How AI Affects Coding Careers

RIP coding? OpenAI has just introduced Codex — a cloud-based AI agent that autonomously writes features, fixes bugs, runs tests, and even documents code. Not just autocomplete, but a true virtual teammate. This marks a shift from AI-assisted to AI-autonomous software engineering. The implications are profound. We’re entering an era where writing code can be done by simply explaining what you want in natural language. Tasks that once required hours of development can now be executed in parallel by an AI agent — securely, efficiently, and with growing precision. So, what does this mean for human skills? The value is shifting fast: → From execution to architecture and design thinking → From code writing to problem framing and solution oversight → From syntax knowledge to strategic understanding of systems, ethics, and user needs As Codex and other agentic AIs evolve, the most critical skills will be, at least for SW tech roles: • AI literacy: knowing what agents can (and cannot) do • Prompt engineering and task orchestration • System design & creative problem solving • Human judgment in code quality, security, and governance It’s a new world for solo founders, tech leads, and enterprise innovation teams alike. We won’t need fewer people. We’ll need people with new skills — ready to lead in an agent-powered era. Let’s embrace the shift. The real opportunity isn’t in writing code faster — it’s in rethinking what we build, how we build, and why. #AI #Codex #FutureOfWork #SoftwareEngineering #AgenticAI #Leadership #AIAgents #TechTrends

Will #AI #replace software #engineers? Top engineers are paid not for their ability to script (to express themselves in code) but for their ability to understand the architecture of software systems and to write the kind of code that plays nicely with the rest of their organization’s complex codebase. They know that their work is more than simply translating their thoughts into a programming language, it’s about understanding the implications of their technical choices. While AI will effectively automate the former, automating the latter is a tall order. Here's my answer, along with others, featured in this ZDNET piece: https://bit.ly/zdnet_swes LLM coding assistants can't guarantee 100% reliable results. The 'generative' in GenAI means the output is randomly sampled from a distribution of likely responses based on your prompts. So you can get endless answers to the same question—some helpful, others far off the mark. Commercial #LLMs have some error-checking under the hood, but it's not bulletproof. Even human experts can’t guarantee perfect results, which is why organizations keep someone on call around the clock to fix problems and respond to system outages. But anticipating the consequences of code you wrote is often easier than anticipating the consequences of AI-generated code. Expect more surprises, less reliability, and more technical debt as more code is written by AI agents without human oversight. Where performance matters, software engineering agents are unlikely to eliminate the work—they’ll just shift it from writing the code to explaining and reviewing it, which isn't always a win. Engineers will find themselves playing archeologist in the AI’s mistakes. Most coders will tell you it's far more fun and fulfilling to write code yourself than read someone else's. AI-generated labor at scale sounds great on paper, but someone will still need to monitor the bots, fix their mistakes, evaluate edge cases, maintain long-term systems, and ultimately take responsibility. 🍼 Unless we're careful, we risk replacing builders with babysitters. It's up to us how that plays out. 🍼 My advice to software engineers is threefold: 1) Double down on precise thinking. Whether prompting or coding, the key skill is explaining your wishes to the machine in the way that gets you the most reliable outcome. 2) Become an expert in complex systems. Agent-generated software will dramatically increase the complexity of the systems you’ll be architecting solutions for, so tomorrow’s engineering challenges will be harder than today’s. 3) Work on human skills that bots can’t replace: sound decision making, the mental agility to adapt to rapidly changing technologies, the critical thinking frameworks needed to complement AI insights, and a deep understanding of systems architecture. Please ✨ repost ✨ so the message doesn't vanish in the abyss of social media... subscribe to my newsletter at https://lnkd.in/ePiCimXg

🔥 Hot take 🚨: AI won't replace developers - it radically shifts the skills needed to be a great developer. This is not being talked about enough. In the age of AI, we need to rethink what we teach people entering the software development profession. Writing code is no longer a marketable skill. The new tools and materials AI provides us with require us to step up the ladder and reshape our skills and jobs. New primary developer skills in the age of AI include: - Describing the function of an application and its parts (system design) - Describing advanced unit tests (ensuring the code actually does what it says) - Staying current with the latest standards (the AI is inherently conservative and will surface prevalent older patterns over new standards every time) - Red-team testing (ensuring the code doesn't go off the rails) - Validate maintainability (the code must remain understandable to humans) The value of a coder used to be measured by their ability to write quality code. The AI coding assistant can now do that in a fraction of the time. The new value of a coder is guiding the AI coding assistant to write the right code, at the right time, for the right context. AI coding assistants are great at writing boilerplate code and flashy demos, but terrible at writing enterprise-level applications. Why? Because while they can reproduce most coding patterns, they have no understanding of the purpose of the code nor how it fits into the larger context. Coding in the age of AI is a different job with different skills: The developer is no longer a coding machine but a senior manager of a highly skilled code writer with zero real-world experience or understanding.

