Opportunities and Risks of Vibecoding & AI-Assisted Programming

The use of AI in programming is changing not only teaching, but also business, environment and society. This overview serves as a basis for reflection in classroom discussions or for deciding whether to use the systems.

Critical Aspects and Risks

High Energy Consumption & Ecological Consequences

Large AI models require energy-intensive data centers. Training and operation cause significant electricity and water consumption. Expansion of data centers changes local infrastructure.

Source: https://www.nature.com/articles/d41586-024-00478-x

Resource Consumption from Software Use

Even individual AI queries consume significantly more energy than classic search queries. Scaling in the education sector amplifies the effect.

Source: https://www.forbes.com/sites/dianneplummer/2025/08/27/every-ai-prompt-leaves-a-hidden-environmental-price-to-pay/

Economic Uncertainty & Labor Market

Automation can change or displace certain programming and entry-level jobs. Companies invest heavily in AI, creating global competitive pressure. A long-term consequence of Big Tech expansion is also destabilization of white-collar work, which is often associated with the middle class.

Source: https://finance.yahoo.com/news/microsoft-ai-boss-says-ai-134000462.html

AI as Potential Technology Bubble

High investments sometimes face unclear long-term business models. Much is promised, but actual use is often forced rather than desired. Historical parallels to the dot-com bubble are discussed.

Source: https://techreport.com/news/business/the-ai-bubble-debate-can-both-bulls-and-bears-be-right/

Political and Geopolitical Dimension

AI companies are in close exchange with governments. AI is increasingly viewed as a strategic key technology. Big Tech significantly supports the Trump administration.

Source: https://factually.co/fact-checks/business/tech-companies-donations-to-trump-campaign-f6aacc

Training Data & Copyright Issues

Many models were trained with publicly available data without authors always consenting. Ongoing court cases show unresolved legal situations.

Source: https://www.brookings.edu/articles/ai-and-the-visual-arts-the-case-for-copyright-protection/

Exploitation of Human Labor

Human workers often handle the classification and evaluation of training data: "Every breakthrough in AI capability represents thousands of hours of human labor, much of it performed under exploitative conditions." (Rodriguez 2025) Most of these workers live in countries of the Global South.

Source: https://www.createathon.net/post/the-ai-training-industrial-complex-inside-the-digital-sweatshops-powering-silicon-valley-s-future

Positive Aspects and Opportunities

1. Lower Entry Barriers

Learners without programming experience can achieve working results faster. Motivation increases through early success experiences.

2. Focus on Problem Solving instead of Syntax

Teaching can emphasize planning, structuring and problem analysis more strongly. Programming becomes more conceptual.

3. Time Savings in Development Process

Routine tasks are automated. More time for creative or didactic aspects.

4. Individual Support

AI explains code at different levels. Especially helpful for heterogeneous learning groups.

5. Inclusion in Computer Science Education

Learners with linguistic or mathematical barriers receive additional support. More participation possible.

6. Quickly Visible Results

Projects can be published within a short time. Promotes project-oriented learning, for which little time is normally planned.

Source: https://www.sciencedirect.com/science/article/pii/S0360131523001234