Reasons to Learn Programming Skills Without AI

🔍 In a recent MIT experiment, three student teams were asked to write software code in Fortran, a programming language none of them knew. The results were pretty interesting: 🥇 The team using ChatGPT finished the fastest 🥈 The team using a specific AI coding assistant (Code Llama) came in second 🥉 The team using just Google search finished last, breaking down the task into components and solving it the old-fashioned way. However, when tested on their ability to recall the solutions from memory, the situation was reversed. The ChatGPT team remembered nothing and failed, while half of the Code Llama team passed, and every student in the Google Search team succeeded. 📚 This experiment underscores a crucial educational lesson: hard work, sweat, and some measure of frustration are essential for learning. Spoon-fed solutions don't stick, and it is the process itself of struggling through problems where the value is. 🤖 As AI coding tools become more available, the demand for developers who can effectively use these tools will grow. But it's clear that solid computer science skills are essential before you can make good use of AI. You will have to learn these the old-fashioned way. 💡 The conclusion from this experiment is simple: there is no substitute for hard work 😊 Don't just rely on AI tools; learn to code, crash, debug, and repeat. Your coding future depends on it. Jason Gulya

“If AI can write code, why should I bother learning it?” Let me tell you why. Think of calculators → They made arithmetic faster. But we still teach children how to add and subtract. Because understanding the basics builds problem-solving skills a machine can’t replace. Coding is the same. AI might write a snippet for you in seconds. But if you don’t understand the logic behind it… ↳ How will you know if it’s correct? ↳ How will you debug when it breaks? ↳ How will you judge if it’s secure, efficient, or even solving the right problem? That’s why coding proficiency matters, now more than ever. Because the future won't belong to people who can prompt AI. It will belong to people who can tell when AI just handed them a beautifully formatted disaster. PS: How would you redesign a beginner coding class if AI was part of the toolkit from day one? #LearnWithSofiat

It’s tempting — you describe a task, and the LLM writes the code for you. Feels magical, right? But here’s the catch 👇 🚫 No Deep Understanding: If you skip learning the logic behind the code, you’ll struggle to debug or optimize it when things break (and they will). 🚫 Limited Problem-Solving Growth: Coding isn’t just about syntax — it’s about thinking in systems. When an LLM does that thinking for you, your analytical edge fades. 🚫 Dependency Trap: You start relying on the model for even the simplest logic. The skill that once made you valuable — structured problem-solving — erodes over time. 🚫 Innovation Requires Intuition: Great developers innovate because they understand — data structures, algorithms, patterns, trade-offs. No model can replicate that human intuition. 💭 LLMs are incredible assistants, not replacements. Use them to accelerate learning, not avoid it. Master the craft first. Then let AI amplify your skill — not replace it. #genai #AI #Coding #LLM #DeveloperGrowth #ArtificialIntelligence #Productivity #Learning

Some truths stay solid no matter what tools we use. Before AI, after AI, yesterday, tomorrow. The fundamentals do not bend. 1. Clear thinking beats clever code. If you do not understand the problem, no amount of generated code will save you. 2. Naming things is still painful. A bad name can ruin an otherwise good idea. 3. You spend more time reading code than writing it. You will read your own mess later. Write so it hurts less. 4. Software engineering is a team sport. Your success depends on how well you unblock others and how well they can build on your work. 5. Asking for help early is a strength, not a weakness. The sooner you ask, the less damage you create. 6. Communication skills matter more than your tech stack. Most real bugs come from people, not syntax. 7. Tests define what is real. AI helps you write code, but it cannot tell you what the correct behavior should be. 8. You will never feel like you know enough. Nobody does. 9. The best engineers improve the system, not just the code. Architecture, processes, handoffs, and clarity matter as much as logic in a function. 10. Curiosity always wins. AI amplifies who you already are. Your desire to learn sets the ceiling. What would you add?

