Today, let's explore something we all use but rarely think about: abstraction. Think of it as programming's "magic trick"—making complex concepts appear simple.
In the early days of computing, programming meant working directly with machine-level binary zeros and ones. Early computers used punch cards, where holes represented instructions or data—not always direct binary, but still machine-specific codes. One wrong punch could cause your entire program to fail! Programming in machine code was tedious and error-prone, which led to the invention of assembly language, a symbolic representation of machine instructions. Programs called assemblers translated these symbols into binary machine code. Even with assembly, basic operations required multiple instructions—adding two numbers might take 2-4 lines of code, with explicit management of registers and memory addresses. Later, languages like C emerged, allowing us to simply say, "Hey computer, add these numbers!" without worrying about low-level details like registers or memory management. Each new layer of abstraction made programming more accessible while hiding the complexity beneath.
But C wasn't enough. Managing memory manually? Dealing with pointers? Along came high-level languages like Java and Python, which abstracted away memory management through garbage collection. Python emerged with the motto "Life's too short to type semicolons!" Why remember syntax when we can use plain English? With its clean syntax and extensive libraries, Python let us focus on solving problems instead of fighting with the language. What once took 100 lines of C code could now be done in 10 lines of Python.
Abstraction is generally beneficial—it hides complexity and provides a simple interface to users. However, developers sometimes take abstraction too far. For example, JavaScript developers pushed abstraction to extreme levels. Rather than learning server-side languages, they invented Node.js—because learning a second language seemed like too much work. Now they can build "full-stack" applications with just JavaScript, though both frontend and backend might crash when trying to add 0.1 + 0.2.
Why discuss this now?
We're witnessing another significant level of abstraction in programming: AI agents. This new layer offers extraordinary value to programmers but also allows you to avoid touching your code entirely. You can state requirements in a prompt, and AI agents write, test, and run tools without human intervention. My productivity has increased roughly 2-3x.
Another emerging trend is Vibe Coding, an AI-driven approach where developers describe software requirements in natural language, allowing LLMs to generate and refine code autonomously. This approach shifts programmers from manual coding to guiding AI tools through iterative feedback. While it will undoubtedly lower the barrier of entry for new programmers, there are concerns about code quality and long-term maintainability. Only time will tell how this evolves.
These developments raise important questions about their impact on the next generation of programmers.
When using AI to write a feature, you can expect one of these outcomes: - The feature is completed optimally - The feature is completed with a suboptimal implementation - When tests fail, it gets stuck in an endless cycle of trying different approaches - The feature is completed but inadvertently breaks other parts of your codebase
You have roughly a 25% chance of getting it right the first time. All other outcomes require manual intervention. To get the best out of AI pair programming, I have several recommendations for both new and experienced programmers.
For new programmers, don't rely solely on AI agents to do your coding—they'll limit your growth and understanding. Think of learning to code like learning to fly a plane: While you can use autopilot, you must be capable of taking control if something goes wrong. I recommend writing code like it's 1999 by building calculators, to-do apps, and small games—the "boring" stuff. Master one language like it's your mother tongue. Whether it's Python, JavaScript, or C—pick one and you'll start seeing patterns, not just syntax. While manual coding might slow you down compared to others showing high productivity, find time without delivery pressure to practice manual coding.
When your AI assistant generates problematic code, you'll need to determine if it's due to your prompt or the AI's limitations. Remember that unclear prompts lead to problematic code—AI will implement exactly what you ask for, even if the approach is flawed. It won't tell you when your idea needs work—it'll simply generate code that matches your specifications. If you don't understand how code should function, you won't be able to fix it when it fails. To get the most from AI tools: - Write clear instruction prompts - Ensure every feature is covered by unit tests - Set clear AI rules for your project and workspace - Try different models to find the optimal one for you - Use reasoning models for discussing design decisions
Abstraction remains programming's greatest accelerator and most subtle trap. While AI-powered tools represent the next logical step in our quest to simplify development, they demand renewed discipline. The most effective developers will be those who maintain core coding skills while strategically employing AI assistance - understanding both the "what" and the "how" of their systems. As we embrace these tools, we must remember: true mastery lies not in avoiding complexity, but in understanding it deeply enough to hide it effectively. The future belongs to developers who can harness AI-assisted coding as a pair programmer rather than a dependency, while maintaining fundamental knowledge of how their code actually works.