Will AI Fix Modern Software — or Just Make the Same Problems Faster?
As artificial intelligence becomes increasingly integrated into software development, a critical question emerges: can AI truly improve the quality and reliability of software, or will it simply accelerate the same longstanding issues? This debate was recently explored in an episode of Dave’s Garage: Shop Talk, where retired Microsoft operating systems engineer Dave Plummer and co-host Glen Hodges examined whether AI-assisted development leads to better outcomes or merely automates existing flaws.
The discussion centers on a growing concern in the tech industry: despite advances in hardware and development tools, many users experience software that feels slower, more bloated, and less reliable than expected. Plummer and Hodges argue that this paradox stems not from a lack of capability, but from systemic patterns in how software is built and maintained.
The Abstraction Layer Problem
One of the key factors contributing to software inefficiency is the accumulation of abstraction layers. Modern applications often rely on multiple layers of frameworks, libraries, and APIs that simplify development but introduce performance overhead. Each layer adds complexity, increasing the potential for bugs and slowing down execution.
Plummer notes that while these abstractions enable faster development and greater accessibility for programmers, they often come at the cost of transparency and control. When developers rely on code they do not fully understand or cannot modify, debugging becomes more demanding and performance issues harder to trace.
AI: Accelerator or Solution?
The hosts examine whether AI code generation tools address these challenges or exacerbate them. While AI can produce functional code quickly, it often does so without deep contextual understanding of system constraints, performance requirements, or security implications.
As Hodges points out, AI tends to replicate patterns found in its training data — which includes both exemplary and flawed code. Without careful oversight, this can result in the propagation of inefficient practices, redundant logic, or vulnerabilities that mirror those already prevalent in modern software.
Crucially, the effectiveness of AI in software development depends not on the technology itself, but on how it is used. When employed as a tool to assist experienced developers — rather than replace their judgment — AI may facilitate reduce boilerplate perform and accelerate prototyping. However, if used to bypass scrutiny or reduce investment in skilled engineering, it risks increasing technical debt and lowering overall software quality.
Third-Party Scripts and Attack Surface
Another major concern highlighted in the discussion is the widespread use of third-party scripts — external code loaded from domains outside a developer’s control. These scripts, commonly used for analytics, advertising, or embedded widgets, represent one of the largest attack surfaces on the web.
Plummer emphasizes that many security breaches are not the result of sophisticated attacks, but of basic oversights: failing to vet external dependencies, neglecting update mechanisms, or granting excessive permissions to unverified code. AI-generated suggestions that encourage reliance on such scripts without proper safeguards could worsen this trend.
What Separates a Good Developer in 2026?
Despite technological advances, the hosts conclude that the qualities defining effective software development remain fundamentally human: critical thinking, system awareness, and a commitment to simplicity and maintainability. Good developers understand trade-offs, question assumptions, and prioritize long-term stability over short-term convenience.
They too stress the importance of continuous learning and skepticism — especially when evaluating new tools like AI. Rather than adopting innovations uncritically, developers should assess how those tools impact performance, security, and clarity.
Key Takeaways
- Modern software often feels slower due to increasing abstraction layers and complexity, not hardware limitations.
- AI can accelerate code generation but may replicate existing inefficiencies if not guided by expert judgment.
- Third-party scripts remain a major security risk, often exploited due to poor oversight rather than advanced attacks.
- The best defense against declining software quality is skilled engineering — not automation alone.
- AI’s value lies in augmenting, not replacing, human expertise in software development.
Frequently Asked Questions
Can AI write better software than humans?
Currently, AI excels at generating syntactically correct code based on patterns in data, but it lacks the contextual understanding to consistently optimize for performance, security, or maintainability. Human oversight is essential to ensure quality.
Why does software feel slower even with faster hardware?
Increased reliance on abstraction layers, third-party dependencies, and feature bloat often offsets hardware gains. Each added layer introduces latency and potential points of failure.
Are third-party scripts dangerous?
They can be. Loading code from external sources creates risk if those sources are compromised, untrustworthy, or poorly maintained. Vulnerabilities in third-party scripts are a common vector for attacks.
How should developers use AI responsibly?
AI should be used as a productivity aid — for example, to generate boilerplate code or suggest solutions — but all output must be reviewed, tested, and integrated with awareness of system-wide impacts.
What makes a developer effective in the age of AI?
Strong fundamentals in computer science, a habit of questioning assumptions, proficiency in debugging, and a focus on writing clear, maintainable code remain more important than ever.