Buoyed by the rise of AI, many businesses rushed to automate their operations, hoping it would ease workloads and shrink development timelines. And why wouldn’t they? AI tools can write code in seconds, create apps in minutes, spin up entire systems with a single prompt and turn a junior developer into something that looks like a senior one — at least on the surface.
However, they quickly discovered that, although AI generates code fast, it often breaks under real conditions, and systems appear flawless until they fail. When these code malfunctions occur, the AI responsible for their creation often fails to provide an explanation. Teams then find themselves staring at long chains of errors created by code that only looked correct.
This early promise is turning into a deeper lesson about how software really works. The hardest part of engineering has never been writing code. It has always been debugging, the slow and often meticulous work of tracing the source of a failure, understanding what triggered it and repairing it so the system can run the way it was meant to.
While AI has made code creation faster, it has not made systems easier to understand or maintain. The strain has simply moved to the later stages of development, where failures are harder to diagnose. That gap is now shaping the real story of AI in software development and is where new innovators see a major turning point.
The Big Debugging Problem
Debugging requires a degree of reasoning that current AI systems find hard to grasp. These models were trained to predict the next likely token in a sequence, which works well for generating codes that follow familiar patterns. But real software does not operate that way. Real software functions as a dynamic system, evolving over time, accumulating state, interacting with data and relying on numerous implicit assumptions.
As Ishraq Khan, CEO and founder of Kodezi, explained, “Debugging is not predicting the next line of code. This involves reconstructing the reasons behind failures in complex systems with thousands of moving parts.” He argues that while models like GPT and Claude can complete patterns, they do not understand how those patterns behave once deployed. Khan noted that frontier models routinely score above 70 percent on code synthesis benchmarks but drop below 15 percent on real debugging tasks, a reality that one study by Microsoft Research notably affirmed.
In the most recent Stack Overflow Developer Survey, developers reported that debugging, testing, and maintenance occupy a significant share of their time, even as AI tools become more common. GitHub’s own engineering updates have acknowledged similar concerns, noting that AI assistants can introduce context gaps that require deeper human review once the code reaches production environments.
According to Khan, this problem led him to build a debugging-specific model instead of another general LLM. Chronos, Kodezi’s debugging-first model, was trained on millions of real debugging sessions, giving it exposure to the kinds of errors, logs and system behaviors that general models rarely see. The goal, explained Khan, is to help developers identify issues sooner, understand why they occurred and reduce the time spent rewriting or patching code after it breaks.
The Illusion Of Speed
Many organizations adopted AI coding tools because they offered visible speed at the beginning of the workflow. But faster creation can hide slower delivery. Developers save time during generation and then lose it during integration, validation and repair. Khan estimates that debugging alone consumes close to half of a developer’s time, which convinced him early on that code generation was never the real bottleneck.
“Developers are not saving time. The work is simply moving downstream where the cost is harder to see,” he said. It is one of the clearest insights from our conversation and echoes what many teams are now experiencing. AI boosted the front end of development but left the back end untouched. The work did not disappear. It simply shifted.
This creates what engineers and analysts call complexity debt, a buildup of small problems that quietly spread through a codebase. Tiny inconsistencies, subtle logic breaks, and duplicated functions pile up over time, and teams eventually spend more hours cleaning up than creating anything new. Companies experience a slowdown in releases, an increase in maintenance costs, and a realization that the initial speed they achieved through AI was not entirely sustainable.
As I’ve reported before, AI breaks down when it cannot see the full context of a system. Debugging is where that limitation becomes most visible.
The Next Frontier
As the industry grows more aware of these challenges, attention is shifting toward what comes after code generation. Investors and engineers are beginning to see debugging as the next major category in AI infrastructure. This transition mirrors earlier shifts toward observability, DevOps, and MLOps, fields that became essential because they addressed the hidden problems behind attractive demos.
As Khan told me, “Generation was the easy part. Debugging is the real frontier because it forces AI to understand failure, memory, and causality.” This is where the long-term economics of AI become clear. Companies do not gain real ROI from producing more code. They gain ROI from coding that remains correct, predictable, and stable as systems grow.
Companies are learning that the real value is not in how much code AI can produce but in how well that code holds up once it hits real environments. Fewer repeated failures, faster fixes, and more stable releases matter far more than raw output. Debugging tools that can hold context, remember past failures, and recognize recurring patterns could reshape entire engineering teams by turning debugging from cleanup work into a continuous learning process.
External experts see the same shift. GitHub CEO Thomas Dohmke noted in a recent interview that while AI tools can help launch software, scaling and maintaining those systems still requires deep technical understanding of how they operate in real environments, a point he emphasized in a conversation with The New Stack.
It’s clear that the broader industry now recognizes debugging as a major missing layer in building trustworthy AI systems, offering insights into whether automation can stand on its own or whether humans must continue cleaning up behind it.
What It Means
The real test now is whether AI can handle what happens after the code is written. If an AI tool cannot identify or fix its mistakes, it will always need human supervision. The AI tool that can trace a failure, explain it, and learn from it becomes far more useful in day-to-day engineering work.
Khan points to memory as the missing capability. “AI will only become trustworthy when it can understand its mistakes, not just produce more output,” he noted. Chronos, Kodezi’s debugging-first model, was trained on millions of real debugging sessions, which gives it exposure to failure patterns that general models do not typically see. It treats debugging as a conversation over time, not a single prompt. It learns from failed attempts and applies that experience forward.
Other experts agree that sustainable software, not fast software, will define the next stage of AI. Speed without stability increases costs. Stability without learning makes systems brittle. And the long-term direction, several engineers argue, is toward systems that can correct themselves with less human intervention — not by replacing developers, but by reducing the constant maintenance load that slows teams down today.
The industry has woken up to one simple truth: The future of AI isn’t about how quickly systems can create, but how well they can recover. Debugging is where that story begins, where intelligence shows itself. And it is where companies will discover whether their AI investments are truly making life easier or simply adding another layer of cost.