Will LLMs Render Low-Code/No-Code Initiatives Obsolete?

The shadow of LLMs looms over every low-code/no-code initiative. We are witnessing ongoing debates about the future of software development paradigms, particularly the viability of low-code/no-code (LCNC) platforms. Major issues like:

• Lack of built-in security controls
• Serious security-related challenges due to the lack of cybersecurity expertise of non-technical users
• Scalability and customisation issues 
• Platform dependency
• Technical debt caused by the accumulation of suboptimal code

can definitely act as a deterrence. A 2024 MITRE Corporation study, for instance, found that 42% of enterprise LCNC projects encounter scalability challenges when integrating with legacy systems.

On the other hand, the revolution of LLMs is advancing at unprecedented speed in every segment. 

• GPT-4 Turbo achieves 89% accuracy in converting business requirements to functional Python code for common CRUD applications.
• Fine-tuned models document MUMPS and IBM Assembly code with 76% accuracy compared to human experts.
• Systems like Claude’s Artifact implement iterative debugging, resolving 68% of syntax errors without human intervention.

If you haven’t so far, we strongly suggest that you watch Eric Schmidt’s take on the AI revolution.

It’s no wonder then that some argue LLMs will render LCNC obsolete. However, some evidence suggests that a more nuanced relationship is possible. This report synthesises findings from academic research, industry predictions and developer communities to analyse the trajectory of the potential transformation and its impact on existing LCNC providers. 

The Current State of Low-Code/No-Code Ecosystems

A transformational capability of low-code/no-code platforms is that they have made it possible for non-technical users to build applications, shifting software development beyond traditional coding. Gartner, for example, predicts that 70% of new enterprise applications will leverage these tools by the end of 2025. 

This large-scale adoption is mostly driven by the demand for rapid prototyping and addressing developer shortages. Major platforms like Appian and OutSystems now incorporate AI-assisted features, with drag-and-drop interfaces. This implementation is reducing traditional coding requirements by 60-80% for common business workflows.

However, limitations persist. Proprietary architectures in tools like Bubble.io create vendor lock-in, while complex logic implementation often requires JavaScript extensions. 

LLMs’ Capabilities in Reshaping Development Workflows

Modern LLMs demonstrate unprecedented code-generation abilities, handling tasks from address normalisation (reducing 50 lines of heuristic code to a single API call) to legacy system documentation. 

These capabilities enable new development paradigms. For instance, at Twilio, AI-assisted coding reduced prototype creation time for customer service applications from two weeks to three days. However, LLMs still struggle with system-level architecture. A 2024 Stanford study found that only 32% of generated web applications passed full integration testing without human intervention. 

What Makes LCNC Solutions Persist Then?

There are three major reasons:

1. Visual Abstraction Layer

LCNC platforms provide constrained environments that prevent runtime errors through visual workflows. For example, Appian’s process modeller reduces logic errors by 54% compared to manual coding. This is particularly important for compliance-heavy industries like healthcare and finance.

2. Governance and Collaboration

Enterprise LCNC solutions offer granular permission controls and version tracking missing in raw LLM outputs. ServiceNow’s AI Builder shows a 92% adoption rate for audit-compliant workflow modifications versus 37% for direct LLM implementations.

3. Integration Ecosystems

Platforms like Zapier maintain prebuilt connectors to 5,000+ APIs versus GPT-4’s ability to generate custom integrations requiring security reviews. For common SaaS workflows, LCNC reduces integration time from 40 hours to under 2 hours.

Factors That Prevent Obsolescence of LCNC Solutions

While LLMs disrupt generic LCNC tools, at least three factors prevent full replacement:

1. Cognitive load reduction
2. Compliance requirements
3. Error surface management

Visual interfaces remain superior for spatial reasoning tasks because clearly defined areas of controls with obvious visual boundaries are key for users to build spatial memory. Since LCNCs allow users to develop apps in a closely similar matter to drafting a flowchart, that means users can rely on spatial memory to navigate complex processes or information structures, reducing the cognitive effort required to understand and interact with the drag-and-drop element. 

