How AI Will Influence Software Development Demand

#AIMondayMomentum This morning I came across an old but interesting post on X about Jevons Paradox that triggered a contrary thought in my mind that I found worth mentioning here and would be keen to understand different perspectives. In 1865, William Stanley Jevons observed that when James Watt’s steam engine made coal more efficient, England didn’t use less coal — it used more. Efficiency lowered cost. Lower cost expanded usage. That’s Jevons Paradox: "When something becomes cheaper and more efficient, we use more of it." So what does that mean for AI and software development? If AI makes writing code faster and cheaper, does demand for developers fall? Or does total software production explode? History suggests the latter. We’ve seen similar patterns: • Fuel-efficient cars → more driving • Efficient data centers → more digital proliferation • LED lighting → more total usage • Faster bandwidth → more streaming, longer sessions, higher total data consumption • Serverless / cloud efficiency → total cloud usage skyrockets • CGI in films → higher audience expectations → more visual complexity → larger production teams and higher total costs Efficiency doesn’t shrink demand. It expands ambition. In software, this shows up in three shifts: 1️⃣ The “Infinite Backlog” Effect Every company has ideas that were “too expensive” to build. AI lowers the activation energy. Nice-to-have now becomes viable. Even small businesses can now justify custom tools. The market expands. 2️⃣ Rising Complexity & Maintenance AI accelerates code generation - but volume brings burden. More code. More systems. More integration. The need for skilled humans doesn’t disappear - it shifts toward review, architecture, governance, and alignment. 3️⃣ Role Mutation: The Software Producer The job evolves from writing code to orchestrating systems. Execution becomes commoditized. Judgment, taste, integration, and problem definition become premium skills. Will roles change? Absolutely. Will demand shrink? Not necessarily. If Jevons Paradox holds true, AI may not reduce software jobs — it may redefine and expand them. The real question is not whether AI replaces developers. It’s whether we’re ready for the scale it unlocks relatively rapidly.

People have the impact of AI on software engineering backward. Everyone is worried that AI will put software engineers out of a job. They're wrong. AI will make software engineers more in demand than ever. AI gives engineers incredible leverage. That makes an individual engineer more valuable, not less. Yes, that means it requires less engineers to construct a given piece of software. What everyone is missing is the demand for software is not fixed, it's highly elastic. People underestimate how much value there is to be created from more software and automation. We're going to see the Jevons paradox on steroids. As I've been looking at how companies are deploying AI internally I see a consistent pattern. Once you get past the hype and see what is actually being automated, you find that it is engineers who are driving the adoption of AI (see Klarna or Shopify). I talked to the cofounder and CTO of a 1000-person company this week. He found that in order to get non-engineering functions to adopt AI, they needed engineers to build the right tooling. Systems thinking where you can navigate across multiple layers of abstraction is what you need to realize value. I think we'll see engineers starting to take over and automate other functions. The adoption of AI by engineering teams will be the model for entire companies.

Here's a counterintuitive truth: AI won't reduce the demand for software engineers - it will actually increase it. Let me explain why. Currently, organizations cap their software investments due to: - Unpredictable timelines - Budget overruns - Talent availability - Fragility concerns (if it's working, don't break it) But what happens when AI dramatically improves the productivity and reliability of software development? The economics fundamentally change. Consider a future in which: - Features are delivered consistently and reliably - Quality improves - Failures are dramatically reduced - Costs become predictable - Technical debt is eliminated - Security is enhanced - Teams can tackle more ambitious projects This improved predictability and output won't reduce demand for engineers -- instead, it will unlock previously untapped opportunities to deliver all of the software enhancements that an organization dreams of. The result? A virtuous cycle: - Better software drives business growth - Growth creates new opportunities - New opportunities require more engineering talent - More talent leveraging AI creates better software #FutureOfWork #SoftwareEngineering #AIinTech #TechTrends

Python has become the top programming language on GitHub, driven by AI programming, while Sundar Pichai reveals that over 25% of Google’s code is now AI-generated. This isn’t just a productivity boost -- it’s a shift in how the world builds technology. What does this mean for the future of software development? • Faster time to market: AI accelerates development, helping projects launch quicker. But speed must be paired with robust quality control. • Changing developer roles: Developers are evolving into AI collaborators -- crafting prompts, guiding AI models, validating outputs, and integrating machine-generated code into complex systems. This shift requires developers to master new skills like understanding AI model limitations, debugging AI-generated code, and ensuring ethical AI implementation. • New quality standards: AI-assisted coding brings new challenges, requiring updated code review processes, metrics for maintainability, and rigorous validation of AI-generated snippets. This includes developing new testing methodologies specifically for AI-generated code and addressing the explainability and interpretability of such code. • Transforming education: Future engineers will focus on skills like prompt engineering, model evaluation, and system-level thinking, shifting away from traditional coding-only curricula. • Reshaping teams: Smaller, specialized teams may emerge, focusing on orchestrating AI-driven workflows instead of writing every line of code manually. • The rise of natural language programming: As AI tools rely heavily on natural language prompts, programming itself may shift from traditional syntax to conversational interaction. This raises a critical question: will English's dominance in these interactions widen the accessibility gap or democratize coding for a global audience? • Ethical challenges: AI-generated code raises concerns about intellectual property, accountability, biases, safety, and security. Ensuring licensing compliance, mitigating inequities, addressing vulnerabilities, and building transparent frameworks will be critical to balancing innovation with responsibility. With AI fundamentally transforming software development, are we ready to navigate this new era of opportunity, challenges, and responsibility? #CodingWithAI #FutureOfCoding #ReponsibleAI

