Generative AI and the Code Paradox: When Automation Empowers Traditional Engineering
- Edson Pacheco
- Jun 12
- 3 min read
There is a subtle—and perhaps unavoidable—ironicity in the current state of technological innovation. Over the past decade, there has been much talk about the future of development being in visual platforms: low-code, no-code, drag-and-drop. In fact, these solutions have democratized part of the digital creation process and offered real gains in certain contexts.
But then generative AI appears, not to eliminate code, as many imagined, but to reinforce its leading role .
Little by little, tools like GitHub Copilot, CodeWhisperer, and the like are making software engineers more productive, more autonomous, and less constrained by repetitive tasks . And in this movement, traditional code — which many thought was in decline — is repositioning itself as a strategic choice, not just a technical one.
The return of engineering as a lever for differentiation
The promise of low-code/no-code has always been clear: to deliver solutions faster, with less reliance on technical teams. In many companies, it has worked — especially in operational areas or for prototyping.
But what we have seen in the last two years is a change of axis.
With generative AI, the marginal cost of programming has fallen—both in terms of effort and time. The programmer no longer needs to write each line by hand; he or she can now orchestrate blocks, review suggestions, and adapt patterns. An engineer with Copilot, for example, can save 30% of time on tasks such as creating functions, unit testing, or documentation (GitHub, 2023).
This leap in productivity, in practice, places traditional code on another competitive level . It stops being “slow and expensive” and becomes fast and tailored — without the limits imposed by closed platforms.
Abstraction that costs dearly in the long run
The problem with visual solutions arises when they scale. What starts out as efficient automation often becomes a maintenance bottleneck, with fragmented business logic and little documentation. The phenomenon of “islands of automation” is increasingly common in companies that have adopted low-code without governance — applications that solve a local problem but create a global risk.
Furthermore, sophisticated customizations are difficult to implement on generalist platforms. Code, on the other hand, allows almost total freedom — as long as it is well designed.
This doesn’t mean we should abandon no-code or low-code. They have their place. But perhaps the natural destiny of robust digital products is to eventually return to their engineering base—now powered by AI .
A less technical choice than it seems
Choosing to develop with traditional code today is no longer a matter of technical prowess. It is a business decision. Companies that control their technology base are able to evolve faster, avoid dependencies and explore new models with greater freedom.
It’s telling that giants like Google, Amazon and Microsoft are investing in AI for developers — not to replace them. This tells us something: engineering is still the differentiator, just augmented by intelligence .
Conclusion
While visual platforms will continue to be useful as an access and automation tool, the most strategic move that mature companies tend to make is another: rescuing engineering, but with new instruments .
It’s possible that the future of software isn’t “no code,” but rather more code than ever before—written faster, smarter, and more strategically .
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