In the ever-evolving tech landscape, both low-code and code-first development are reshaping how we approach software creation. Low-code platforms have made it possible for non-developers to create their own applications, which is pretty amazing when you think about it. But as I've been looking at the bigger picture, particularly for larger projects, I keep asking myself: is low-code really the way forward for complex, large-scale applications? Or is code-first development—now with the help of AI—going to take over? Honestly, I'm leaning towards code-first playing a bigger role as projects grow and get more complicated.

Why Low-Code Became the Shiny New Thing (and Why It Sometimes Fails)

Let's start with low-code. These platforms have shaken up the way we approach development. They provide simple visual tools so that pretty much anyone, even those without a tech background (yes, even your accountant Steve), can start building basic applications. For businesses, that was a big deal. They could build apps quickly and easily, bypassing those all-too-familiar bottlenecks that come with waiting on developer resources. So, low-code is great for spinning up prototypes, simple automations, or basic tools in a flash.

However, it's not all sunshine and rainbows. When you start looking at large, complex projects—think applications with thousands of users or loads of data—low-code can hit some roadblocks:

• Scalability & Performance: Bigger applications need to handle growing user demands, without taking a nosedive in speed. While low-code platforms promise scalability, they're often relying on pre-set performance features that aren't built to scale in the same way a hand-crafted solution would be.
• Customisation & Flexibility: Larger projects usually need very specific functionalities. Low-code tools might be speedy, but they often box you into a corner with their limited set of features. Imagine trying to add a third floor to a prefab house—it's not impossible, but it's certainly not easy.
• Security & Control Over Codebase: When working on bigger projects, security becomes a top priority, and developers need direct control over the codebase to make sure everything stays safe. Low-code platforms can make this difficult since they limit how deeply you can tweak or secure the code.
• Vendor Lock-In: Once you're deeply invested in a low-code platform, it can be tough to change course. Imagine spending months on a project, only to realise switching to another platform or approach would mean starting almost from scratch—ouch.

Code-First Development with AI

1. Scalability & Performance That Doesn't Break a Sweat

Big projects demand high performance and the ability to scale. AI-enhanced code-first development allows developers to write optimised, efficient code that's built to handle heavy loads. Developers can directly tweak parts of the code that are critical for performance, which makes a huge difference when things need to scale up smoothly.

AI tools also help spot performance issues and suggest optimisations—kind of like a co-pilot for your code. You end up with an application that can grow alongside your project, without getting stuck in the constraints of a low-code platform's framework.

2. Flexibility That Lets You Be Creative

The bigger the project, the more creative and tailored you need to be with the functionality. Code-first environments give you full control—every line of code is yours to craft, optimise, and make uniquely yours. With AI lending a hand, generating boilerplate code, handling repetitive tasks, and suggesting useful patterns, you have more freedom to build what you need without being limited by a platform's drag-and-drop components.

For complex integrations, advanced workflows, and unique business logic, this flexibility is a huge win.

3. Closing the Speed Gap with AI Assistance

Many people assume code-first means slower development. Fair enough—writing code takes time. But with AI, this gap is narrowing. AI tools help by suggesting code, autocompleting lines, and even nudging you towards best practices. So you get the speed boost while maintaining full control over the entire codebase.

4. Security, Control, and Fewer Dependencies

Large projects come with big responsibilities, and security is one of them. With AI-enhanced code-first development, you have total oversight over your code, which makes it easier to handle security requirements and data privacy concerns.

Low-code can offer security features, sure, but the downside is that there are often more dependencies—UI design components, platform limitations, and pre-built workflows all add layers that can be tricky to secure effectively. With code-first, you own it all.

What About Low-Code and AI?

Don't get me wrong; low-code isn't sitting idle on the sidelines. Many platforms are starting to integrate AI to enhance what they can do—whether it's helping with logic, data analysis, or UI design. But here's the catch: low-code's AI capabilities often rely on platform-specific features, visual editors, and drag-and-drop functionalities. This dependency means they can't achieve the same depth of integration or customisation as code-first, which can tap directly into AI libraries, custom scripts, and more.

So, while low-code is starting to incorporate AI, it's not quite the seamless, adaptable experience you get with a code-first approach.

The Face-Off: Code-First with AI vs. Low-Code for Large Projects

For large-scale projects that demand flexibility, scalability, and control, AI-enhanced code-first clearly comes out ahead.

The AI Caveats: Why You Still Need a Human Touch

AI has come a long way, but it's not a magic wand. There are some areas where human oversight is still crucial:

• AI Suggestion Accuracy: AI-generated code is great, but it's not always perfect. Developers need to check that code suggestions fit project requirements and adhere to best practices—because let's face it, no one wants AI-generated bugs.
• Cost and Training: AI tools often require licensing fees and training to implement effectively. While these are typically good investments, they need to be weighed against the long-term benefits.

A Balanced Approach: Code-First Where It Matters, Low-Code for Simplicity

While AI-enhanced code-first development shines for large-scale applications, low-code still has a role to play. Here's how to balance the two:

• Use Low-Code for Prototypes & Simple Tools: Low-code is brilliant for quick-turnaround prototypes, proofs of concept, or smaller applications that don't need deep customisation.

• AI-Enhanced Code-First for Core, Complex Projects: For core systems that need to scale, require complex logic, or need tight security, code-first is the way to go. The flexibility and long-term control are well worth it.

• Mix & Match Skills: Teams can benefit from using both approaches. "Citizen developers" can iterate on low-code components, while traditional developers use code-first to build out the complex features.

Prediction: Code-First is Poised to Take the Lead

As software development evolves, I see code-first—boosted by AI—becoming the go-to choice for large, complex projects. Sure, low-code will always have its place, especially for smaller tools or rapid prototypes. But for projects that demand deep customisation, scalability, and security, code-first is the clear front-runner.

The trick is finding the right balance between the two. But one thing's certain: as projects grow more complex, AI-enhanced code-first development is set to take the lead where it matters most.