Furthermore, several sources indicate that LCNC solutions generally present fewer challenges for complying with data protection regulations like GDPR and CCPA than LLMs.

Finally, in some instances, LCNC’s constrained environments are noted to produce 78% fewer runtime exceptions than open-ended LLM outputs. 

When you take a step back and look at the bigger picture, you can clearly see convergence potential. Let’s see if that is a viable scenario and what has to happen for it to materialise.

Coexistence Through Integration and Mutual Evolution

Businesses should select LCNC solutions with built-in LLM integration or a roadmap for future adoption. Additionally, business users (ie, non-technical staff) should be trained in prompt engineering to maximize the potential of AI-enhanced tools. 

However, businesses must modernise their governance frameworks to ensure quality and compliance. This can be done through the implementation of AI review boards that assess the accuracy, security and ethical implications of an AI-generated code.

When it comes to professionals, they should not only be proficient in LCNC platforms but also understand how to fine-tune LLMs for industry-specific applications. In addition, developers should consider:

• Mastering system architecture design to be able to design scalable and efficient integration patterns. 
• Learning how to validate and refine LLM outputs to ensure reliable high-performing AI-driven applications.

Assuming that all of the aforementioned is realised, we can project the possible convergence path. 

The Estimated Convergence Outlook (2025-2030)

Phase 1: Augmented Development (2025-2027)

LCNC platforms should integrate LLMs as first-class components, namely:

• Visual builders that accept natural language prompts to generate custom UI elements and therefore enable AI-assisted component generation.
• Smart debugging through real-time error correction within workflow designers.
• Automatic code export options alongside platform-specific runtimes.

Platforms like Appsmith, Mendix, Appian and Microsoft Power Platform have already incorporated advanced AI features such as AI-assisted development, intelligent automation and built-in LLM integrations. Users now have access to AI-powered code suggestions, automated testing and predictive analytics – functionalities that significantly accelerate the development process. However, it is still a far cry from what’s not only possible but expected. 

Phase 2 (Possible Future Direction): Specialised Toolchains (2028-2030)

In our view — and mind you, this is only an extrapolation based on current trends and general industry knowledge — we can expect the emergence of domain-specific platforms such as:

• HIPAA-compliant LCNC environments with embedded LLMs trained on FHIR standards in the healthcare industry.
• IIoT-focused tools that combine visual PLC programming with AI-generated optimisation code in manufacturing.
• Audit-trailed AI builders for regulatory-approved algorithm development in fintech.

So, rather than serving as generic automation tools, these next-generation platforms should and could act as intelligent co-developers, embedding regulatory, operational and domain expertise into their core functionalities.

Conclusion

So the capital questions are:

1. Will the rapid development of Large Language Models mark the demise of the Low-Code/No-Code industry?
2. Who will — and how — survive the onslaught?
3. Is there an unforeseen threat to already established LCNC providers?

The one thing that LLMs don’t possess is visual builders or interfaces and dashboards suitable for software development and deployment (eg, drag & drop functionalities). It is on the top of the list for the so-called, citizen developers or utilisation by non-technical users. So the answer to the first question is most likely, no. Convergence is the more likely scenario.

However, that doesn’t mean that LCNC SMBs should sleep easy now. Their problem stems from initiatives of industrial behemoths like Microsoft. Its Power Platform which already converges LCNC and LLM engines is the real threat. Even companies like Lucidworks, Pegasystems and Flowise that have already launched similar solutions have low odds of success in competition against Microsoft. The only way they’ll survive is either through a merger or going niche while enabling commercially available tiers for start-ups. It’s highly unlikely that Microsoft will scale below enterprise-level use. 

What about pure LCNC providers such as Bubble, Glide or Appy Pie?

The window for successful transformation is narrowing. The odds are that LCNC companies that fail to become LLM-convergent by 2027 face a high probability of acquisition or obsolescence by 2030. 

The future belongs to platforms that can hybridise LCNC’s structural governance with LLM’s cognitive fluidity, creating what we might as well term “Liquid Development Architectures.”