Coding Interviews in 2026

Summary

The video discusses the evolving landscape of coding, tech interviews, and the role of AI tools in software development. While AI is increasingly used to assist in coding, traditional skills like DSA (Data Structures and Algorithms) remain important in interviews. The speaker emphasizes that AI should not replace the need for deep understanding of coding fundamentals, using personal hiring experiences to illustrate how AI-generated code can still lead to mistakes. The speaker promotes a platform, Neat Code, which blends pattern-based learning with AI-enhanced tools, offering smart study plans for coding interview preparation.

Takeaways

• 😀 Traditional DSA questions are still being asked in interviews at companies like Anthropic and OpenAI, despite the rise of AI coding tools.
• 😀 AI-assisted coding interviews are emerging, but they tend to be harder and require a solid understanding of algorithms and coding fundamentals.
• 😀 DSA isn't dead yet, and companies still value the foundational knowledge that comes with it, even with the growing use of AI tools.
• 😀 Even in the era of AI, companies still hire for technical skills, problem-solving ability, and the ability to communicate and understand code, not just for the ability to generate code.
• 😀 AI tools can speed up the development process, but they cannot replace critical thinking, problem-solving, and the ability to clean up messy code.
• 😀 A checklist for solving coding problems is essential for any competent developer, as it ensures correctness, performance, and maintainability.
• 😀 Even with AI, coding interviews and job performance require understanding the problem at hand, ensuring code correctness, and optimizing performance when necessary.
• 😀 Companies aren't hiring people solely for their ability to solve DSA problems; they are looking for candidates who deeply understand the problems and the solutions they propose.
• 😀 AI tools should be used to enhance, not replace, problem-solving skills. It’s essential to be able to discern when AI-generated solutions are flawed or incomplete.
• 😀 Personalized study plans tailored to your time constraints and the specific company you’re interviewing for can greatly improve your coding interview preparation.
• 😀 The skill of thinking critically about code—questioning its correctness, performance, and maintainability—is essential, especially when using AI tools.

Q & A

What is the current state of coding interviews in 2026?

In 2026, coding interviews are evolving. While AI tools like GPT are increasingly being used in coding processes, many companies, including big tech players like Meta and Anthropic, are still relying on traditional coding assessments such as DSA (Data Structures and Algorithms) to evaluate candidates. Despite AI advancements, the importance of core technical skills remains intact.

Are Data Structures and Algorithms (DSA) still important for coding interviews?

Yes, DSA is still crucial in coding interviews. Even with AI-assisted tools, companies continue to ask DSA-related questions to ensure candidates understand fundamental problem-solving techniques and algorithms. DSA skills help demonstrate your ability to reason through problems and apply algorithms effectively.

Why are companies like Anthropic and OpenAI still asking DSA questions despite AI tools?

Companies like Anthropic and OpenAI continue to ask DSA questions because the purpose of these interviews is not just to test raw coding ability but to evaluate problem-solving, understanding of algorithms, and ability to think critically. AI tools can assist with implementation, but a strong foundation in DSA ensures candidates can validate and explain their solutions.

How does AI-assisted coding affect coding interviews?

AI-assisted coding interviews are more complex than traditional DSA interviews. They require candidates to use AI tools effectively while still demonstrating an understanding of the code they write. The AI can speed up development, but candidates must still ensure their code works correctly and explain their solutions. AI tools also elevate the interview bar by requiring candidates to handle more complex problems.

What is the main benefit of using AI tools in coding interviews?

The main benefit of using AI tools in coding interviews is increased efficiency. AI tools can help candidates write code faster, debug issues, and offer code suggestions. However, the candidate must still understand the solution to ensure correctness and explain the choices made during development.

What are some challenges companies face when hiring developers in the age of AI?

One challenge is ensuring that candidates are not overly reliant on AI tools and can still write clean, maintainable code. Even though AI can help with repetitive tasks, companies still need developers who can understand the code, think critically, and contribute to the long-term maintenance and scalability of a project. Another challenge is balancing speed with code quality.

Why did the hiring example with the individual who relied on AI fail?

The individual failed because, while they used AI tools to assist in coding, they did not fully understand the code. They missed key details like reading the code, fixing bugs manually, and understanding the problem at a deeper level. This lack of engagement with the code led to errors and poor quality work, even though the person had some experience.

How does NeatCode support coding interview preparation?

NeatCode provides a structured learning approach by offering pattern-based learning, quick code execution, and solutions for every problem. It also includes hints and debugging support, allowing users to improve their understanding and skills. NeatCode tailors study plans for specific companies, helping users focus on the most relevant problems they might encounter in their interviews.

What role do soft skills play in coding interviews?

Soft skills, such as communication, problem-solving, and the ability to work with others, play a crucial role in coding interviews. Being able to explain your thought process, collaborate effectively, and understand the problem in detail are important qualities that go beyond technical skills, especially in team-based development environments.

How do AI tools change the nature of problem-solving for developers?

AI tools change the nature of problem-solving by making coding faster and more efficient, but they do not replace the need for critical thinking. Developers still need to understand the problem, validate AI-generated solutions, and ensure correctness. AI tools can handle certain tasks, but developers must still provide oversight and make decisions based on the project's needs.