How AI Will Influence Software Development Demand

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

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

Everyone keeps asking me the same question: “Is my Salesforce Developer job at risk because of AI?” Let me make this simple: Your job isn’t at risk. But your skill set might be. AI isn’t coming for Salesforce developers. AI is coming for developers who still write code the same way they did in 2018. Here’s the truth nobody wants to say: The developers who learn AI → will replace the developers who don’t. And Salesforce is moving faster than ever: Agentforce. Prompt Builder. Data 360. AI-powered development environments. Automations that write half your boilerplate code for you. This isn’t the end of Salesforce development. This is the biggest opportunity we’ve had in a decade. New Roles. New Skills. New Money. • AI-enhanced automation designers • Prompt + Agent builders • Data Cloud + AI orchestration specialists • Integration developers who use AI to deliver 5x faster • Devs who can blend Apex, metadata, and intelligence into real business outcomes So… Will developer demand drop? Absolutely not. Companies don’t want fewer developers. They want developers who can ship faster, smarter, and more intelligently — and AI is the amplifier. If you evolve, you’ll be more in demand. If you ignore AI, you’ll be… well, replaceable. The future is hybrid: You + AI. Learn it. Leverage it. Lead with it.

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?

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.

The rise of AI coding tools doesn’t mean the end of #developers - it signals the start of their next evolution. Despite the rapid adoption of AI in software development, data from IDC and Morgan Stanley Equity Research tells a clear story: 1) The global developer population is projected to grow 10% annually, reaching 60M by 2029 2) The broader software development market (spanning app dev, software quality, and lifecycle tooling) is set to expand 20% annually to $61B by 2029. We don’t need fewer developers. We need more software - and more people to build it. As #coding assistants and AI agents become part of the development stack, developers’ roles will evolve: they’ll act as curators, reviewers, and architects, focusing on higher-order design and architecture while AI handles repetitive coding tasks. And as AI lowers the barrier to entry, expect an explosion of new developers - non-technical professionals who can now build with code-like precision. It’s an incredible time to be building, and #infrastructure software sits at the center of it all. If you’re a founder in this space, I’d love to connect.

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?