Will Software Engineers Or Citizen Developers Benefit More from Large Language Models?
AI Enhances Productivity for Engineers
The emergence of Large Language Models (LLMs) is reshaping the software development landscape, benefiting both software engineers and citizen developers, albeit in distinct ways. Software engineers are experiencing a paradigm shift where AI acts as an accelerant, automating routine coding tasks, debugging, and even architectural recommendations. This augmentation enhances productivity, allowing engineers to focus on higher-level problem-solving, system design, and optimization rather than getting bogged down by syntax-heavy implementation. However, while senior engineers benefit from AI-assisted development, junior developers may face a steeper challenge in proving their value, as many entry-level coding tasks are now automated by AI tools.
Conversely, LLMs significantly lower the technical barrier for citizen developers, democratizing software creation through natural language programming and low-code/no-code platforms. Business professionals, entrepreneurs, and non-technical users can now build applications, automate workflows, and query databases without deep programming knowledge. This shift empowers organizations to reduce IT bottlenecks and accelerate digital transformation, making software development more inclusive. However, while LLMs enhance accessibility, they do not replace the need for technical oversight-complex systems, scalability, and security considerations still require the expertise of software engineers.
Ultimately, the greatest beneficiaries of LLMs will be those who can integrate AI fluency with domain expertise. Software engineers who adapt to AI-driven development will transition into AI-augmented problem solvers, focusing on strategic thinking, system architecture, and AI governance. Meanwhile, citizen developers will leverage LLMs to build solutions without deep coding skills, but their reliance on AI-generated outputs means they will still require engineers to refine, validate, and scale their solutions. This evolving dynamic suggests that AI is not a zero-sum disruptor but rather a force multiplier, enhancing productivity across both technical and non-technical domains while shifting the nature of software development itself.
Mohammad Haqqani, Founder, Seekario
Both Engineers and Citizen Developers Gain
The rise of large language models (LLMs) is creating a fascinating shift in the software development landscape. It’s natural to wonder who will benefit most: seasoned software engineers or the increasingly empowered citizen developers. The truth is, both stand to gain, but in different ways. Engineers can leverage LLMs to automate repetitive tasks, accelerate coding, and even generate initial drafts of complex algorithms. This frees them to focus on higher-level design, architecture, and problem-solving, essentially supercharging their productivity and allowing them to tackle more ambitious projects.
On the other hand, citizen developers, those individuals with domain expertise but perhaps less coding experience, are seeing a new world of possibilities open up. LLMs can help them translate their ideas into functional applications with less reliance on traditional coding. Imagine a marketing manager being able to build a simple data analysis tool by describing what they need in plain language. This democratization of development could unlock a wave of innovation from those closest to the problems, leading to solutions we haven’t even imagined yet. It’s not about replacing engineers, but rather expanding the pool of those who can contribute to the creation of software.
Brandon Batchelor, Head of North American Sales and Strategic Partnerships, ReadyCloud
Citizen Developers Benefit Most
Large Language Models (LLMs) are changing the game for both software engineers and citizen developers, but the biggest winners will be citizen developers. These AI-powered tools lower the barrier to entry, allowing non-technical users to build applications, automate tasks, and generate code without deep programming knowledge. Businesses can now train employees in no-code and low-code platforms, increasing efficiency and reducing dependency on traditional developers for routine tasks.
That said, software engineers will still have the edge when it comes to building, refining, and scaling AI-driven solutions. LLMs can generate code, but they still require human oversight to ensure accuracy, security, and efficiency. While citizen developers gain accessibility, software engineers who leverage AI for code generation, debugging, and automation will become even more valuable, focusing on high-level problem-solving rather than repetitive coding tasks.
Georgi Petrov, CMO, Entrepreneur, and Content Creator, AIG MARKETER