Why Aren't AI Tools Making Software Better?
AI tools are undoubtedly transforming coding workflows, offering faster prototyping, code generation, and automated solutions. But despite these advancements, software quality hasn’t improved as dramatically as one might expect. Why is this the case?
Takeaways
#AI accelerates development but cannot replace the deep thinking required for quality and robustness. Developers should:
Addy Osmani explores this paradox in The 70% Problem: Hard Truths About AI-Assisted Coding. While AI accelerates development by enabling rapid iteration and helping with repetitive tasks, it often falls short when it comes to delivering robust, production-ready software.
AI in Coding: Speed vs. Quality
Developers use AI tools in two main ways:
However, junior developers often over-rely on AI, leading to what Osmani calls "house of cards code"—fragile and unsustainable structures that crumble under real-world conditions.
Where AI Excels—and Where It Struggles
AI handles the easy 70% of a project well, but struggles with the last, most complex 30%. This gap can result in endless cycles of bug-fixing and technical debt, particularly when expertise is lacking.
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Why is AI not making software better? AI excels at automating predictable, repetitive tasks, but software development often requires creativity, judgment, and problem-solving—skills that AI cannot replicate. Fred Brooks’ concept of “There is no silver bullet” reminds us that no tool, including AI, can fully overcome the inherent complexity of crafting high-quality software.
The Role of Developers in the AI Era
The rise of AI tools has undeniably transformed how developers approach their workflows, but it’s clear that these tools alone aren’t enough to produce high-quality, robust software. This is where the developer’s role becomes pivotal—not just as users of AI, but as skilled collaborators who bring the essential elements AI cannot provide.
The Future of AI as Collaborative Problem-Solvers
Addy Osmani envisions a future where AI tools evolve into "agentic" collaborators. These tools would not only assist with routine tasks but also take on more complex responsibilities, such as planning and executing tasks with greater autonomy. In such a scenario, AI would act as a partner that works alongside developers, sharing the workload in a more meaningful way rather than simply speeding up repetitive processes.
How AI Complements, but Doesn’t Replace
While this vision is promising, we’re not there yet. Today’s AI excels at tasks like prototyping, code generation, and automating repetitive workflows. However, these tools struggle with higher-level design considerations, nuanced problem-solving, and understanding the broader context of a project. That’s where developers step in.
The Irreplaceable Role of Human Judgment in Software Development
AI lacks the ability to make qualitative decisions or understand the broader implications of design choices. For example, it can suggest code optimizations but cannot evaluate whether these changes align with the project’s overall goals or user expectations. Developers bring this critical perspective, ensuring that the software not only works but also delivers value.
Conclusion
AI is a powerful tool for accelerating development, but it cannot replace the deep thinking necessary for quality and robustness. Developers should use AI to complement their expertise while focusing on refining complex aspects of projects that require human judgment. Emphasizing polish, user experience, and system reliability over speed ensures better outcomes in the long term.
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