Are all Software Engineers gonna be unemployed as AI gets better at coding? Nope. The Software Engineering job consists of many tasks, and writing code is just one of many that we do. From my time at Pinterest and Meta, I spent ~40% of my time actually writing code. The remaining time was spent collecting information, reading documentation, helping coworkers, or debugging. As AI coding tools become more prominent, more of our time will shift away from simply writing code. Therein lies the answer to adapting for the future: become amazing at “filling in the gaps” for the work that the AI can do. Utilize AI for rote coding tasks, but maintain your critical thinking skills for the significant part of your job that goes beyond writing the code. Here are examples of premium skills going forward: - Debugging and reviewing AI-generated code - Monitoring software and working with other teams (humans) to fix issues - Decomposing a business objective into smaller milestones that we could feed into an AI The ultimate currency in the workplace, both now and in the future, is trust. Are you trusted to be accountable for your work, or are you simply parroting what the AI is spitting out? Whether you’re a new college grad or a seasoned engineer, you must evolve your role in the knowledge economy to become a productive collaborator with AI: https://lnkd.in/gCabkUGt

Is AI changing the way we write code? Absolutely. But are we really thinking about what it’s doing to the profession of software engineering? In the last few months, I’ve heard polarizing opinions on this topic: “LLMs can’t be trusted for coding. They generate flawed logic.” “Who needs software engineers anymore? AI can do it all!” I believe both extremes are missing the point. Tools like GitHub Copilot, Cursor, and Claude are now common in developer workflows. Senior developers are using them to generate boilerplate code, write tests, and debug faster. But here’s the catch: these tools work well only if you already know how to code. You still need judgment, intuition, and experience to spot bugs, fix logic issues, or make design decisions. If not, you may end up in an endless loop of prompting the LLM to fix its own mistakes – sometimes making things worse. Traditionally, junior engineers learned by struggling through bugs, fixing them, writing documentation, and getting feedback in PRs. That hands-on messiness built real capability over time. It’s how they became senior engineers. Now, with AI doing most of the “hands-on” work, what happens to that learning curve? 👉 Do junior engineers become passive observers? 👉 Do they just prompt and click “accept” on AI suggestions? 👉 Or worse – do they stop getting hired altogether? If we cut out junior roles in the name of “efficiency,” we’re hollowing out the future pipeline of senior engineers. This is not just about code quality or productivity anymore. It’s about the long-term health of the profession. We still need experienced engineers – the ones who can write secure, scalable, maintainable code. But we won’t have those in 5–10 years if we don’t invest in growing junior talent today. Senior developers aren’t born. They’re built. Through real-world struggle, feedback, and time. Yes, Gen AI is here to stay. Yes, it will change how we write software. But we can’t treat engineering as a task that can be fully outsourced to AI. Let’s be intentional in how we adopt these tools: ✅ Use AI to amplify expertise, not replace learning ✅ Focus on mentorship and review, not just output ✅ Value growth over short-term speed This applies not just to software – but to every profession AI is starting to touch. If you’re a tech leader, hiring manager, or founder – it’s time to ask: 🚀 Are we building the future of engineering – or breaking it before it gets there? I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

