Low-code application platforms have spent the past decade solving a fundamental problem: the shortage of skilled developers and the growing backlog of business applications that need building. By abstracting complexity through visual interfaces, pre-built components, and drag-and-drop functionality, LCAPs empowered business users and IT professionals to deliver solutions faster.
The promise was compelling: democratise application development, accelerate digital transformation, and free professional developers to focus on more complex challenges. Yet as generative AI has matured with extraordinary speed, a provocative question has emerged: if AI can generate code directly from natural language prompts, what purpose does low-code serve?
The technology that was meant to bridge the gap between business requirements and technical implementation may find itself bypassed entirely by AI that can write production-quality code on demand. This is not merely theoretical speculation. CTOs and CIOs face increased pressure to justify AI investments, whilst the global market for artificial intelligence code tools is projected to reach $25.7 billion by 2030, growing faster than many low-code segments. The relationship between AI and LCAP is unfolding in three distinct phases, each with profound implications for how organisations approach application development.
AI as the accelerant
In the immediate term, AI is enhancing rather than replacing low-code platforms. Major LCAP vendors have integrated generative AI capabilities throughout their technology stacks, using AI to improve the low-code experience rather than compete with it. AI-powered features include intelligent component recommendations, automated testing, debugging assistance, and natural language interfaces for configuring workflows. For developers working within low-code environments, these capabilities reduce friction, accelerate development cycles, and lower the skill threshold even further.
This symbiotic relationship plays to the strengths of both technologies. Low-code platforms provide governance, security frameworks, integration capabilities, and enterprise-grade deployment infrastructure. AI augments these foundations with intelligent automation, making the platforms more powerful and accessible. A citizen developer can now describe a workflow in natural language and have AI generate the appropriate visual configuration, whilst still benefiting from the LCAP’s built-in compliance controls and scalability features. For organisations with existing low-code investments, this phase represents a validation of their platform strategy.
The erosion begins
The second phase is already manifesting, and it fundamentally challenges the rationale for low-code platforms. As AI code generation tools become more sophisticated, they increasingly eliminate the need for visual abstraction layers. If a business analyst can describe requirements in natural language and receive functional, well-architected code in response, why introduce an intermediary platform at all? The very problem that low-code solved, the difficulty of translating business requirements into technical implementation, is being addressed more directly by AI.
Generative AI offers unprecedented flexibility, addressing many pitfalls of traditional low-code platforms. Low-code platforms, by their nature, impose constraints. They excel at common use cases but struggle with requirements that fall outside their pre-built component libraries, with customisation limitations being a well-documented challenge. AI code generation can theoretically handle both standard and novel requirements with equal facility, generating bespoke solutions without architectural constraints.
The economic implications are stark. Low-code platforms represent significant financial commitments, including licensing costs, training investments, and the opportunity cost of building applications in proprietary environments with limited portability. If AI can generate standard code in widely-used languages and frameworks, organisations gain flexibility, avoid vendor lock-in, and leverage existing developer skills rather than platform-specific expertise.
Moreover, the talent dynamics change fundamentally. If AI can translate natural language requirements directly into code, the intermediate skill set that low-code required becomes unnecessary.
When AI makes abstraction obsolete
The third phase poses the critical question: when AI becomes sufficiently capable of generating, testing, deploying, and maintaining applications end-to-end, does LCAP survive as a product category? The logic is difficult to escape: low-code platforms exist to simplify coding by providing visual abstractions and pre-built components. If AI eliminates coding difficulty altogether by generating code on demand, the abstraction layer becomes redundant overhead.
This is not a distant hypothetical. According to Stack Overflow’s 2024 Developer Survey, 63% of professional developers currently use AI in their development process, with another 14% planning to adopt it soon. Research indicates that AI now generates 41% of all code, with 256 billion lines written in 2024 alone.
As these tools improve in architectural sophistication, testing rigour, and security awareness, the gap between AI-generated code and low-code platform outputs narrows and may eventually invert. At that inflection point, organisations will question why they are paying platform licensing fees and accepting architectural constraints when AI can generate more flexible, maintainable, and portable solutions.
The counter-argument from LCAP vendors centres on governance, security, and enterprise integration capabilities that extend beyond pure code generation. These are valid considerations, but they represent a significant narrowing of low-code’s value proposition. If platforms survive, it will likely be by pivoting from “low-code application platforms” to “enterprise application governance platforms” that happen to support multiple development modalities, including AI-generated code.
Navigating the transition
For CTOs and technology strategists, this evolution demands careful navigation. In the near term, low-code platforms enhanced with AI capabilities offer genuine productivity gains for specific use cases, particularly internal tools and applications with well-defined requirements.
However, the window for low-code platforms as a distinct category may be measured in years rather than decades. The technology that seemed poised for dominant growth is facing an existential challenge from AI that can accomplish the same objectives more directly, more flexibly, and potentially at lower total cost.
The strategic question has shifted from “how do we scale our low-code capabilities?” to “how do we prepare for a world where AI generates most code directly?” The organisations that answer that question thoughtfully will navigate the transition successfully. Those that treat low-code as a permanent solution may find themselves defending increasingly obsolete architectural decisions in an AI-defined future.
