What Happens to Software Engineers When AI Takes Over?

The demand for software engineers has dropped significantly in the past few years. According to a story published by The Washington Post in 2024, “More than a quarter of all computer programming jobs have vanished in the past two years.” They, like many others, attributed this decline to the rise of artificial intelligence. 

We know everyone has questions, and many have made big promises, but the truth is: there is little certainty. Whilst it is true that AI has made immense progress in the past few years, we don’t really know how it will continue to evolve or the consequences of that evolution. The way we see it, there are two scenarios: 

In the first, AI continues to evolve and improve at the current exponential rate. This would mean that anyone could code in human language. To understand the implications of this, it’s important to realize that the history of coding is all about levels of abstraction. 

Software development started with binary code, zeros, and ones. Years later, it moved into machine code, then assembly, followed by C, C++, and finally Python and JavaScript.  

As coding evolved, languages became easier and more human-like. In this scenario, AI becomes the next level of abstraction.  

However, technical human knowledge will still be needed, although less of it. And these engineers will still need to know how to code, because obviously, project managers won’t be able to handle that. 

(Want to understand Generative AI? Read our blog post: What is Generative AI? A Comprehensive Guide to this Cutting-Edge Technology)

In the second scenario, AI doesn’t improve as much as we thought it would. Driven by the hype, most companies will have shifted to mostly AI-generated code, laid off most of their developers, and kept a few to manage the AI tools. Years later, problems start to surface, vulnerabilities, bugs, and issues caused by underperforming code. Then companies will have to backtrack to 80 percent AI code and bring engineers back to clean it up. 

This year, we’ve seen companies trying to keep up with the turmoil of change, responding with massive layoffs and bold new product offerings. In many ways, they’re placing bets on what they think the future will look like. Take Globant as an example: they recently launched their “AI Pods,” which promise faster coding powered by agentic AI, supervised by humans. Moves like this reflect a broader trend: companies are gambling on which scenario will prevail and reshaping their teams and strategies accordingly. 

Alternatively, Keller’s “AI & Machine-Learning Talent Gap 2025” report highlights a dramatic 61% jump in global AI/ML job postings in 2024, far outpacing other tech roles. Despite this surge, there’s a projected 50% hiring gap, meaning demand for skilled professionals is roughly double the available supply.  

Simply put: good developers are scarce and expensive. 

But that doesn’t mean the best engineers have to come with the highest price tag. Several factors contribute to the cost-efficiency of a software engineer for a U.S. company, and nearshoring is one of the most strategic advantages. 

Take Latin America for example. While companies in countries like Argentina (such as BairesDev or Globant) have traditionally been go-to options, their rising costs—driven by exchange rate shifts and inflation—are making them significantly more expensive than they used to be.  

In contrast, partnering with a Bolivian-based company like Jalasoft offers U.S. companies a far more cost-effective alternative without compromising on talent quality. With a robust in-house training system, Jalasoft delivers top-tier engineers who are not only affordable but also bilingual, culturally aligned, and time zone compatible. 

The Birth of Prompt Engineers 

The role of the human developer - said Thomas Dohmke, chief executive of GitHub, the Microsoft-owned developer site, to The New York Times - becomes to guide and direct the A.I. agents, like “the conductor of an A.I.-empowered orchestra.” 

As AI tools like ChatGPT, GitHub Copilot, and Claude become more advanced, we’re witnessing the emergence of a new role in the tech industry: prompt engineers. These conductors of A.I.-empowered orchestras specialize in crafting clear, strategic prompts that guide large language models (LLMs) to produce useful, accurate, and high-quality outputs.  

It would be a big mistake to assume, however, that their task is just typing instructions into a chatbot. Prompt engineers require a deep understanding of language models, context control, and task framing. In many ways, it’s becoming a new interface between humans and code. Instead of writing syntax-heavy functions, prompt engineers orchestrate complex software behavior using natural language, logic, and experimentation. 

This role still demands technical thinking, logical structure, and domain knowledge. As Arnal Dayaratna, an analyst at IDC, a technology research firm in that same New York Times article, said: “The skills software developers need will change significantly, but A.I. will not eliminate the need for them, not anytime soon anyway.” 

AI Can Write Your Code… Should It? 

At Jalasoft, we’ve been exploring this question not just from a technical standpoint but also from a strategic and ethical perspective.  

As part of an ongoing test within our R&D team, a senior engineer was assigned to refactor a complex piece of code as a test. The task required a full day of work and demanded a deep understanding of system architecture, business logic, and long-term impact.  

With the help of AI, that same refactor could be completed in just hours. Even more striking: a junior engineer using the same AI tool could technically perform the task.  

 But this is where the real risk begins.  

“While the code will compile and pass automated tests, the junior developer won't have the experience to evaluate why the AI solution worked or whether it should have been trusted. In fact, this version could introduce subtle bugs and compromise scalability,” explainsJorge López, Jalasoft’s CEO and founder.  

This brings us right back to our second scenario: AI evolves, but it’s just not good enough. What started as a shortcut can quickly turn into technical debt, higher support costs, system downtime, and potentially millions of dollars in losses.  

“To me, this is the classic double-edged sword: AI can dramatically increase velocity, but in the wrong hands, it can amplify risk at scale,” adds López.  And when that happens, it’s not a technical failure. It’s a strategic failure that exposes the urgent need for leadership, governance, and intentional decision-making.  

Our R&D team is actively experimenting with AI tools—but in a controlled, structured environment. “We’re not blindly integrating AI into every workflow. Instead, we’re partnering closely with our clients to define when AI makes sense and how it should be applied. We’re developing frameworks prioritizing human oversight, quality assurance, and long-term alignment, not speed for speed’s sake,” explains López.  

So, what should young software engineers do? We believe they should prepare. 

Educating the Next Generation of Engineers 

At Jalasoft, we believe the key is preparing engineers to thrive alongside AI, not compete with it. That’s why we proudly support Jala University, a U.S.-based institution dedicated to training the next generation of software engineers with a forward-thinking, AI-integrated approach.  

Jala University’s program goes beyond traditional coding. Students are taught the fundamentals of machine learning, data science, and automation, as well as how to work effectively with modern AI tools. They gain hands-on experience with prompt engineering, contextual thinking, and real-world problem solving. 

What sets Jala University apart is its commitment to graduating students at a mid-level, rather than entry-level. The goal is to produce engineers who are ready to contribute immediately, capable of collaborating with AI systems, and equipped to handle the complexities of modern software development from day one. 

Equally important is the emphasis on human-centered skills: critical thinking, communication, and ethical awareness, which are crucial for leading and managing AI-integrated teams. 

Conclusion: Skeptic but prepared 

What we’re seeing so far is that AI usage tends to lean more toward augmentation than full automation. Developers are increasingly relying on AI to check their work, explain complex concepts, and iterate more efficiently, not to replace them, but to enhance their capabilities.  

This reinforces the idea that engineering isn’t going away; it’s simply evolving. 

We’ve also seen a lot of hype around AI and productivity, with claims of massive gains across the board. But the reality is more nuanced. We’ve seen that productivity depends heavily on the project, the team, and how well AI tools are integrated into existing workflows. In some environments, the gains are real and immediate. In others, they’re slower to materialize and may require cultural and process shifts first. 

At Jalasoft, we’re watching this transformation with both curiosity and caution. We remain skeptical of the hype, but fully prepared for what’s next. By supporting initiatives like Jala University and continuously investing in the development of high-performing, bilingual engineers in Latin America, we’re helping shape a future where human expertise and AI capability go hand in hand.  

Because in a world where change is the only constant, preparation—not prediction—is what sets true technology leaders apart.