I’ll admit it---I use AI tools constantly in my software development work. They’re genuinely impressive specialized tools that help me generate code snippets, brainstorm solutions, and debug tricky problems in my software engineering projects. They’re becoming an essential tool in the modern digital world.
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The Double-Edged Sword of AI in Development
AI tools have transformed how we approach software development processes. They can:
- Generate boilerplate code
faster than I can type - Suggest optimizations for software architecture
- Help debug complex issues in various programming languages
- Serve as an always-available
pair programmer - And I like to leverage ai to help practice my Portuguese
But here’s my worry: we need to be thoughtful about how we integrate these tools into our workflows. AI models have significant limitations. They confidently present incorrect information, contradict themselves, and inevitably reflect biases in their training data. This means we always need to carefully review what they provide before incorporating it into our software solutions.
The Training Data Challenge
Here’s something I’ve noticed that doesn’t get discussed enough: AI tools are primarily trained on solutions to problems we’ve already solved. This creates an interesting dilemma:
For familiar problems in software development, AI might give you a workable solution (though quality varies based on your prompting skills)
For truly novel problems requiring innovative approaches, AI models become dramatically less reliable and even hallucinate more.1
What keeps me up at night? When software engineers rely too heavily on AI-generated solutions, their own problem-solving abilities can weak. It’s like calculator dependency---convenient in the moment, but potentially limiting your growth as a skilled engineer over time.
This Is Different from the Search Engine Revolution
Sometimes I compare today’s AI-driven future to when search engines revolutionized computer science in the 90s, but I feel there’s a crucial distinction:
Search engines let you choose between:
- Discovery: browsing different results to understand topics deeply
- Application: clicking the top result and implementing immediately
However, AI tools typically push you straight to application---here’s THE answer! You might explore alternatives if the first solution fails, but the default is accepting what you’re given. (Sometimes I appreciate this about AI when I’m stuck in decision paralysis.)
Both discovery and application are vital for software development teams. Remove discovery from the equation, and application becomes increasingly unstable as your projects grow more complex. Much like how LLMs struggle to write performant code2. What I’m trying to say is there are many blind spots when it comes to AI’s performance.
The Fundamental Skill at Risk
Computer science emerged because humans wanted to solve problems more efficiently. We built machines to handle repetitive tasks so we could focus on bigger challenges.
But I worry we’re losing our grip on this process---not because software developers are less capable, but because the pressure to deliver quick software solutions is overwhelming. I feel this pressure in my own development process every day. "Just write it with AI" says every boss these days. Many times making skilled software engineers choose between shipping code quickly, or shipping code that runs with little or no bugs.
In chasing these instant results, we risk losing something fundamental: focused thinking, focused problem solving. And focus, like any technical skill, requires consistent practice to develop and maintain.
What This Means for Your Future in Software Development
I’ve spent years watching emerging technologies evolve, and here’s what concerns me: we’re potentially training a generation of engineers who excel at prompting AI but struggle with solving problems from first principles.
The most valuable tech skill you can develop isn’t just “AI fluency”---it’s the ability to think deeply without distraction, to understand problems at their core, and to build software solutions from foundational concepts. This focused thinking is what will differentiate extraordinary software engineers from those who simply know how to ask AI agents the right questions.
In a world of instant answers, the people who can truly focus will be irreplaceable in tech industry roles. They’ll see connections others miss and solve problems that machine learning can’t touch. Please don’t trade that superpower for convenience.
Finding Balance in Our AI Relationship
Listen, I’m not suggesting we abandon AI tools---they’re transforming our software engineering work in amazing ways by handling repetitive tasks and letting us focus on human creativity. My team’s developer productivity has genuinely improved since leveraging AI in such a way that complements our strengths.
What I am advocating for (based on painful personal experience) is mindfulness in how we use them:
Use AI to accelerate your work, not replace your thinkingPractice solving problemsfrom scratch regularly to maintain your critical thinking skillsView AI as a collaboratorin your software engineering journey,not as a substitute for deep understandingCreate intentional space for discoveryalongside application
The future belongs to skilled engineers who can blend human creativity with artificial intelligence---using these powerful tools while maintaining the core skills that make them excellent software developers.
What’s your experience with AI tools in your development process? Have you found ways to streamline workflows while keeping your critical thinking sharp? I’d love to compare notes as we’re all figuring this out together. What do you think the future of software engineering is?