Analyze the following discussion about "Is DSA still necessary when modern programming languages provide built-in libraries?"

Compare all participants' viewpoints. Identify:

Common arguments supporting DSA Arguments supporting built-in libraries Differences in opinions Real-world software engineering perspective Examples where DSA knowledge is required despite libraries Examples where libraries are the better choice Final balanced conclusion

Create a professional developer-level answer explaining why DSA and libraries are complementary, not replacements for each other.
Focus on scalability, performance optimization, debugging, system design, and engineering decision-making.

The combination of Artificial Intelligence (AI) and Robotics is expected to have the greatest impact by 2035. AI enables machines to learn and make decisions, while robotics allows them to perform physical tasks. Together, they can revolutionize industries such as manufacturing, healthcare, transportation, and logistics, leading to increased productivity and innovation.
Several emerging technologies will shape the future by 2035, including Artificial Intelligence (AI), Robotics, Biotechnology, Clean Energy, and Quantum Computing. However, AI is expected to have the broadest impact because it can enhance and accelerate the development of other technologies while transforming industries, economies, and daily life.

AI answers reduce clicks → fewer ads → revenue risk.
Google must rethink on Ad placement inside AI responses
Subscription models. This is a fundamental shift, not just a tech upgrade. The real challenge is monetization, not technology.

Agentic AI represents more than just automation with a fancier name because it can make decisions, plan steps, and adapt to new situations without constant human guidance. Traditional automation follows fixed rules and performs repetitive tasks, while agentic AI can break down complex goals and determine how to achieve them. This allows it to handle multi-step workflows such as researching information, writing code, and interacting with external tools. The difference lies in flexibility and reasoning rather than simple execution. While it builds on automation, agentic AI is considered a significant leap due to its autonomy and ability to manage complex tasks.

Prompt engineering can be seen as both a real skill and a temporary workaround, depending on how AI technology evolves. It is a real skill because crafting effective prompts requires clarity, structure, and an understanding of how AI systems interpret instructions. Good prompts can significantly improve accuracy, relevance, and quality of responses across tasks like coding, writing, and analysis. However, as AI models become more advanced, they may require less detailed prompting and better understand natural language. Even then, the ability to clearly guide AI and communicate intent will remain a valuable and lasting skill.

Quantum computing poses a significant threat to modern encryption because it can solve complex mathematical problems that classical computers cannot efficiently handle. Many current encryption systems, such as RSA and elliptic curve cryptography, rely on these difficult problems to secure data. A powerful quantum computer running specialized algorithms could potentially break these protections much faster than today’s machines. This creates a risk where attackers may store encrypted data now and decrypt it later once quantum technology becomes mature. However, researchers are developing post-quantum cryptography to replace vulnerable methods and ensure data remains secure in the future.

For beginners, starting with frontend development is often recommended. It is easier to visualize results because you can immediately see what you build in the browser using HTML, CSS, and JavaScript. This helps beginners understand core web concepts and stay motivated.

Once comfortable with frontend basics, learning backend development (APIs, databases, and server logic) becomes easier. Ultimately, both fields offer strong career opportunities, and the best choice depends on a person’s interests.

In the tech industry today, learning ability is just as important as both degrees and skills. Technology keeps evolving, so developers and engineers need to constantly adapt to new tools, frameworks, and ideas. Someone who is curious and willing to learn can grow faster than someone who only relies on past qualifications. A degree can give a structured start, and skills show what you can actually build. But the real advantage comes from staying updated, experimenting with new technologies, and continuously improving. In the long run, the ability to learn and adapt is what truly sustains a career in tech.

Low-code and no-code platforms are powerful in that they simplify application development by replacing traditional hand-coding with visual interfaces and drag-and-drop components, making technology more accessible to non-technical users. Rather than being a threat to developers, these platforms are a natural evolution in the industry — just as calculators didn't eliminate mathematicians, these tools won't eliminate programmers. They handle the repetitive, straightforward builds so that skilled developers can redirect their energy toward complex architecture, system security, and high-level problem solving and and those who adapt by learning to govern, extend, and optimize these environments will find themselves more in demand than ever.