As artificial intelligence (AI) becomes more capable of writing code, fixing bugs and assembling applications, companies are asking: why hire an inexperienced programmer? That question creates a tension for tech firms. They can automate routine tasks, yet they still need people who will learn to tackle the trickier problems that automation can’t fully solve.
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Many junior developers began their careers by implementing simple functions, patching defects and making small changes. Today, AI assistants can produce that kind of work in seconds. That shift doesn’t make programmers obsolete, but it does change the skills new hires must show to land their first roles.
In Mexico, where young professionals are eager to join an industry that is rapidly adopting AI tools, this evolution also raises a training challenge. Emilis Strimaitis, Head of Product Innovation at Hostinger, tells Expansión that his company continues to recruit junior profiles because it needs to cultivate the next generation of specialists.
“Some people think junior developers aren’t needed anymore, but we keep hiring them because in five years they will be seniors,” he says.
According to Strimaitis, the difference lies in how new developers learn: future programmers will use AI from day one, so they must grasp both how to give good instructions to the tools and how to interpret the outputs they receive.
“They will be native to AI and will learn to solve problems with its help,” he adds.
The AI writes code, but developers must verify it
Automation is already reshaping how work is distributed at Hostinger. Strimaitis explains that one of the company’s senior developers now spends more time on complex problem-solving because AI handles routine tasks and everyday bug fixes.
That arrangement benefits people who understand system architecture, but it also alters how newcomers are trained: a junior developer today must learn to recognize when a proposed solution is actually correct and when it could introduce new problems.
Supervising AI also requires technical knowledge
Mantas Lukauskas, Hostinger’s technical lead for AI, told Expansión that programmers need to inspect and approve changes before deploying them, especially when an application handles customer data or integrates with other services. Oversight becomes critical in those contexts.
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He adds that systems driven by AI agents also need access controls to prevent individuals from viewing data they shouldn’t. These responsibilities demand knowledge of programming, security and system integration. A developer can ask AI to generate a function, but they must understand how it operates to spot whether it exposes private information or introduces vulnerabilities. Learning to use an AI coding assistant does not replace technical expertise; if anything, it heightens the importance of being able to evaluate the tool’s outputs.
How should a junior developer adapt their CV so they don’t get left behind?
The change matters for job seekers because, instead of simply listing languages and frameworks, candidates now need to show what problems they can solve and how they use AI without depending on it.
The Stack Overflow Developer Survey 2026 finds that AI tools are already part of daily work: 66% of respondents use AI assistants for programming. Of those who answered questions about specific tasks, 69.3% said they use AI to generate code in areas they already know, and 63.8% use it to fix bugs or modify existing programs. Among developers who use AI to check their work, 76.5% run the code locally before relying on it, and 53% review tests or security implications.
Tools such as Lovable are also broadening who can build software: these platforms let people create applications through natural-language instructions without hand-coding every line. At an event organized by Laboratoria, an organization that promotes women’s participation in tech, Fernando Obregón—an ambassador for Lovable—explained how he used the tool to build the platform for his guided-tour startup. His experience illustrates how AI lowers the barrier to creating an initial version of an app, even for those without deep programming knowledge.
But being able to produce a first draft isn’t the same as delivering a production-ready product. Technical skills are still necessary to find bugs, assess how an application behaves in reality, and protect users’ information.
Martina De Leo, a talent acquisition specialist at Adecco Argentina, told iProfesional that employers now value abilities such as analytical thinking, communication, adaptability and continuous learning. Junior candidates who pair those soft skills with experience using AI tools can improve their hiring prospects.
One practical way to show that capability is to include concrete projects in your résumé. Describe an application you built: explain the problem it solved, how you validated its behavior, and what testing you performed. If you used AI, spell out which tasks you automated, what mistakes the system produced and what checks you ran before releasing the code.
A GitHub portfolio or similar repository is a useful way to demonstrate that process, even if you don’t yet have formal work experience. De Leo also recommends gaining experience through solo projects, short courses and participation in professional communities—activities that show initiative. Communication and teamwork skills round out the technical preparation.
Thus, an effective CV should highlight three things: programming knowledge, projects that demonstrate how that knowledge was applied, and the ability to review and validate AI-generated code. The key isn’t to write “I use ChatGPT” on your résumé, but to show what you achieved with that tool and how you verified the results.
What will happen to senior developers?
The rise of automation also poses a question for companies: if they remove the tasks that previously served as on-the-job training for juniors, how will those newcomers gain the experience they need?
Strimaitis believes future professionals can still acquire those skills while working alongside AI, but he warns that the challenge is no longer just teaching young people how to use the tools. Companies must also ensure juniors develop the judgment required to correct and supervise AI. In short, automation changes which technical abilities are essential, and it raises the bar on the critical thinking needed to keep software reliable and secure.
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Lucas Monroe tracks the latest trends in mobile and 5G innovation. His work spans hardware analysis, telecom breakthroughs, and ecosystem development in next-gen connectivity.