After months of experimenting with AI tools across different projects, one pattern keeps emerging: the more complex your project gets, the more obvious AI's limitations become. While headlines love to predict the end of programming careers, the reality on the ground tells a completely different story.
AI has proven really handy at handling straightforward, well-documented tasks. However, when projects grow beyond simple, isolated problems - whether you're working in PHP, JavaScript, Symfony, or React - AI consistently hits the same wall. Let's dive into what actually works and what doesn't, based on real development experience.
Where AI actually excels (and it's pretty useful)
After testing AI across various development scenarios, there are definitely areas where it shines and can genuinely speed up your workflow.
- Simple code generation and conversions represent AI's strongest suit. Need a basic method written? Converting code snippets between languages? Creating skeleton structures to build upon? AI handles these tasks well, especially with PHP, JavaScript, TypeScript, and React - languages with extensive documentation and community discussions that AI can draw from.
- Project setup and configuration is another area where AI consistently delivers. Setting up base Symfony projects, configuring TypeScript environments, or handling initial scaffolding can be delegated with confidence. This is probably because these tasks are well-documented and follow predictable patterns.
- Data transformations and natural language tasks align perfectly with what AI was built for. Whether you're parsing data (taking raw data in one format and converting it into a structured format that your application can work with), handling translations, or adding documentation to existing code, AI is great because it's fundamentally good at understanding and manipulating text-based content. The translation options are particularly solid, and AI can help with both code translations between languages and actual content localization.
- Perhaps most valuable is AI's ability to suggest approaches you might not consider. By drawing from its vast knowledge base of programming discussions and documentation, AI can offer fresh perspectives on problem-solving, potentially saving hours of research time. It gathers insights from countless sources and saves you from digging through long forum discussions.
- Refactoring and cleanup tasks also work well - renaming variables to something more meaningful, restructuring code for readability, or standardizing formatting across files. These are the kind of tedious tasks that eat up time but don't require deep understanding of complex business logic.
AI works great as a starting point for more complex issues, but you definitely need an experienced developer to finish the ideas and handle the nuanced implementation details.
Where AI falls flat (and costs you time)
Despite the hype, AI has clear limitations that may become expensive mistakes if you don't recognize them early.
- TWIG templating consistently confuses AI tools - and honestly, all major language models seem to fundamentally misunderstand how TWIG compiles to PHP. They confidently suggest solutions that simply can't work, insisting it's possible even when presented with evidence to the contrary. It just fails across the board.
- Complex framework-dependent problems are another pain point. AI tends to combine elements that won't work together in practice, lacking the nuanced understanding of how different versions, dependencies, and configurations interact in real projects. Something is always broken when you try to implement AI suggestions for complex framework integrations.
- Large-scale project architecture requires the kind of holistic understanding that current AI tools struggle with. When your application involves multiple interconnected classes, scattered dependencies, and intricate business logic, AI gets lost. It's not just a technical limitation - describing the full context of a complex application to an AI becomes unwieldy and time-consuming.
- Version compatibility issues create real headaches. AI sometimes confidently provides solutions combining incompatible library versions or suggests approaches that worked in older versions but have been deprecated. The tendency to "hallucinate" solutions when uncertain means you'll spend time debugging AI-generated code that never had a chance of working.
- Token and input parameter limitations remain a significant challenge. While each newer AI model can understand and "remember" longer conversations and take more variables into account, we're still limited in how much context we can provide. Humans can naturally be aware of much larger sets of information that matter for each topic, making it easier to reach for all the small details and factors required for solving complex problems.
https://bitbag.io/blog/is-ai-going-to-replace-developers
The reality check: what this means for your workflow
The fundamental issue isn't that AI is bad at programming - it's that programming involves much more than writing code. AI excels at the mechanical aspects but struggles with the contextual understanding, architectural decisions, and domain expertise that experienced developers bring to complex projects.
AI is great for taking care of data parsing and natural language related tasks, simply because it's already good at understanding conversations. In these kinds of tasks, it already has all the context it needs and can handle dull tasks that couldn't be easily described to a computer before.
Generating smaller code fragments and single functions is also a great task for AI. Its knowledge of most programming languages is at least good right now. AI built its knowledge around available documentation and many articles you can find on the internet, so it has the skills needed in its main base used for generating answers - just like the knowledge to understand us and be part of a conversation.
It copes worse with programming tasks that involve complex projects, use many libraries, and depend on different classes scattered throughout applications. Here, we often need to understand many different factors and know how to find relevant places throughout the code of entire applications. It's also much harder for us users to describe everything important to the AI.
There's also a real risk that over-relying on AI makes developers intellectually lazy. When you stop thinking through problems and miss out on learning experiences that build genuine expertise, you're essentially trading short-term convenience for long-term skill development.
Best practices we've learned
Based on our experience, the most effective approach treats AI as a sophisticated research assistant and code generator rather than a replacement for developer judgment.
- Use AI for the grunt work: Simple functions, data parsing, basic configurations, repetitive tasks where the pattern is clear and well-established, and setting up project scaffolding.
- Keep humans in charge of the complex stuff: Architectural decisions, framework integrations, debugging complex issues, and anything requiring deep understanding of your specific application's context.
- Always verify AI output: Even when AI provides seemingly perfect solutions, experienced developers need to review, test, and validate the code before it goes into production.
- Start small and scale up: Begin with AI assistance on isolated tasks before considering it for more complex integrations. This helps you understand its capabilities and limitations within your specific tech stack.
https://bitbag.io/blog/how-does-ai-speed-up-web-development
Looking ahead
The technology continues evolving rapidly, with each generation offering improved context handling and reduced hallucination rates. As AI tools gain better access to entire project structures and develop more sophisticated understanding of complex codebases, the line between AI-suitable and human-only tasks will undoubtedly shift.
For now, the key insight is that AI excels at strengthening human capabilities rather than replacing them. It's a powerful tool for handling the mechanical aspects of development, freeing up experienced developers to focus on the architectural decisions, complex problem-solving, and domain expertise that actually determine project success.
The businesses winning with AI aren't the ones trying to replace developers - they're the ones using it to make their existing development teams more efficient while maintaining the quality and reliability their projects demand.
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