The good news is that massively popular libraries such as React—the most widely used FrontEnd tool in web development—make it possible to incorporate these capabilities, although making the most of them requires a shift in approach.
AI-Powered React: more speed, less friction
With AI assistance, development teams can focus much more on solution architecture. This includes creating components, writing tests, refactoring code, generating forms, interpreting errors, documenting functions, and suggesting performance improvements.
Perhaps AI’s greatest achievement in our field is the immense know-how it has about the library ecosystem. In the past, a developer had to specialize in one or more complementary tools, such as Redux, Material UI, or Jest, with a learning curve similar to studying a new language. Now, AI acts as an interactive expert that we can turn to for explanations and advice, saving us from having to memorize thousands of methods and properties for every new library.
The illusion of perfect code and the role of the senior developer
However, we should not overstate the role of this technology: it is an extremely useful tool, but it is not omniscient. AI does not understand the business context, client constraints, project history, or the long-term consequences of every technical decision. It can generate a solution that appears correct in the short term but proves difficult to maintain, introducing technical debt or inconsistencies with the existing architecture. At the end of the day, there is a huge difference between simply “generating code” and building quality software.
That is why AI should be viewed as an advanced assistant that should not make critical decisions without human oversight. In this new scenario, the role of a senior developer becomes more important than ever, because they know how to evaluate code beyond its technical correctness and determine whether an AI-generated solution follows the project’s patterns, scales properly, is testable, affects performance, or creates future maintenance issues.
For example, a junior developer can ask AI to build a screen, something the assistant will do immediately. But a senior developer will also evaluate whether that screen is well designed, whether the components are properly separated, whether the logic should live in a hook, whether state is being managed correctly, or whether the solution is too tightly coupled.
Guiding AI is also a skill
The key is understanding that working with AI is not simply about asking it to build a particular component—which it will almost certainly do faster—but about having the technical judgment needed to evaluate its response to our request.
To achieve good results, developers need to know how to explain the context, break down the problem, define constraints, request alternatives, review the output, and correct course when the response is not appropriate.
In React, the difference between a good and a bad prompt is substantial. Asking:
“Create a user table.”
Is not the same as providing a precise technical instruction:
“Create a React component with TypeScript to list users, using Material UI, separating the fetching logic into a custom hook, handling loading, error states, and pagination, while keeping the components reusable.”
The quality of the result is directly proportional to the quality of the instruction. The developer’s role evolves: by delegating the mechanical work of writing code, they become the director, validator, and organizer of the work.
The strategic challenge and costs
We are currently at a turning point in the industry. Every month, new AI models emerge with increasingly sophisticated capabilities, including agents. The challenge for organizations is not to chase every new development, but to carefully assess whether these tools actually add value to their processes.
In practice, AI-assisted development is iterative: request, evaluate, adjust, and repeat. It is an environment of constant experimentation in which not all models deliver the same level of response quality. That is why, in addition to performance, it is essential to consider return on investment. A marginal improvement—for example, 1%—does not justify a significant increase in operating costs.
There is also a particular market dynamic at play: some providers launch highly capable models at low prices to encourage adoption and dependency, only to adjust their pricing later with little advance notice, directly impacting projects.
Even during evaluation, some models can detect that they are being tested and optimize their responses accordingly, potentially distorting the results.
In the long term, it is often more efficient to work with a proven model that understands the stack well—for example, tools such as React—and focus efforts on guiding it, supervising it, and refining instructions so that it responds consistently to the project’s needs.
By Julián Haeberli, Software Developer of Baufest.
