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Is It Still Worth Studying Software Engineering in 2025? AI’s Role in Shaping the Future

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Is It Still Worth Studying Software Engineering in 2025?

With AI tools like ChatGPT and MidJourney advancing rapidly, there’s a growing concern: Will software engineers become obsolete? If you're pursuing a degree in computer science or working in the tech industry, you’ve probably asked yourself this question.

But here’s the good news: AI is a tool, not a threat. Let’s dive deeper into how AI is changing the software engineering landscape—and why it’s still worth pursuing this career path.

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The AI Hype: Should We Panic?

First, let’s clear the air: this is not a panic post.

Yes, AI is transforming the tech industry, but it’s not here to replace software engineers. It’s here to augment how we work. Tools like ChatGPT automate repetitive tasks, but they can’t replace the creativity, strategic thinking, or problem-solving that define great engineers.

Take prompt engineering as an example. A few years ago, no one could’ve predicted its rise as an essential skill. Now, it’s becoming crucial for anyone leveraging tools like ChatGPT or MidJourney. The lesson? The field is evolving, and so should you.

A Typical Day in the Life of an Software Engineer

To understand the impact of AI, let’s analyze how software engineers spend their day:

Time Breakdown (on average):

  • Coding: 35–40%
  • Debugging/Fixing Issues: 25–30%
  • Meetings and Collaboration: 15–20%
  • System Design/Planning: 10–15%
  • Code Reviews: 10%

That means only about 40% of your day involves writing code. AI tools might automate parts of this, but the rest—like designing systems, collaborating with teams, and solving complex problems—still relies on human expertise.

A Typical Day in the Life of an AI Engineer

Now, let’s dive into what an AI engineer’s day looks like. Interestingly, many companies are now hiring AI engineers instead of traditional software engineers.

Contrary to popular belief, being an AI engineer isn’t just coding or training models. It’s a multi-step process involving everything from data handling to deploying AI systems. Here’s a breakdown:

Data Preprocessing & Labeling (30%)

AI engineers spend a big chunk of their day working on datasets. This includes cleaning, organizing, and preparing data to train models. They write scripts to remove duplicates, normalize data, and handle missing values. For labeled datasets—like image recognition—they often use annotation tools or manage labeling teams to ensure accuracy.

Training Models & Experimentation (25%)

Experimenting with and fine-tuning models is a key task. Engineers test different architectures, like neural networks or transformers, and adjust hyperparameters to optimize performance. This involves analyzing results and iterating until they get the best outcome.

System Design & Integration (15%)

AI engineers also integrate models into real-world applications. They build scalable pipelines for data processing and optimize performance for production environments. For example, deploying a chatbot or recommendation system for millions of users requires robust system design.

Post-Processing & Monitoring (15%)

After models produce outputs, engineers refine raw predictions, set accuracy thresholds, and filter results for usability. Monitoring models in production is crucial—they check for performance issues and retrain models as needed.

Collaboration with Experts & Teams (10%)

AI engineers frequently collaborate with domain experts—like those in healthcare or finance—to align technical solutions with business needs. This often includes translating complex AI concepts into actionable insights for stakeholders.

What AI Can and Can’t Do

Here’s a quick breakdown of what AI tools like ChatGPT can handle versus what remains out of reach:

What AI Can Do:

  • Generate boilerplate code and templates.
  • Debug common coding issues.
  • Automate repetitive tasks like writing tests or deployment scripts.

What AI Can’t Do (Yet):

  • Understand business context and align technical decisions with strategic goals.
  • Design complex, scalable systems.
  • Manage teams or handle cross-department collaboration.
  • Innovate or create solutions outside the scope of historical data.

AI enhances productivity, but it doesn’t replace the need for creative problem-solving and contextual understanding.

The Bigger Picture: AI Is a Tool, Not a Threat

History has shown us that new tools create new opportunities.

When calculators were introduced, people feared that accountants would lose their jobs. Instead, accountants adapted, focusing on higher-level tasks. The same applies to software engineering: AI will automate some aspects, freeing you up to work on:

  • Solving real-world problems that require creativity and innovation.
  • Designing scalable systems for emerging technologies.
  • Exploring new industries like finance, healthcare, or entertainment that are adopting AI.

Resources & Further Reading

To support the discussion on AI and software engineering, here are credible sources and studies:

1. Supporting Data for Statistics

2. AI’s Role in Software Engineering

3. Counterarguments & Nuance

4. Upskilling & Adaptation

5. Ethical Considerations

Final Thoughts: Should You Study Software Engineering?

Absolutely. The key to thriving in this field is adaptability. Stay curious, keep learning, and embrace AI as a tool to enhance your work.

Want to see how AI can help you start coding your first Python game from scratch? Subscribe to my channel and turn on notifications to catch the next video.

What’s your take? Are you excited or nervous about AI’s impact on software engineering? Let me know in the comments below—I’d love to hear your thoughts!

Peace out!

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