How AI Will Transform Coding Practices

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

I was convinced AI would transform how we build software. I did not expect it to happen so fast. Over the past year, through conversations with leaders like Thomas Dohmke, startups in the AI software development space, working with the Anthropic team, and observing our own builders at Doctolib, one thing has become clear to me. AI is changing how we think about building software like nothing before. Specs turn into working prototypes instantly. Design systems and architecture principles are continuously reinforced by the tooling itself. Writing production-ready code from scratch is no longer our bottleneck. Tests are generated automatically to validate intent. Complex refactoring is handled by autonomous agents. And this is accelerating. As Ethan Mollick once said: "The AI we use today is the worst AI we will ever use.” Better models enable more capable agent fleets and higher autonomy, which in turn drive even better models As tech builders, our day-to-day job is changing… We don’t focus as much on manual implementation, writing boilerplate, or debugging line by line. Instead, we design the systems and scaffolding that allow AI to do reliable work. We orchestrate agents with the right intents, we validate AI-generated architectures, and we define strict quality guardrails. ….but the outcome doesn’t change: creating better technologies for our users. This is a strong opportunity for all tech companies to innovate faster, but for us even more so in view of the specificities of healthcare and the quality of our technologies and teams. 🔹 AI will help us create more value for our health professionals and anyone managing their health. 🔹 AI will help us tackle all user feedback, bugs and incidents in minutes. 🔹 AI will make us launch more specialties and more countries faster. At Doctolib, we're going all-in on this transformation. Dozens of specialized agents deployed. Our engineering leaders are driving this change, committing code 5x more frequently than a year ago. Teams already deliver significantly more value to patients and health professionals. If you want to join that revolution and contribute to reinventing the daily life of health professionals and improving health for everyone, we welcome all builders. It's only the beginning.

AI-assisted coding isn’t just about autocomplete anymore. It’s becoming a full lifecycle - from planning to building to reviewing. Developers are no longer just writing code, they’re orchestrating systems of agents that generate, test, and refine it. The shift is from “write code faster” to “build and ship systems end-to-end.” Here’s how the generative programmer stack is evolving 👇 𝗕𝗨𝗜𝗟𝗗 - 𝗖𝗼𝗱𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 Full-Stack App Builders: Turn ideas into working applications quickly by generating frontend, backend, and integrations in one flow. CLI-Native Agents: Work directly from the terminal to generate, edit, and execute code with tight control and speed. IDE-Native Agents: Integrate inside development environments to assist with coding, debugging, and real-time suggestions. Async Cloud Coding Agents: Run tasks in the background - writing, testing, and iterating on code without blocking your workflow. 𝗣𝗟𝗔𝗡 - 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 Spec-first Tools: Start with structured specifications that define what to build before writing any code. Ask / Plan Modes: Break down problems, explore approaches, and validate logic before jumping into implementation. Design-to-Code Inputs: Convert designs or structured inputs into working code, reducing manual translation effort. 𝗥𝗘𝗩𝗜𝗘𝗪 - 𝗥𝗲𝘃𝗶𝗲𝘄, 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Code Review Agents: Automatically analyze code for issues, improvements, and best practices before deployment. Testing & Verification: Generate and run tests to ensure reliability, correctness, and stability across different scenarios. Benchmarks: Measure performance and quality using standardized evaluation frameworks. What this means: Coding is shifting from manual effort to guided execution. The developer’s role is moving toward direction, validation, and system design. The edge is no longer just writing better code. It’s knowing how to use these tools together to ship faster and more reliably. Which part of this workflow are you using AI for the most today?

This quote stuck with me. Not because it talks about speed. But because it’s about removing friction - between people, tools, and ideas. We often talk about GenAI as a tool for faster coding. But the real transformation lies elsewhere: 🔹 In how Dev, QA, and Product collaborate from day one 🔹 In how requirements turn into working prototypes - within minutes 🔹 In how architectural standards and test cases get baked into the code automatically What’s changing? ✅ 𝐓𝐰𝐨-𝐰𝐚𝐲 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐜𝐨𝐝𝐞: No more static generators. GenAI tools now understand context, iterate collaboratively, and respect compliance or architecture guidelines from the start. ✅ 𝟏𝟎𝐱 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 - 𝐛𝐲 𝐝𝐞𝐬𝐢𝐠𝐧: GenAI bridges skill gaps, enabling any developer to master obscure languages, security standards, or best practice - without being an expert in all. ✅ 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 𝐛𝐚𝐤𝐞𝐝 𝐢𝐧: Enterprise coding guidelines can be embedded into the AI. Review cycles shrink. CI/CD flows faster. Security improves. ✅ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐮𝐩𝐥𝐢𝐟𝐭: With less time spent on boilerplate code, developers can focus on user experience, innovation, and business impact. Generative AI doesn’t eliminate steps. It synchronizes them. It’s not just faster. It’s smoother. And that might be even more valuable. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝘁𝗲𝗰𝗵 𝗹𝗲𝗮𝗱𝗲𝗿𝘀: How are you rethinking software delivery now that GenAI is not just a prototype, but a partner? #GenAI #SoftwareEngineering #AI #Leadership #TechTransformation #DevOps #FutureOfWork #Deloitte