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 professionals as we used to know them are being replaced by Artificial Intelligence. Why be cynical and deny it? We’re seeing a clear trend: many hi-tech companies are either letting juniors go or freezing entry-level hiring. Some are restructuring around AI initiatives, retaining or hiring experienced engineers at the expense of juniors. The new must-have skill is the ability to work effectively with AI. As someone working in a company that from day one has hired senior engineers only, I get it. But as an educator working with middle schoolers in a technology specialty, I can’t help but wonder: where does this leave junior engineers trying to insert themselves in the workforce? A recent article by María Eugenia Fiore Fiore and Juliana Rios highlights a reality: AI is fundamentally changing what it means to start or evolve a career. The World Economic Forum’s 2025 Future of Jobs Report says 66% of businesses plan to hire talent with specific AI skills. It’s pretty clear: as we collaborate more with AI agents, adaptability and discernment become fundamental. We need to know what to delegate to AI, and what still requires a human touch, discovering along the way how this technology can help us work smarter. Nahuel Vigna summed it up well in an article he wrote last month: AI-powered code assistants are automating the simpler, repetitive tasks that used to be junior territory. So, yes, the traditional junior developer role is one of the first to be transformed by AI. The question is: how can they transform to stay relevant and valuable? It might look discouraging for students and freshly graduates, but I believe there’s a silver lining: the definition of “junior” is evolving. AI-native juniors (what I like to call the next generation of juniors) are rapid learners and creative problem-solvers, empowered by AI. Their goal shouldn’t be just to “land a junior job”,  but to reach higher levels of knowledge and autonomy, and to stand out in a market where “anyone can code” is the new myth. I cannot say this louder: if you’re a junior, treat AI as a mentor, not a shortcut. See yourself as a future senior in training — like an elite athlete who trains as if they’re already a champion. That’s the mindset that leads to success in the AI-driven economy. #AI #FutureOfWork #Upskilling #AILiteracy #Education #CognitiveTransformation #CloudX

At Fueled, where we hire dozens of engineers every year, we expect AI to completely change how we evaluate talent. The shift is more fundamental than most people realize. Previously, engineering hiring processes focused on three things: code quality, architectural thinking, and cultural fit. But AI has made the first dimension obsolete. Code quality is now table stakes. We expect candidates to use AI tools as part of their workflow. Testing pure coding ability? That's missing the point. Instead, we're looking for engineers who can think WITH AI, not just use it. The best candidates: - Break problems into steps rather than (attempting) one-shot coding - Tell stories about keeping AI on track and recognizing its limitations - Know when AI reaches its limits and step in manually - Understand that these tools need supervision, not blind trust The implications are profound. For junior engineers: This shift is tough. Most juniors excel at discrete coding problems but struggle with systems thinking and decomposition. The bar has moved. Junior engineers need to level up their architectural thinking faster than ever. For experienced engineers: Those with entrepreneurial energy thrive. They push tools to their limits, find novel solutions when AI breaks, and see problems through a systems lens. The cultural fit criteria hasn't changed. We still need a commitment to quality, entrepreneurial energy, and clear communication. But now, architectural thinking and the ability to orchestrate human-AI collaboration matter most. This isn't just about adapting to new tools. It's about fundamentally rethinking what it means to be a skilled engineer in 2025. 💡 Prediction: In five years, the most valuable engineers won't be those who code the fastest—they'll be the ones who can orchestrate complex human-AI systems at scale. How is your engineering team adapting to this new reality?

A technical lead recently told me, "I don't have tasks for entry-level engineers on my team. AI coding assistants are doing a better job, and I can skip the mentoring efforts." That hit hard—and it’s a growing sentiment in the industry. AI coding assistants are changing the landscape. They handle everything from code completion and debugging to generating entire code blocks from natural language prompts. Developers using these tools report finishing tasks up to 55% faster. But there's a catch. The entry barrier to becoming an individual contributor has just gotten higher. Fewer companies are willing to invest in entry-level programmers, and traditional growth paths are being disrupted. And if juniors rely too heavily on AI, they risk missing out on foundational skills—deep debugging, core logic comprehension, and hands-on experience. This can result in "hollow" expertise that hinders long-term growth. Yet, this isn’t just a threat—it’s a massive opportunity. Junior developers who treat AI tools as learning companions—not crutches—can actually accelerate their careers. By pairing AI’s power with critical thinking, rigorous practice, and strong fundamentals, juniors can cultivate skills that AI can’t replicate. The key is intentional adaptation: - Treat AI as your pair programmer, not your replacement. - Prioritize human-centric skills like creativity, communication, and critical thinking. - Sharpen your abilities in debugging, code review, and prompt engineering. The future of software development isn’t AI vs. humans—it’s humans who know how to work with AI. What’s your take? Are you seeing this shift on your team?