’t have access to a site reliability engineer and, further, few organizations are spinning up new developer and operations teams simultaneously. Instead platform engineering concepts, along with AI-assisted operations empower autonomous software delivery. Engineers to own production systems like never before. Platform engineering is a helpful method to support success amid AI-driven code development.

AIOps, or Artificial Intelligence for IT Operations, refers to using AI (and machine learning and data science) to IT operations problems, particularly in the data center management space. But in this article, Majors advocates for leaning on AIOps to augment operational work like debugging, observability, resiliency testing, infrastructure troubleshooting, and more.

Real ROI won’t come from generating more code faster – it will come from streamlining the lifecycle around it.

 

 

 

Tags: aiops, charitymajors, leaddev, platformengineering, summary