Opinion
The hidden risk in modern software development: Code is moving fast, but everything else is lagging behind
"The ability to write code faster is a powerful advancement, but also a double-edged sword. Without corresponding upgrades in the rest of the development lifecycle, we risk building brittle systems, increasing time-to-resolution, and delivering poor user experiences," writes Amir Ish-Shalom, VP of R&D at Lightrun.
The tech world is rightly amazed by how rapidly AI tools are accelerating code creation. In just a year, we’ve gone from autocomplete to fully generating production-grade code snippets in seconds. According to a recent Stack Overflow survey, over 70% of developers now incorporate AI tools into their daily workflow.
This marks a massive leap forward in software development, but like any leap taken too quickly, something gets left behind. In this case, it’s almost everything else.
The Software Development Life Cycle (SDLC) has been transformed at the coding stage. What used to take a week of development now takes just hours. But critical stages like quality assurance, debugging, CI/CD pipelines, and production monitoring haven’t kept pace. The result? A dangerous imbalance: code gets shipped to production faster than ever, but organizations are often unprepared for what comes next.
Fast Code, Slower Teams
Perhaps the most critical issue is a growing disconnect between developers and the code they "wrote." In the past, a developer would write each line by hand, wrestle with the logic, and gain a deep understanding of how the system worked, essential knowledge when it came time to debug a crash or solve an edge-case bug.
Today, much of that code is written by AI. A developer might ask an AI agent to write a function that interfaces with a third-party API. It works, until it doesn't. When something goes wrong in production, the developer may not understand why. They didn’t write the logic themselves, and the AI agent doesn’t retain context from past queries. Debugging becomes guesswork.
What used to be a specialty skill, debugging, has morphed into a game of reverse engineering. According to Gartner, debugging and production troubleshooting now consume up to 40% of developer time. As AI-generated code proliferates, this issue is escalating rapidly.
Speed at the Cost of Stability
The software development process depends on balance. But when the velocity of coding increases dramatically while QA, monitoring, and incident response remain static, the result is broken systems and frustrated users.
Production is where the real test begins. Issues like latency, rare edge cases, or sudden crashes often appear only when code is deployed in real-world conditions. Existing observability tools, log-based monitoring, APM platforms like New Relic and Honeycomb, offer only a partial picture. Developers are often left without real-time insight into how their code is behaving in front of real users.
That’s why more companies are adopting tools for debugging directly in production, without stopping services or compromising performance. These tools help reconnect developers to their codebase, even if they weren’t the original author. But even that’s not enough. If organizations want to keep up with the speed of AI-assisted development, they must also upgrade their entire toolchain, QA, debugging, and monitoring, with AI-powered solutions.
It’s a Leadership Challenge Now
This shift requires a fundamental mindset change. It’s not enough to “adopt AI”, development leaders must evolve their entire engineering process. That includes investing not only in AI code generation tools, but in intelligent debugging, observability, and QA platforms that can operate at modern speeds.
CEOs, CTOs, and VPs of Product must ask: Can our teams effectively debug the code we’re deploying? Do we have enough visibility into our dev and staging environments? Are we upgrading only part of the pipeline, or are we rethinking the entire SDLC end-to-end?
The ability to write code faster is a powerful advancement, but also a double-edged sword. Without corresponding upgrades in the rest of the development lifecycle, we risk building brittle systems, increasing time-to-resolution, and delivering poor user experiences.
The path to success isn’t just faster code. It’s a fully modernized development process, one that keeps pace with the speed of AI and ensures long-term reliability, maintainability, and performance.
Amir Ish-Shalom is VP of R&D at Lightrun.