🔍𝗔𝗜 𝗶𝗻 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - 𝗦𝗸𝗶𝗹𝗹 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿 𝗼𝗿 𝗦𝗸𝗶𝗹𝗹 𝗖𝗿𝘂𝘁𝗰𝗵? After 18 years in the software industry, working closely with many engineers (especially junior and mid-level engineers), I’ve always been cautiously optimistic about AI. But I’ve always had a concern: 👉 If AI is used carelessly, it may reduce real learning instead of accelerating it. Today, I found strong evidence supporting that intuition - not just from experience, but from rigorous research by Anthropic: 📌 AI Assistance Can Impair Learning (Coding Skills Study) https://lnkd.in/gygkb_CY 🧠 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀 Anthropic studied developers solving a coding task while learning a new library. Here’s what they found: -> Developers using AI finished slightly faster -> But their understanding was significantly weaker 📉 In a follow-up mastery quiz, AI-assisted developers scored ~17% lower than those who coded without AI. Even more interesting: 🔍The biggest skill gap was in debugging + comprehension - the exact skills required to build robust, maintainable software and to understand why something works (or breaks). 💡 𝗧𝗵𝗲 𝗠𝗼𝘀𝘁 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 Not all AI usage harms learning. The study showed a clear difference between two groups: Passive AI Users: - copy/paste code - accept suggestions blindly - treat AI as an auto-complete machine Active AI Learners: - ask “why?” - request explanations - explore alternatives - validate with their own reasoning And guess what? Active AI learners performed much better. 🚀 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 AI is not a shortcut to expertise. It can make you faster, but speed without understanding is risky. To thrive in the AI era: 1. Strengthen fundamentals (CS basics, design, debugging) 2. Use AI intentionally - like a mentor, not an auto-complete tool 3. Focus on deep understanding and engineering judgment 🎯 👥 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗙𝗼𝗿 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 Don’t just encourage AI usage. Encourage structured learning with AI. Build cultures where engineers: - question AI outputs - explain decisions - learn deliberately - debug deeply instead of patching quickly Because the future belongs to engineers who can do both: 🚀 Move fast with AI 🧠 Think deeply without it #SoftwareEngineering #AI #DeveloperSkills #Leadership #Learning

We’re drowning in options for AI coding help – ChatGPT, Gemini, Copilot, and a whole ecosystem around them. They can spit out code faster than most humans. But here’s the thing not enough people really talk about loudly enough: to truly leverage these tools, you still need to be…a pretty good coder. Relying on AI assistance effectively isn’t passive. It's more like having a super-powered intern – incredibly helpful, but still needing direction and oversight. Think about it. You get the best results when you can: → Formulate precise prompts: “Write me an application” is useless. “Generate a React component that fetches data from this API endpoint with these specific error handling requirements” is way better. → Evaluate generated code: AI isn’t magic. It hallucinates, makes logical errors, and often produces output you wouldn't ship to production without serious review. You need the skills to spot those problems. → Iterate strategically. Asking for a complete application in one go is rarely effective. Breaking down tasks into smaller chunks (“Generate this function”, “Modify this component”), reviewing the results, and requesting targeted changes? That’s where things get really efficient. Essentially, AI coding assistants amplify your existing abilities. They're powerful force multipliers, not replacements for fundamental knowledge. But the problem is: As we lean more heavily on these tools, there's a very real risk of skill decay. If you’re constantly letting AI write the bulk of your code, how much are you actually…learning? How quickly will that muscle memory fade? Maybe even more concerning is the impact on aspiring developers. Why grind through data structures and algorithms when an AI can seemingly do it for you? We might see a generation entering the field with significantly weaker core skills. This isn’t about fearing automation taking jobs (though that’s a valid concern, too). It's about creating a future where we have a workforce dependent on these tools, unable to function effectively when they inevitably hit limitations or require deeper understanding. And let's be honest, those limitations will exist. AI coding assistants are amazing for boilerplate, common tasks, and speeding up development. They’re less reliable for complex architecture, nuanced problem-solving, and genuinely innovative solutions. Maybe that will change. But until then, you still need developers who can think critically and write code from first principles. Learning to code properly is more important now than ever – it’s about understanding why things work, not just copying and pasting AI-generated solutions. We're entering a new era of software development. An era where knowing how to code isn’t becoming obsolete, it’s becoming the crucial differentiator. Don’t get left behind by thinking AI makes coding skills unnecessary.

Junior developers are not allowed to use AI for coding in our teams. That's what a CTO told me last week. At first, that sounded backwards to me. ━━━━━ But his reasoning was simple: If juniors don’t learn to think through problems themselves, they never become seniors. And right now, that pipeline is breaking. ━━━━━ Between 2023 and 2025, entry-level hiring dropped massively. Because many companies thought AI could handle junior-level work. Boilerplate. Simple features. Basic tasks. But that’s exactly how developers used to learn for later. ━━━━━ Before AI: -> Juniors wrote simple code. -> Made mistakes. -> Learned why things break. -> Got better Today: -> AI writes the simple parts. -> Juniors are expected to understand complex systems immediately. -> Without the foundation. ━━━━━ The result? • faster output today • fewer experienced engineers tomorrow • growing dependency on a shrinking group of seniors You can’t skip the learning phase. Software engineering is not typing code. It’s understanding systems. ━━━━━ AI doesn’t remove the need for developers. It increases the need for people who actually understand what’s happening underneath. Strong organizations understand this. They don’t optimize for short-term speed. They invest in capability. Because if you don’t grow juniors today, you won’t have seniors in five years.

