Artificial intelligence is rapidly transforming the IT industry, promising convenience and speed. But behind this facade lie serious risks: the degradation of skills and the creation of a generation of developers incapable of critical thinking. It’s time to ask ourselves: Are we becoming hostages to a technology that promised us freedom?

1. Crisis of Fundamental Knowledge: "The Operator Generation" AI is turning development into a "puzzle assembly." Young programmers are losing the ability to understand why code works. The more people rely on AI, the less they develop their own judgment and cognitive skills.

This will lead to:

Decline in the quality of architectural solutions: Developers will create systems that work but are inefficient.

Loss of the ability to solve complex problems: If developers stop understanding the basics of programming, they won’t be able to tackle tasks requiring deep knowledge of algorithms, architecture, and optimization.

Loss of the ability to innovate: If you don’t understand how basic things work, you won’t be able to create something new.

Research:

A study by Microsoft and Carnegie Mellon University showed that the more people rely on generative AI tools to complete tasks, the less they demonstrate critical thinking skills.

The article "The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers" notes that beginners relying on AI may struggle to develop metacognitive skills and face an illusion of competence.

AI is like a crutch. If you use it constantly, your muscles atrophy. The same happens to the brain: if you don’t train it, it becomes weaker.

1. Junior Developers Under Threat: How AI Will Create a Shortage of Senior Specialists Artificial intelligence is already capable of generating simple code, performing tasks that were once assigned to junior developers. Why would companies hire newcomers if AI can do the same work faster and cheaper? At first glance, this seems like progress: companies save resources, and projects are completed faster. However, behind this convenience lies a serious threat to the entire industry.

Imagine this: today, AI replaces junior developers, and in 5–10 years, the industry will face a catastrophic shortage of experienced professionals. Senior developers will become even rarer and more expensive. Companies will be forced to compete for a limited number of specialists, leading to rising salaries and increased development costs.

1. Vulnerabilities in AI-Generated Code AI generates code without considering security. The number of repositories containing personal and payment data has tripled, the number of APIs lacking authorization and input validation has increased tenfold, and the number of exposed confidential API endpoints is growing. As AI-generated code scales, so do the risks to application security, highlighting the need for more effective risk identification and management.

The company Apiiro, with support from Gartner Research, published a concerning report on the security of AI-generated code. Experts analyzed millions of lines of code across dozens of companies in the financial, industrial, and tech sectors. The analysis revealed that when using AI to write programs, security inevitably takes a backseat to development speed.

Apiiro specialists propose a comprehensive approach to solving the problem. First, it’s crucial to implement advanced automated code analysis systems that can detect vulnerabilities during the development stage. Additionally, new security standards tailored to AI development must be developed. Furthermore, the protection of repositories containing sensitive data should be strengthened by implementing multi-layered access control systems.

Trusting AI without verification is like trusting a blind driver to navigate a mountain road.

1. AI Hallucinations: Lies Without Blushing AI doesn’t understand what truth is. It simply predicts the next token based on the data it was trained on. And it can mislead even experienced developers.

Code Hallucination (2024): The authors explore various types of hallucinations in code generated by large language models and present the HallTrigger technique for effectively creating such hallucinations.

Conclusion: What Should We Do?
Recommendations for developers:

Don’t fear the future: Let this environment motivate you to strive for excellence, not procrastination.

Learn the basics: Algorithms, data structures, patterns—the foundation without which you can’t solve complex problems. Make them chase you, not the other way around.

Don’t delegate everything to AI during training: Write code manually, even if it takes longer.

Train critical thinking: Ask yourself, “Why did AI suggest this?”

Don’t forget about security: If you don’t verify its suggestions, you risk creating an insecure product.