I host a CTO dinner every month across the country. Guess how many think AI is replacing their engineers? Answer: 0. None of the guests have thought that engineers are being replaced by AI. Yet there’s much chatter about AI taking over for human developers. Instead, I believe it’s more important to think about how these roles will evolve. We’re seeing AI automatically generate code snippets and detect bugs. We’re seeing a shift to architectural thinking and less of a focus on coding itself. Does that means software developers are going away? No. It DOES mean that human developers will take on more complex work. They’ll be the ones to understand and conceptualize strategy and approaches in the context of your goals. They’ll be the ones to problem-solve and innovate, while AI takes on the cumbersome, rote, and repetitive tasks. The rise of AI means that we need new jobs, too. There will be software that powers the code writing agents, QA agents, task prioritization and orchestration, etc. This is an entirely new software industry that will need developers to write and maintain. I suspect demand (in aggregate) will only increase, even if there are productivity gains happening at the same time. We need human developers. In fact, we need them more than ever. But higher-order skills are reigning king in the face of AI’s rise. It’s essential for today’s engineers to understand the benefits and challenges AI presents and know how to navigate this landscape. Leaders need to think of artificial intelligence as a partner instead of a replacement.

Is AI Replacing Developers? Not Quite Yet... I caught up with Theodora Orji, a prompt engineer at Outlier and software developer, to get her take on how AI is impacting the world of coding. Her perspective? AI isn’t here to replace developers, it’s here to enhance them. 𝗕𝘂𝘁 𝗼𝗻𝗹𝘆 𝗳𝗼𝗿 𝘁𝗵𝗼𝘀𝗲 𝘄𝗶𝗹𝗹𝗶𝗻𝗴 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗮𝗻𝗱 𝗮𝗱𝗮𝗽𝘁. “𝘐𝘵’𝘴 𝘢𝘣𝘰𝘶𝘵 𝘩𝘢𝘳𝘯𝘦𝘴𝘴𝘪𝘯𝘨 𝘈𝘐 𝘢𝘴 𝘢 𝘵𝘰𝘰𝘭. 𝘐𝘵 𝘤𝘢𝘯 𝘥𝘰 𝘢 𝘭𝘰𝘵 𝘪𝘯 𝘴𝘦𝘤𝘰𝘯𝘥𝘴, 𝘣𝘶𝘵 𝘪𝘵 𝘴𝘵𝘪𝘭𝘭 𝘯𝘦𝘦𝘥𝘴 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 𝘵𝘰 𝘨𝘶𝘪𝘥𝘦 𝘪𝘵 𝘢𝘯𝘥 𝘧𝘦𝘦𝘥 𝘪𝘵 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘥𝘢𝘵𝘢. 𝘛𝘩𝘰𝘴𝘦 𝘸𝘩𝘰 𝘭𝘦𝘢𝘳𝘯 𝘵𝘰 𝘸𝘰𝘳𝘬 𝘸𝘪𝘵𝘩 𝘈𝘐 𝘸𝘪𝘭𝘭 𝘵𝘩𝘳𝘪𝘷𝘦. 𝘛𝘩𝘰𝘴𝘦 𝘸𝘩𝘰 𝘥𝘰𝘯’𝘵... 𝘮𝘪𝘨𝘩𝘵 𝘨𝘦𝘵 𝘭𝘦𝘧𝘵 𝘣𝘦𝘩𝘪𝘯𝘥.” This really struck a chord with me. We’re at a turning point where the role of developers is evolving fast. AI can accelerate workflows, eliminate repetitive tasks, and unlock creative solutions at scale. But as Theodora rightly points out, the real power lies in knowing how to wield this new tool. From my perspective, there are three key takeaways: 1️⃣ Embrace AI as a collaborator, not a competitor – Developers who leverage AI to speed up mundane tasks will free up more time for strategic and creative problem-solving. 2️⃣ Upskill Continuously – Staying relevant means learning how to work alongside AI, whether it’s mastering prompt engineering or understanding how to integrate AI models into existing systems. 3️⃣ Focus on Strategic Thinking – AI is great at execution but poor at strategy. Developers who can think strategically and apply AI’s power to business problems will be indispensable. AI isn’t here to replace developers. It's here to enhance them and enable them to do greater things. The question is: are you ready? #AI #SoftwareDevelopment #TechInnovation #Developers #PromptEngineering #DigitalTransformation #FutureOfWork #Upskilling