I used to write code. Now I write prompts. And my productivity has exploded. The most valuable skill for developers today isn't knowing every syntax detail – it's knowing how to explain what you want clearly to AI. I've been building with Claude AI lately. When I provide the right context and guidelines, it generates solutions in minutes that would have taken me days to code myself. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐬𝐡𝐢𝐟𝐭 𝐡𝐚𝐬 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞: 🔹The developer's role is transforming Instead of typing out every line of code, I'm now an architect and director. I focus on the "what" instead of the "how." 🔹Context is the new coding The quality of my output directly correlates with how well I can articulate my requirements. Clear communication beats technical prowess. 🔹Iteration is still king AI doesn't replace the feedback loop - it accelerates it. I can test 10 approaches in the time it used to take for one. 🔹Deep knowledge still matters Understanding fundamentals helps me evaluate AI output, spot errors, and know what's possible. 🔹The productivity gap is widening Developers embracing this paradigm shift are outpacing those clinging to traditional-only methods by orders of magnitude. My workday has transformed from writing functions to writing specifications. 𝐅𝐫𝐨𝐦 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 𝐭𝐨 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧𝐬. 𝐅𝐫𝐨𝐦 𝐡𝐨𝐰 𝐭𝐨 𝐰𝐡𝐚𝐭. This doesn't make development obsolete - 𝐢𝐭 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐬𝐮𝐩𝐞𝐫𝐜𝐡𝐚𝐫𝐠𝐞𝐝. The future belongs to developers who can clearly communicate their vision and leverage #AI as a multiplier. P.S. What percentage of your coding time is now spent writing prompts instead of code? 0%? 50%? 100%?

Let’s take a step back from the GenAI race, which is rapidly making AI accessible to every organization—and that’s a good thing ! For software professionals like myself, I believe the real transformation isn’t just about improving GenAI model performance. 👉 The Software Development Life Cycle (SDLC) as we know it cannot—and will not—remain the same. 💡 So, here are my 10 key opiniated insights on this profound paradigm shift : 1️⃣ The cost of producing code that works is dropping. Whether measured in lines, functions, or user stories, GenAI has the potential to dramatically reduce development efforts—and it’s only getting better. 2️⃣ Man-days as a metric will soon be obsolete. When AI generates full features in minutes, IT organizations must rethink pricing models, effort estimation, and delivery strategies to stay relevant. 3️⃣ Software teams will shrink and specialize, likely aligning with business verticals. Standardized roles and redundant profiles will disappear, leaving only the most adaptable, business-savvy engineers. 4️⃣ Prototyping will be AI-powered and near-instantaneous. Businesses will experiment, refine, and even develop software independently—akin to a "Data Studio for everyone" moment, but for software. Managing this explosion of AI-generated software will be a challenge. 5️⃣ Agile development cycles will become outdated. The concept of 2-3 week sprints will seem archaic as AI enables continuous iteration and real-time feedback, shifting software delivery from weeks to minutes. 6️⃣ Legacy modernization will require far less effort. AI will help reverse-engineer, refactor, and migrate systems, transforming technical debt from a growing liability into a manageable asset. A great codebase will be one optimized for AI agents (by AI agents ?). 7️⃣ Testing will be fully AI-driven. Automated generation, execution, and refinement will make 100% coverage—once seen as wasteful and absurd—the new standard. Operators have the potential to redefine end-user testing, monitoring, and compliance. 8️⃣ Ultimately, IT professionals will shift from coding software to designing and managing AI-powered pipelines. These pipelines, delivered as-a-Service, will (almost) autonomously produce working software tailored to specific business needs. 9️⃣ These AI-powered pipelines will be the backbone of AI-driven software factories. They will natively support multi-variant testing, continuous deployment, and dynamic optimization—turning traditional development into real-time software evolution. 🔟 Software will no longer follow a “develop then release” model—it will continuously evolve. AI will monitor, refactor, and optimize codebases in real time, dynamically adapting to many factors such as user behavior, intent, and system performance. 🚨 The Big Picture ? IMHO, AI is fundamentally reshaping the SDLC, which was originally designed around human experience, speed, and processes. And the pace of change ? Probably faster than we can imagine.

