The short answer? No, and it won't be replacing developers anytime soon.
Despite the buzz and headlines predicting the end of programming careers, the reality on the ground tells a different story. After countless conversations with developers across different specialties, one pattern emerges consistently: the more expertise you have in a particular domain, the more apparent AI's limitations become.
While AI has proven remarkably capable at handling straightforward tasks - writing small functions, translating code between languages, reformatting data, and automating repetitive processes - it consistently struggles when faced with complex, large-scale development challenges. Whether you're working in Python, JavaScript, Java, or any other language, AI tools hit the same wall when projects grow beyond simple, isolated tasks.
Let’s look at the following examples:
Example #1: Sylius/Symfony Loco integration
The developer’s task was to integrate translations from Sylius/Symfony with Loco (localize.biz)
The project involved integrating translations using PHP and bash within the Symfony and Sylius 2.0 framework. While the developer’s custom translation file contained only 220 keys, the complexity arose because Symfony's translation command gathered translations from multiple sources: my custom file, Sylius frontend, Sylius admin, and various installed bundles and plugins, creating a much larger dataset to manage.
Upside: AI helped with the basic setup
AI performed well on the straightforward initial tasks:
- Installing the required Loco packages in Symfony
- Creating basic shell commands to send and pull translations
Notably, this was all well-documented in Loco's official documentation.
Downside: AI completely failed on the complex problem
The real challenge emerged when the system worked fine for small translation files (300 keys) but failed with larger files due to server timeouts and Loco's API limitations. The solution required building a custom Symfony command that would:
- Gather all translations like Loco's translate bundle (approximately 2,200 keys total)
- Split translations into manageable chunks and save them to disk
- Create separate commands to send each chunk in individual processes
- Build a command to pull back translated messages
The developer tested four leading AI models. Only two came close to understanding what needed to happen. After extensive conversations and countless adjustments, none could properly gather all translations. One model managed to extract 1,500 keys, but that still wasn't everything. At every single step, there were issues:
- The 1,500 keys that were extracted weren't saved correctly to the local server
- Even when the developer simplified the task to just "extract all messages," AI failed
- Each attempt introduced new bugs and complications
<div class="rtb-text-box is-blue-50">Conclusion: A backend developer with deep Symfony knowledge was ultimately needed to solve this. What AI provided was barely a starting point that required substantial corrections. For complex tasks like this, using AI might actually cost the same amount of time as building the solution from scratch, except with more frustration along the way.</div>
Example #2: Twig - passing variables from a separate file included in another file
The developer wanted to include a menu configuration (menu position labels, icons, and routes) placed in one file and use it in two different places within my Twig templates.
This project used Twig/HTML within the Symfony framework, though this limitation applies to virtually all basic Twig integrations except heavily modified CMSes like Craft CMS. Going into this, the developer was pretty confident this wasn't possible in Twig, but decided to try anyway - maybe there was something they didn't know about.
Upside: AI was confident and convincing
The developer consulted three major language models, and all claimed it could be done. For about an hour, they provided solutions that sounded completely plausible and well-reasoned. Their explanations were detailed and seemed technically sound.
Downside: AI was completely wrong about fundamental Twig limitations
None of the solutions worked. As the developer updated each AI about the problems he encountered, they kept insisting it was possible and offered increasingly complex workarounds. Every single approach failed.
A quick Stack Overflow search confirmed original instinct: it simply can't be done. Users there explained with clear examples how imported Twig files are converted into private PHP functions, making it impossible to pass variables back to the parent template. When the developer examined the compiled cache files on the server, this was exactly what he found - private PHP classes with no way to share variables upward.
<div class="rtb-text-box is-blue-50">Conclusion: AI is particularly weak with Twig because it fundamentally misunderstands how Twig compiles to PHP. Since Twig functions are based on PHP at a deeper level, AI often assumes PHP-based solutions will work in Twig. They don't work, they can't work, and they won't work, but AI doesn't seem to grasp this core limitation.</div>
https://bitbag.io/blog/best-ai-ecommerce-tools
Wrapping up
As these examples demonstrate, AI excels at handling the small stuff, like writing basic methods and eliminating repetitive typing, but consistently falls short when navigating larger frameworks or solving more complex integration challenges.
There's a real risk that over-relying on AI makes developers intellectually lazy, causing them to stop thinking through problems and miss out on learning experiences that build genuine expertise. Even when AI provides a useful starting point, it still requires a skilled developer to validate, correct, and expand the suggested solution. AI turned out to be a decent assistant for simple tasks, but it's nowhere near ready to replace the deep understanding and problem-solving skills that experienced developers bring to complex projects.
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