The Hidden Costs of AI-Assisted Mainframe Development
Jennifer Nelson, CEO of Izzi Software, makes the case for cautious skepticism when using new approaches to debug
Artificial Intelligence (AI) is rapidly becoming mainstream in the world of coding and programming. More than 60 percent of developers now leverage AI tools in their daily work, a massive increase from just 10% five years ago. This seismic shift is not only transforming how code is written, but it is also sending ripples into the realm of debugging, an area that has long been considered sacrosanct to the skill set of technologists.
While the benefits of AI-assisted debugging cannot be denied, it prompts a critical examination of its impact on the skills (and instincts) of professionals in the IBM Z universe.
AI-Driven Skill Dilution
Let’s start with a basic fact that I think everyone can agree on, regardless of their philosophical stance: AI tools have made debugging more efficient. They can sift through vast amounts of data with remarkable speed, identify patterns, and even suggest fixes for common coding errors. In mainframe environments, where performance and reliability are paramount, this can significantly reduce downtime and enhance productivity.
However, this efficiency comes at a hidden cost, which is the potential dilution of fundamental debugging skills among technologists. And that’s not just a hypothetical problem, and it is an issue that could potentially have massive long-term negative ramifications.
As AI begins to automate the identification and resolution of bugs, there is a growing concern that developers may start to rely too heavily on these tools. The instantaneous feedback and suggestions from AI can create a false sense of security, leading to a fundamental disconnect between developers and the intricacies of the codebase.
The craft of debugging involves more than just identifying errors; it requires a deep understanding of a system’s architecture, logic flow and the developer’s own code decisions. It is art as much as science, and nuanced knowledge is at risk of being overshadowed by an overreliance on AI-driven suggestions.
A great example of this outside of the tech world is “the knowledge” that London taxi drivers must master before they receive their licenses. It is an arcane system that requires them to know literally every one-way street and roundabout in one of the most confusing cities in the world. But over the last decade, digital navigation tools such as Waze and Apple Maps have rendered this approach obsolete.
It may be more efficient, but the loss of the old ways means that travelers will never get an expert recommendation for the best fish and chip shop in Mayfair. New doesn’t always mean better!
Complacent Developers
Another paradoxical outcome of AI-assisted debugging is the potential increase in developer (over) confidence. With AI tools providing quick recommendations, mainframe developers might feel emboldened to push changes into production without fully grasping the underlying implications.
This can manifest in several ways, including the introduction of new vulnerabilities that a more cautious approach might have mitigated. As the old saying goes, no good deed goes unpunished!
Where AI falls short is that it can write great logically correct code in situ, but it doesn’t understand the nuances of mainframe operating systems like developers do. So everything always has to be “fact checked” by a developer so that the logic runs clean when it’s actually executed in a real environment. This needs to include proper debugging tools to show developers where they have to focus their attention to fix issues quickly and with precision.
Moreover, as AI tools continue to evolve, they often function like black boxes that obscure the rationale behind their suggestions. This lack of transparency can lead developers to trust the tool’s outputs without understanding the basis for the suggested output, thereby hindering their ability to think critically about errors.
As a result, the quality of debugging insight can be compromised, diminishing the opportunity for technologists to learn from their mistakes. To use the London cab analogy, one might end up at a chain restaurant rather than a historic pub because all the navigation tool knows is that you’re looking for “pub.”
Finding the Right Balance
The integration of AI applications into coding practices on IBM Z systems necessitates a new equilibrium that can be best envisioned as a balance between harnessing the efficiencies of AI tools and preserving the foundational skills of debugging. This delicate balance is essential not only for individual developer growth but also for the overall health of mainframe development teams.
The bottom line is that the experience and knowledge that developers need to make sure their code “compiles clean” for executing in a mainframe environment is something that AI agents don’t understand.
Technologists will need to cultivate a mindset that combines AI assistance with critical thinking and systematic troubleshooting. AI can serve as a valuable partner, suggesting solutions and highlighting potential issues, but it should not replace developers’ need for deep understanding and personal engagement with their code. Encouraging a culture of continuous learning and fostering collaboration by pairing seasoned developers with those who are less experienced can help in maintaining the human intuition that is vital to effective debugging.
As AI continues to reshape the landscape of mainframe development, technologists must navigate this evolving terrain with care. The promise of increased efficiency and productivity is alluring, yet the hidden costs of AI-assisted debugging, skill dilution and overreliance, demand a more thoughtful approach.
To ensure that AI enhances, rather than erodes, debugging skills, developers must engage with these tools strategically and through the lens of critical thinking. Acknowledging the limits of AI while embracing its capabilities will be crucial in maintaining the expertise needed to debug complex systems effectively. In an era where artificial intelligence is poised to redefine our craft, the challenge lies in leveraging its potential while preserving the profound human capabilities that form the bedrock of skilled debugging on the IBM Z platform. The journey forward is one that requires a harmonious blend of technology and human insight, ensuring progress and proficiency in mainframe development. In short, we all need to be technologists who are sceptical about technology but willing to engage to optimize productivity.