The world of tech experts and students is currently going through a period of the greatest uncertainty in the last two decades. According to data from Business Insider and research conducted within the Hoover Institution at Stanford University, enrollments in computer and information science fields decreased by 8.1% in the autumn of 2025 compared to the previous year.

This trend is not just a statistical anomaly. It is a signal that the paradigm we operate in – the relationship between technical education and a future career in software engineering – is fundamentally changing. For Czech students at CTU or BUT, this might raise the question: "Is it still worth investing years in deep algorithmic study when AI can write code in seconds?"

Is AI the culprit? The difference between correlation and causation

Jacob Light, a Stanford economist, analyzed over 50 million courses at 1,019 US universities as part of his extensive research. His conclusion, however, is cautious: even though the emergence of large language models (LLMs) coincides with the decline in interest in computer science, the data does not directly prove that AI is the direct cause.

Light identified five key factors that may be behind this development:

• Weaker job market for junior developers: Companies are still looking for experts, but positions for beginners are changing. AI tools like GitHub Copilot (available in the Czech Republic for approx. 250 CZK/month for individuals) or Cursor increase efficiency demands, reducing space for "learning" junior roles.
• Uncertainty about the value of a degree: If AI can debug code and design architecture, students are increasingly questioning whether a traditional CS (Computer Science) degree is still the best path to success.
• Changes in student demographics: Changes in US immigration policy have affected the number of international students, who previously formed a significant part of technical fields.
• Competition from new fields: Instead of pure computer science, students are choosing more modern directions, such as Data Science or AI Engineering.
• Quality of student preparation: There is a growing gap between the technical preparedness of incoming students and the demands of university studies.

Comparison of game-changing tools

To understand why students are concerned, we need to look at the capabilities of current models. A traditional programmer used to spend hours writing boilerplate code. Today, these tasks are taken over by models with extremely high accuracy:

Practical impact: What does this mean for Czech developers and companies?

This decline in the USA is a warning signal for the European and Czech markets as well. The Czech Republic has a strong tradition of software engineering, but we must realize that the role of "coder" is dying, while the role of "system architect" (engineer) is deepening.

For the Czech technology scene, this means three things:

1. Changing demands for junior roles: Companies in Prague or Brno will no longer be looking for someone who only knows Python or Java syntax. They will be looking for people who understand AI orchestration – the ability to manage models, verify their outputs, and integrate them into complex systems.
2. Importance of regulation (EU AI Act): Within the European Union, safety and ethics will be key. Developers must understand how models work "under the hood" to comply with strict European standards for transparency and control over algorithms.
3. Increased importance of soft-skills: When AI takes over technical work, people must excel at problem definition, client communication, and project management.

If you are a student or an aspiring developer, the biggest mistake would be to stop studying the fundamentals of computer science. Without an understanding of data structures, operating systems, and network security, you will not be able to effectively control AI tools. You will only be a "copier" of code who cannot even fix it when AI makes a mistake.