Would it really matter if you cant code in the future

Lets analyze the comments on Deedy Das’s LinkedIn post where he argues that AI prevents junior developers from understanding code.

What are the key arguments from pro-AI and anti-AI sides?‍

This isn’t just about efficiency—it’s a philosophical clash between abstraction purists (who argue AI is the next logical layer in tech evolution) and fundamentalists (who warn that bypassing basics risks creating a generation of "hollow" developers). Let’s dissect both sides and chart a path forward.

The Abstraction Argument: "Let AI Do the Work"

Proponents like Maxim Dsouza argue that AI is the next layer of abstraction, akin to how high-level languages (Python, Java) replaced assembly code. Their case:

• Democratization: Non-coders (designers, scientists) can build software by describing problems in plain English.
• Efficiency: Why waste time debugging syntax when AI generates flawless code in seconds?
• Evolution: Developers once wrote machine code; today, we drag-and-drop UI components. AI is the next step.

"Soon, software will be built without needing deep programming expertise. Problem-solving, not coding, will reign supreme."
— Maxim Dsouza

The Case for Fundamentals: "AI Isn’t Magic"

Skeptics counter that AI is a tool, not a replacement. Ignoring basics risks:

• Debugging Disasters: AI-generated code often contains hidden flaws (e.g., security gaps, inefficiencies). Fixing them requires understanding how code works.
• Architectural Blindspots: AI writes snippets, not systems. Designing scalable apps demands knowledge of concurrency, databases, and trade-offs.
• The "Prompt Monkey" Trap: Developers who rely solely on AI become stuck when tools fail (e.g., hallucinated APIs, outdated libraries).

Real-World Example:
A LinkedIn commenter shared how a junior dev aced an assignment with ChatGPT but couldn’t explain the code or fix a simple bug. "They had the answer but no understanding," he noted.

Detailed Breakdown:

Pro-AI Arguments:

• Abstraction as Evolution:
  • "Today, you don’t need to understand OS layers to build software. AI is the next step." – Maxim Dsouza.
  • Historical precedent: High-level languages (Python) abstracted assembly code; cloud platforms abstracted servers.
• Democratization of Coding:
  • Non-technical professionals (e.g., scientists, designers) can prototype ideas without coding expertise.
• Efficiency Gains:
  • AI automates repetitive tasks (boilerplate code, debugging syntax), freeing time for creative problem-solving.
  • Example: GitHub Copilot reducing time spent on CRUD operations.
• New Job Roles:
  • Emergence of roles like "AI code curators," prompt engineers, and ethical overseers.

Anti-AI Arguments:

• Skill Erosion:
  • Juniors use AI to generate code but lack understanding of fundamentals (e.g., recursion, memory management).
  • Example: A candidate aced an assignment with ChatGPT but couldn’t explain the code (Utkarsh Gupta).
• Debugging & Maintenance Risks:
  • AI-generated code often contains hidden flaws (e.g., security gaps, inefficient algorithms).
  • Example: Python projects collapsing due to dependency bloat (Paraskevas P.).
• Loss of Problem-Solving Grit:
  • "If you don’t struggle, you don’t learn." – Hank Yeomans.
  • Instant AI answers discourage deep thinking and iterative learning.
• Ethical & Systemic Blindspots:
  • AI lacks context for domain-specific needs (e.g., healthcare compliance, banking security).

Best Arguments from Pro-AI Advocates

What are the strongest arguments from those who support AI use in coding?

Expanded Insights:

• Accelerated Development:
  • Example: AI writes API endpoints in seconds, letting developers focus on user experience.
• Democratization:
  • A biologist with no coding experience uses ChatGPT to analyze genomic data.
• Upskilling Tool:
  • AI explains complex concepts (e.g., "How does a neural network work?") in real time.
• Future-Proofing:
  • As AI handles syntax, developers shift to higher-value tasks: system design, ethics, and cross-domain innovation.

Anti-AI Arguments: Validity in an AI-Dominated Future

Do arguments against AI-dependent juniors hold merit if AI replaces coders?

Detailed Analysis:

Short-Term (Next 5–10 Years):

• Yes:
  • Debugging requires understanding code logic (e.g., fixing race conditions in AI-generated concurrency code).
  • System design demands human intuition (e.g., choosing between microservices and monoliths).
• No:
  • For simple tasks (e.g., scripting), AI efficiency outweighs skill gaps.

Long-Term (10+ Years):

• Shift in Fundamentals:
  • Basics evolve to include prompt engineering, AI validation, and ethical oversight.
• Human Roles:
  • Developers become "problem definers" (identifying what to solve) rather than "problem solvers" (writing code).

Process vs. Outcome: Does It Matter How Problems Are Solved?

If the goal is problem-solving, does it matter if AI or humans did the work?

Deep Dive:

When Process Matters:

• Complex Systems:
  • Example: An AI-built e-commerce app may function but fail under Black Friday traffic without load-balancing logic.
• Innovation:
  • Breakthroughs (e.g., blockchain, React) emerged from deep understanding, not pattern-matching.
• Ethics & Security:
  • AI might suggest a biased algorithm or insecure API. Humans must intervene.

When Process Doesn’t Matter:

Rapid Prototyping:

Example: A designer uses AI to draft a UI demo without worrying about backend logic.

• One-Off Tasks:
  • A script to clean data doesn’t need perfect architecture.

The Hybrid Future: "AI as a Co-Pilot, Not the Pilot"

The truth lies in balance. Here’s how to thrive in the AI era:

a. Learn the Basics—Then Let AI Accelerate

• Foundations First: Variables, loops, algorithms, and system design are timeless. Use AI to explain concepts (e.g., "How does a hashmap work?"), not replace them.
• AI for Grunt Work: Automate boilerplate (e.g., CRUD endpoints), but manually code critical logic to retain intuition.

b. Upskill in AI Collaboration

• Prompt Engineering: Master the art of guiding AI (e.g., "Write a Python function to sort a list, optimized for large datasets").
• Validation & Refinement: Treat AI outputs as drafts. Ask: "Is this secure? Efficient? Maintainable?"

c. Focus on What AI Can’t Do

• Problem-Finding: Identify what needs solving, not just how.
• Ethics & Context: Ensure solutions align with business goals, user needs, and regulations.
• Innovation: AI mimics patterns; humans invent new ones (e.g., quantum algorithms, novel UX paradigms).

Conclusion:

AI won’t eliminate developers—it will redefine them. The future belongs to problem-synthesizers who:

• Leverage AI for speed.
• Rely on fundamentals for depth.
• Focus on creativity, ethics, and systems thinking.

As one developer poignantly warned:

"AI is a bicycle for the mind. But if you never learn to walk, you’ll never know where to ride."

The choice is yours: Will you be a passenger on AI’s train, or the engineer steering it?

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