𝐖𝐡𝐚𝐭 𝐇𝐚𝐩𝐩𝐞𝐧𝐬 𝐖𝐡𝐞𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 𝐒𝐭𝐚𝐫𝐭 𝐂𝐨𝐝𝐢𝐧𝐠 𝐓𝐡𝐞𝐦𝐬𝐞𝐥𝐯𝐞𝐬? Sundar Pichai’s recent revelation that AI now writes 25% of Google’s code signals a transformative moment in software development and human-AI collaboration. This is more than just a productivity gain -- it heralds a reimagining of what it means to be a developer and the fabric of digital infrastructure. As AI increasingly handles repetitive coding tasks, software development cycles may shrink dramatically, accelerating the journey from concept to market. In turn, the role of human developers is poised to shift toward higher-order challenges, like strategic problem-solving and creative design. However, this dynamic raises questions about hybridity and the intertwining of human and machine capabilities in ways that may permanently reshape the skillsets and identities in software development. This shift also carries implications for IP ownership and the oversight of digital ecosystems. As AI plays a more active role, IP questions arise -- who owns the code that an algorithm writes, and how do we ensure transparency and accountability in AI-generated code? We’re on the cusp of a future where software could self-optimize and adapt in real time, moving beyond static code to a state of continuous evolution. With this evolution comes the responsibility to preserve human expertise and critical oversight. If we rely too heavily on AI to do the heavy lifting, there’s a risk of eroding essential human skills and ethical discernment, which remain crucial for creating software that is safe, reliable and aligned with societal values. There’s another profound consideration/rhetorical question here: as AI moves beyond assisting to actively shaping digital ecosystems, will we find ourselves at a juncture where the systems we depend on are partly beyond human comprehension? The potential for autonomous, self-refining code is (another) powerful reminder of the need for thoughtful governance and long-term planning in AI integration -- ensuring that as we unlock AI’s full potential, we adopt a responsible and human-centric approach to the technology and the future it is shaping.

Is AI automating away coding jobs? New research from Anthropic analyzed 500,000 coding conversations with AI and found patterns that every developer should consider: When developers use specialized AI coding tools: - 79% of interactions involve automation rather than augmentation - UI/UX development ranks among the top use cases - Startups adopt AI coding tools at 2.5x the rate of enterprises - Web development languages dominate:          JavaScript/TypeScript: 31%          HTML/CSS: 28% What does this mean for your career? Three strategic pivots to consider: 1. Shift from writing code to "AI orchestration"     If you're spending most of your time on routine front-end tasks, now's the time to develop skills in prompt engineering, code review, and AI-assisted architecture. The developers who thrive will be those who can effectively direct AI tools to implement their vision. 2. Double down on backend complexity     The data shows less AI automation in complex backend systems. Consider specializing in areas that require deeper system knowledge like distributed systems, security, or performance optimization—domains where context and specialized knowledge still give humans the edge. 3. Position yourself at the startup-enterprise bridge     With startups adopting AI coding tools faster than enterprises, there's a growing opportunity for developers who can bring AI-accelerated development practices into traditional companies. Could you be the champion who helps your organization close this gap? How to prepare: - Learn prompt engineering for code generation - Build a personal workflow that combines your expertise with AI assistance - Start tracking which of your tasks AI handles well vs. where you still outperform it - Experiment with specialized AI coding tools now, even if your company hasn't adopted them - Focus your learning on architectural thinking rather than syntax mastery The developer role isn't disappearing—it's evolving. Those who adapt their skillset to complement AI rather than compete with it will find incredible new opportunities. Have you started integrating AI tools into your development workflow? What's working? What still requires the human touch?

AI is changing software development, but not in the way many expected. It’s not replacing programmers—it’s shifting the skills they need to succeed. Programming has always been about solving problems, not just writing code. Now, with AI in the mix, the ability to define problems clearly, structure solutions effectively, and debug complex systems is more critical than ever. AI can generate code, but it can’t understand the nuances of a problem or the implicit assumptions behind a solution. That’s still up to developers. Debugging AI-generated code is harder than debugging your own. AI mistakes are different from human mistakes—often subtle, sometimes unpredictable. Code quality and maintainability still matter. Left unchecked, AI-generated code can lead to massive technical debt. The real shift isn’t about writing clever prompts—it’s about managing context. AI doesn’t just need instructions; it needs structured, detailed input. The best results come from those who understand the problem deeply and can translate that understanding into precise guidance. For junior developers, this means the learning curve is steeper. It’s no longer just about mastering syntax—it’s about understanding systems, debugging effectively, and structuring maintainable code. For senior developers, mentorship is more important than ever. The next generation of engineers won’t learn by just watching AI generate code; they’ll learn by working through complex problems with experienced guidance. Ignoring AI isn’t an option. But using it well means recognizing its limits, refining how we work with it, and staying focused on the fundamentals of good software development. AI isn’t the end of programming—it’s a new beginning. Mike Loukides, Tim O'Reilly

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?