Much has been said about AI decimating the job market for developers. In an industry changing this quickly, we certainly can’t blame people—especially junior and aspiring engineers—for worrying that the AI automation wave might sweep their jobs out from under them.
More existentially, some are wondering whether the age of AI, and particularly the rise of vibe coding, signals the demise of software development. But reports of its death are, to paraphrase Mark Twain, greatly exaggerated.
Not only is there a future for software development, but we’d like to suggest that we’re on the cusp of enormous demand for code developed by humans. From our perspective, AI represents a platform shift that’s changing what it looks like to build software and ushering in a period of explosive demand for ambitious, innovative, and highly specialized code.
A recent conversation between Stack Overflow CEO Prashanth Chandrasekar and OpenAI Head of Developer Experience Romain Huet got us thinking about how developers will build everything that’s suddenly becoming possible. Let’s explore how AI will drive new jobs (and new ways of approaching those jobs) for developers.
Anytime you want to understand where you’re headed, look at where you’ve been. AI isn’t the first major platform shift, and each of those shifts has fundamentally changed how we work.
In the mid-90s, the internet emerged as a mainstream technology. Handwritten college applications gave way to online forms. Physical libraries became digital repositories. Entire business models that couldn’t have existed before—ecommerce, search engines, social networks—became ubiquitous.
Then came mobile computing and the cloud. Arguably, they’re part of the same shift: The client-server model for the early internet was browser-data center; it evolved into mobile device-cloud.
Smartphones changed where and how we interact with technology. Apps went from something you ordered with drinks to the world’s interface. Mobile-first companies proliferated. Again, fears of job displacement gave way to whole new careers: mobile developers, UX designers.
Cloud computing abstracted away the complexity of managing physical infrastructure. DevOps emerged as a discipline. Companies that once needed massive IT departments could spin up global-scale applications overnight. More abstraction, more possibility, more jobs.
Like these seismic shifts, AI is redefining how we learn, create, and solve problems. Consider the evolution of abstractions in learning to code. Once, you learned from textbooks, painstakingly working through examples and asking classmates or instructors if you got stuck. In 2008, Stack Overflow democratized that knowledge. Suddenly, you could tap into the collective wisdom of millions of developers worldwide, finding answers to problems that would have taken hours to solve. That was a major abstraction layer: from personal networks to global knowledge sharing.
Now AI coding assistants have introduced another abstraction layer. We’ve gone from searching for solutions to conversing with an intelligent system that can generate, explain, and iterate code in real time.
None of these abstraction layers eliminated the need for developers. Instead, they changed what skills and experiences organizations were looking for. They unlocked new possibilities and drove demand for people who could build them.
Prashanth Chandrasekar, Stack Overflow’s CEO, is a lifelong Trekkie. When asked about how AI will drive demand for code, he points to the technology of the Starship Enterprise: Replicators that materialize objects from thin air. Holographic environments indistinguishable from reality. Voice-activated AI that anticipates crew needs. Warp drives that fold space-time.
“Once you imagine something,” Chandrasekar observes, “it’s inevitable that we’re gonna go build it at some point.”
The human mind is an imagination engine, constantly coming up with better ways of doing things. Each of those imagined futures requires software to become reality.
For every solved problem, we discover new ones to fix. In curing one disease, you might discover biomarkers that point to five others. In optimizing one supply chain, you might recognize inefficiencies in related systems and understand how to fix them. In building one AI capability, you might imagine a dozen other applications (If it can do x, what about y?). Progress doesn’t satiate our ambitions; it whets them.
Consider one domain AI is reshaping: Drug discovery is becoming, at least in part, a computational problem. Scientists are moving from trial-and-error chemistry to AI-guided molecular design. Simulations that once took months now take days. Coming from a family of doctors, Chandrasekar reflects, “It’d be amazing if we could use AI to actually solve or cure some of the world’s biggest ailments that debilitate a lot of people.” Every disease we target, every biological pathway we map, every personalized treatment we develop—all of it requires sophisticated software, maintained and improved by developers.
Look at any AI market map and you’ll see thousands of companies, each attacking a different layer of the stack or a different vertical application. Venture capitalists are funding this explosion because they see the writing on the wall: AI is fracturing into myriad specialized niches.
This Cambrian explosion is driving demand for developers across every layer:
- The hardware layer is in full reinvention mode. General-purpose CPUs are giving way to specialized AI chips: GPUs, TPUs, neuromorphic processors, quantum computing experiments. Each architecture requires firmware, drivers, optimization libraries, and toolchains. Semiconductor companies are hiring engineers to build the physical infrastructure required.
- The model layer is diversifying rapidly. There’s a proliferation of specialized models fine-tuned for specific domains, from medical diagnosis and legal document analysis to code generation, image synthesis, and protein folding. Each model needs training pipelines, evaluation frameworks, deployment infrastructure, and continuous improvement cycles, driving demand for data scientists and ML engineers.
- The infrastructure layer is being rebuilt for AI workloads. Serving LLMs efficiently requires new approaches to compute allocation, caching strategies, load balancing, and cost optimization. People are building entire businesses around making AI inference faster and cheaper. Every one of these businesses needs engineers who understand distributed systems, performance optimization, and the unique characteristics of AI workloads.
- The application layer may be where the most explosive growth is happening. Every industry, every workflow, and every use case is being reimagined with AI as a central component. Legal tech companies are building AI contract analyzers. Financial services companies are designing fraud detection systems. Manufacturing companies are working on predictive maintenance platforms. Educational companies are creating personalized learning systems. You get the idea.