If AI were replacing developers, why are software engineering job postings surging? (see the chart from Indeed) That’s not a contradiction. It’s a familiar pattern. We’ve seen this movie before: 1) ATMs didn’t “eliminate tellers.” They reduced branch operating costs, banks opened more branches, and tellers shifted toward higher-value work (service, advice, complex transactions). Productivity rose, but the job category evolved rather than vanished. 2) Spreadsheets didn’t “eliminate analysts/accountants.” Excel made calculations faster and cheaper—so firms did far more forecasting, scenario planning, and performance tracking. Productivity rose, and demand for analysis expanded. AI in software is likely following the same logic: Lower cost of building → more building. When prototyping and coding get cheaper, companies greenlight more projects, ship more features, and experiment more. Backlogs don’t shrink— they multiply. Which is why the announcement that Block is cutting 40% of its workforce due to #AI this week should not be applied to the broader economy (story in the first comment). The bottleneck moves “downstream.” AI can draft code, but it doesn’t magically deliver secure, reliable, compliant, maintainable systems. Integration, testing, observability, security, and cost/performance tuning become more important—not less. New work appears. Model-enabled products, internal copilots, data pipelines, evals, and governance create demand for different kinds of engineers. So the headline takeaway: a productivity increase does not automatically mean fewer jobs. It can mean more output, more ambition, and more complementary work—which can raise demand for developers even as AI makes each developer faster. The real question for leaders isn’t “Will AI reduce headcount?” It’s “Will we redesign workflows and products fast enough to capture the expansion?” #AI #SoftwareEngineering #FutureOfWork #Productivity #DigitalTransformation #AIStrategy #Innovation

AI and software jobs — I think we’re asking the wrong question. Most discussions right now are framed as: “Will AI take developer jobs?” That’s not the shift I’m seeing. For decades, we built massive, generic applications. Huge teams. Multi-year roadmaps. Platforms meant to serve everyone. That model made sense when software was hard to build. But we’re entering something different: the age of personalized software. If I need a spreadsheet that does five very specific things for my workflow, I don’t need a vendor roadmap anymore. I can ask an AI agent to build it, refine it, and run it locally. In hours, I have a tool tailored to me — not a feature request sitting in someone’s backlog. I’m not talking about the big foundational software blocks — cloud platforms, databases, operating systems, core infrastructure. Those remain critical and will likely become even more important. What’s changing is the layer on top: the thousands of generic apps and internal tools we used to build with large teams just to get something slightly customized. Scale this shift across millions of users and companies. We may not need giant, one-size-fits-all applications for everything. We may need foundational platforms + composable building blocks + AI that assemble into highly specific solutions. That has real implications:     •    Fewer massive generic apps     •    More personalized tools     •    Smaller teams building highly leveraged systems     •    Creativity becoming the bottleneck, not coding capacity This isn’t the end of software development. It’s the end of software development as we knew it. The question isn’t “Will programmers disappear?” It’s: what happens when everyone can create software for their own needs? We might be entering the most creative era in tech — where ideas matter more than headcount, and the distance between imagination and working software collapses. Curious how others are seeing this. Are we moving from the age of big apps to the age of personal software?

“Will AI make software engineers obsolete?” A student considering their university path recently asked me this, and it’s a fascinating question. Here’s what I told them: If we rewind 20 years, software engineers were learning low-level languages like C++ and assembly. The profession was very different, and companies needed more people to do less. Go back 40 years, and software engineers were using punch cards—something most of us have never seen, let alone used. Today, a single engineer is exponentially more productive than in those days. The point? Technology changes the tools we use, the way we work, and the scope of what’s possible. A single engineer, equipped with AI copilots and automation, can deliver more value than ever before. But this isn’t the end of the profession—it’s a transformation. Here are some of my predictions: 1️⃣ The role of software engineers will evolve. Knowing how to harness AI effectively will become a key differentiator. AI agents and copilots will be as integral to the workflow as version control systems are today. 2️⃣ Small teams will achieve more. Productivity will skyrocket, enabling smaller teams to deliver large-scale impact. 3️⃣ Short-term hiring trends may fluctuate. Companies may recalibrate their workforce as they adjust to new productivity norms. 4️⃣ New opportunities will arise. Startups in industries that have been traditionally light on tech will leverage AI, sparking demand for engineers to build innovative solutions. 5️⃣ Non-tech companies will face a choice. They’ll either invest in their own engineering teams to keep up or risk being left behind. So, will the demand for software engineers go up or down? ⭐ For strong engineers, I believe demand will always be there. ⭐ In the long run, the industry will continue to grow, and those who adapt to new tools and paradigms will thrive. The key takeaway: AI isn’t replacing software engineers—it’s amplifying their potential. #AI #Engineering #SoftwareEngineering

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.