No-code and “vibe coding” tools accelerate rapid MVP testing but reach limits as product complexity grows. Developers are required for backend logic, data flows, design systems, and UX decisions that make products reliable, secure, and scalable. Large language models and AI tooling have improved dramatically, automating repetitive tasks like snippets, boilerplate, and frontend scaffolding, which boosts developer efficiency and value. The developer role is shifting toward integrating and overseeing AI capabilities rather than disappearing. Developers who embrace AI tooling will outcompete those who resist, driving evolution in how software is built and maintained.
You still need someone who knows how things actually work - backend logic, data flows, design systems, UX decisions. The stuff that makes a product good, not just functional. That's where developers come in - and not just any developers, but those who know how to work AI, not fear it. This isn't the end of developers. It's a shift in how they work. It's well documented that LLMs such as Anthropic's Claude, Google's Gemini, and OpenAI's ChatGPT have been drastically improving.
As a result of this, the quality of AI tooling has skyrocketed, allowing developers to work with superhuman efficiency, thus becoming more valuable. This moment in tech isn't about replacing developers; it's about how they evolve. AI won't replace developers. But developers who use AI will replace the ones who don't Just to be clear, AI is amazing at taking care of the repetitive stuff - generating code snippets, filling in boilerplate, even giving you a head start on a frontend.
Collection
[
|
...
]