Each of these layers requires people who understand both traditional computer science fundamentals and how to work effectively with AI tools. Legacy systems need to be integrated with AI capabilities, which is less an AI-specific problem than a systems integration challenge. New systems should be built for reliability, security, and scale; these fundamentals haven’t changed just because AI is involved.
The truth is, what it means to be a developer is changing. As an industry, we’re moving from writing every line of code by hand to orchestrating AI agents that generate code. We’re shifting from solving known problems with established patterns to exploring new problem spaces. Instead of being limited by our personal bandwidth—how much code we can personally write—we’re limited by different factors: our imagination, our judgment, our expectations.
In concert with this change, new roles are emerging that didn’t exist even a couple of years ago:
- AI orchestrators manage teams of AI agents, assigning tasks, reviewing outputs, and ensuring that coherent systems emerge from multiple AI collaborators working in parallel.
- Prompt engineers with domain expertise understand both technical domains and how to elicit the best performance from AI systems. They know which questions to ask and how to evaluate outputs because of their deep subject matter expertise.
- AI QA specialists develop specific testing frameworks for AI-assisted development to ensure that AI-generated code meets production standards.
- Human-AI collaboration architects design workflows that combine human judgment with AI capabilities. Their role is to figure out which tasks should be automated, which require human oversight, and how to create feedback loops that improve both.
The collaboration model between humans and AI is multiplicative, not substitutive. That’s what makes it powerful. As Romain Huet, OpenAI’s Head of Developer Experience, notes about his own team: “We have completely changed the way we work this year. We rarely leave our desk without sending a task to an AI agent because that would be a waste of time.”
Rather than replacing developers, the multiplicative model gives them teammates to tackle the tedium while they focus on higher-order problems. When teams like Huet’s have reliable AI agents taking on well-defined work, their ambition scales. They dream bigger because their capacity and capabilities have expanded. Projects that seemed too formidable to seriously contemplate are suddenly within reach.
The developers who thrive in this environment aren’t the ones who resist AI on principle or those who trust it blindly. They’re the ones who understand the fundamentals of their field deeply enough to guide, evaluate, and effectively collaborate with AI.
Let’s get concrete about where developer demand is growing.
Large companies are going through transformation. Some are optimizing headcount in areas where AI can genuinely automate routine work. But they’re simultaneously expanding in AI integration teams, platform teams that build internal AI capabilities, and application teams that reimagine products with AI as a core component.
The startup explosion is where the most visible growth is happening. These companies need founding engineers, early technical hires who can build fast and navigate uncertainty. The demand for engineers who can thrive in startup environments—people who combine technical depth with product sense—is real.
Cross-industry opportunities may represent the biggest untapped market. Industries that have been relatively slow to adopt cutting-edge technology, like finance, manufacturing, education, agriculture, and transportation/logistics, are now under pressure to integrate AI. They have decades of technical debt and greenfield AI opportunities sitting cheek by jowl. They need developers who are AI-literate but also understand domain-specific requirements, regulatory constraints, and existing systems.
The skills premium is significant for developers who understand both fundamentals and AI tools. They can build systems that scale because they understand architecture, performance, and reliability. They can evaluate AI outputs critically because they know what good code looks like and what edge cases to test for. They can architect hybrid human-AI workflows because they understand both the capabilities and limitations of current AI systems.
Let’s tackle two of the most common objections to our position that AI, in the long term, will drive more demand for developers.
“But won’t AI eventually write all the code?”
AI writes code, for sure. Humans define problems, set direction, and ensure quality. Humans understand what to build, why it matters, how it fits into existing systems, and whether it actually solves the right problem.
AI can generate implementations, but it can’t tell you whether you’re building the right thing. It can’t navigate competing stakeholder priorities. It can’t make architectural decisions that balance technical debt against time-to-market. It can’t evaluate whether the generated code meets your organization’s security, performance, and maintainability standards.
“How will junior developers learn if AI does the basic work?”
This concern isn’t totally unfounded, but it misunderstands how AI changes the learning curve. AI can actually level the playing field in big ways. A junior developer with AI can contribute meaningful code faster than previous generations could. They’re not stuck on syntax errors for hours. They can iterate rapidly and get feedback in real time. They can see working examples instantly.
Of course, junior devs still need to learn the fundamentals to be effective. They still need to understand architecture to evaluate whether AI-generated code is well-designed and fit for purpose. They still need to know testing principles to validate that the code works correctly, and they still need to grasp security principles to catch vulnerabilities.
Mentorship is evolving from teaching syntax to teaching judgment, and that’s generally a good thing. Junior developers learn faster when they can focus on understanding why certain approaches work rather than memorizing how to implement them.
Look, our point isn’t that everything will be fine if you just keep doing what you’ve always done. The world is changing, and developers need to change with it. But the change isn’t from being employed one day to obsolete the next. It’s a shift in how and on what scale we solve problems.
We’re at a beginning, not an end, of software development. As our CEO put it, “There’s literally an infinite number of things to build.” That’s what we’re excited about: The scale and ambition of what we can build is soaring. Barriers to entry have fallen; imagination has become reality. Developers can meet the moment just as they met historical platform shifts, from the internet to cloud computing and the rise of SaaS to mobile-first development.
There’s so much more to build. Let’s get to work.
