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AI is a powerful tool, but it doesn't replace foundational knowledge. To build a production-ready application using AI, you still need to understand the underlying code and architecture. The tool amplifies existing skills rather than creating them from scratch.
There are two competing philosophies in the AI tool space: one aims to automate development entirely, while the other empowers users. Netlify is betting on the latter, building tools that treat the user as a developer, augmenting their abilities to create a massive new wave of builders.
Despite the hype, LinkedIn found that third-party AI tools for coding and design don't work out-of-the-box on their complex, legacy stack. Success requires deep customization, re-architecting internal platforms for AI reasoning, and working in "alpha mode" with vendors to adapt their tools.
AI tools are commoditizing the act of writing code (software development). The durable skill and key differentiator is now software engineering: architecting systems, creating great user experiences, and applying taste. Building something people want to use is the new challenge.
Vercel's Pranati Perry argues that even with no-code AI tools, having some coding knowledge is a superpower. It provides the vocabulary to guide the LLM, give constructive criticism during debugging, and avoid building on a 'house of cards,' leading to better, more stable results.
AI's value is limited by the system it's built on. Simply adding an AI layer to a generic or shallow application yields poor results. True impact comes from integrating AI deeply into an industry-specific platform with well-structured data.
AI coding tools can rapidly build the first 70% of an application, but the final 30%—the complex, unique features that define your vision—will consume the vast majority of your development time. This is a critical reality check for anyone starting with these tools.
When you use AI to generate complex outputs like a website or video, you receive a static, single-layer product. If you don't understand the underlying components (e.g., code, video layers), you can't edit, debug, or evolve the asset, effectively trapping your organization with a 'snapshot in time.'
Instead of fearing AI, design engineers should leverage it to automate boilerplate and foundational code. This frees up mental energy and time to focus on what truly matters: crafting the nuanced, high-quality interactions and animations that differentiate a product and require human creativity.
AI coding tools provide massive acceleration, turning projects that once took weeks or a dev shop into a weekend sprint. However, they are not a one-click solution. These tools still require significant, focused human expertise and effort to guide the process and deliver a final, functional product.
Resist the temptation to treat AI-generated prototype code as production-ready. Its purpose is discovery—validating ideas and user experiences. The code is not built to be scalable, maintainable, or robust. Let your engineering team translate the validated prototype into production-level code.