A thought-provoking piece by Andreas Møller challenges the seductive promise that AI and no-code tools will let anyone build software 10x faster. The uncomfortable truth he surfaces is that these tools optimise for the wrong part of the problem. They make simple things simpler while making complex things harder. The muscle-training analogy resonates deeply here. When you struggle through a problem manually, you build understanding that compounds. When a tool abstracts away that struggle, you gain speed but lose the cognitive workout that transforms juniors into seniors. The learning curve flattens, but so does your growth trajectory. Experienced developers point out that AI-generated code accrues technical debt at alarming rates if you accept edits unchanged. Your codebase becomes a monument to decisions you never actually made, filled with patterns you do not understand. AI excels at the accidental stuff, typing out boilerplate and scaffolding. But the essential work of system design, security architecture, and managing evolving requirements remains stubbornly human. The real risk is not that AI makes programming too easy. It is that it creates an illusion of competence that masks fundamental gaps in understanding. You can ship a prototype in hours, but debugging it in production requires knowledge you never acquired. For those early in their careers, the counterintuitive advice might be this. Choose the steeper path while you still can. The resistance is the point. https://lnkd.in/ewJ38qU4 #SoftwareEngineering #AI #Programming #TechCareers #CodingTools #SoftwareDevelopment

This way of learning separates backend engineers. It is the foundation for becoming a senior or higher. Learn the “why,” not just the “how.” It is easy to follow a tutorial. It is easy to copy code from AI. It is easy to make something “work.” But without the “why,” you are guessing. When you understand the reason behind your decisions, everything changes: • You make better trade-offs. • You stop introducing new bugs with every fix. • You design with scaling and maintenance in mind. The “how” gets you started. The “why” makes you irreplaceable.

Nobody talks about what coding actually teaches us. Let’s fix that. When you learned to code, you thought you were learning how to talk to computers. In reality, you were learning how to: 💡 Deconstruct vague ideas into precise steps 💡 Debug under pressure with incomplete information 💡 Design systems that can handle edge cases, failure, and change 💡 Model complex realities in simple abstractions 💡 Tolerate ambiguity and iterate your way out of it These aren’t coding skills. They’re thinking skills. And they’re still essential—even now, when LLMs can write entire apps. The demand for the role of “coder” is going down. But the skill of computational reasoning is becoming more valuable, not less—because someone still has to define the problem, evaluate tradeoffs, and verify that the output is even correct. Here are other domains that build the same thinking muscles required to work effectively with AI systems—especially in problem definition, reasoning, system design, and validation: ⚡️ Mathematics Trains logical reasoning, abstraction, modeling, and dealing with edge cases. Especially helpful in understanding probabilities, constraints, and system boundaries. ⚡️ Formal Logic / Philosophy Teaches clarity of thought, identifying assumptions, constructing valid arguments, and spotting fallacies—skills critical when verifying AI-generated outputs. ⚡️ Systems Thinking / Control Theory Encourages understanding how parts of a system interact, how feedback loops work, and how interventions ripple—vital when building robust AI-integrated systems. ⚡️ Scientific Method / Experimental Design Develops skills in hypothesis testing, falsifiability, iteration, and careful observation—key for validating AI behavior and outputs. ⚡️ Debugging & Reverse Engineering (in any domain) Strengthens the ability to isolate causes in complex systems and ask the right questions when things go wrong—essential in AI workflows. ⚡️ Chess, Go, or Complex Strategy Games Build pattern recognition, decision trees, anticipating uncertainty—helpful for reasoning under ambiguous AI outputs. ⚡️ UX Design / Product Thinking Forces you to clarify what the user actually needs, define success metrics, and deal with ambiguity—key when AI is just a tool in the system. ⚡️ Writing (especially editing) Sharpens your ability to structure ideas, clarify intent, and revise iteratively—same mental discipline needed to refine prompts, parse outputs, and validate results. ⚡️ Data Analysis / Statistics Trains you to ask good questions, clean noise from signal, and validate findings—critical when AI-generated results need to be grounded in data. ⚡️ Teaching or Mentoring Builds empathy, abstraction, and the ability to communicate complex ideas simply—vital when guiding others through AI-assisted systems or outputs. The syntax is optional. The thinking is not. --- ✅ Share with others, and follow for more practical tips in practical AI adoption and career in tech.