The Next Software Engineering Interview Won't Be About Writing Code. It'll Be About Generating Code With AI

For decades, software engineering interviews have looked almost identical.

A recruiter schedules a technical round. The interviewer asks you to solve a Data Structures and Algorithms (DSA) problem. If you pass, you move on to a system design interview, followed by discussions about databases, networking, frameworks, operating systems and software engineering fundamentals. Finally, you might build a small feature during a live coding session. The objective was simple: prove that you could think like a software engineer before joining the company.

This hiring process has worked for years because companies wanted evidence that a candidate could think like an engineer before writing production code.

Then AI arrived.

Today, millions of developers use AI coding assistants every day. They generate boilerplate code, explain unfamiliar APIs, write tests, debug issues, review pull requests, and even suggest architectural improvements. AI is no longer an optional productivity tool — it is becoming part of the daily development workflow.

Many people believe AI will eliminate coding interviews.

I don't think that's what will happen.

I think AI will transform them.

I believe the next generation of software engineering interviews won't look like this anymore:

Can you write the code and solve this problem?

Instead, they'll ask:

Can you solve this problem effectively with AI?

Imagine walking into a software engineering interview three years from now.

Instead of opening a blank editor, the interviewer gives you access to an AI coding assistant. There are no restrictions on using AI because that's how modern software is built today. In many ways, it's like an open-book exam — the interviewer already knows you have access to the answers. What they're really evaluating is how effectively you use those resources to solve the problem, make sound engineering decisions, and deliver a high-quality solution. However, there are two important constraints:

1. You have only two prompts to interact with the AI.
2. You have a limited token budget.

Suddenly, the interview isn't about who can type the fastest. It's about who knows how to collaborate with AI most effectively.

The interviewer evaluates whether you can guide AI with clear prompts, recognize incorrect or inefficient code, optimize it, and apply core engineering fundamentals like time and space complexity, system design, and computer science principles to validate the final solution.

Here are the areas I believe future interviews will focus on:

Prompt Engineering

The very first thing an interviewer notices is how you communicate with AI.

A great engineer doesn't ask vague questions like "Build this API." Instead, they provide context, explain business requirements, specify constraints, define the expected output, and mention any performance or security requirements.

A well-structured prompt often produces high-quality code on the first attempt, while a poorly written prompt leads to multiple retries and inconsistent results.

Prompt engineering isn't about clever wording — it's about expressing engineering requirements clearly.

Token Consumption

Every interaction with an AI model consumes tokens, and tokens cost money.

For an individual developer this may seem insignificant, but for organizations with thousands of engineers using AI every day, unnecessary token usage can become a substantial operational expense.

Future interviewers may observe whether candidates solve problems efficiently instead of repeatedly regenerating responses:

• Can you provide only the context the model needs?
• Can you refine the existing response instead of starting over?
• Can you achieve the desired solution with minimal AI interactions?

Efficient token usage could become an engineering skill, much like writing memory-efficient or time-efficient code.

Reviewing AI-Generated Code (Avoiding AI Slop)

Generating code is easy. Knowing whether the generated code is actually good is much harder.

AI sometimes produces code that looks correct but contains hidden bugs, poor architecture, duplicated logic, inefficient algorithms, security vulnerabilities, or maintainability issues.

This is often referred to as "AI slop" — code that appears impressive but isn't suitable for production.

A strong engineer reviews AI output critically. They identify edge cases, improve readability, optimize performance, remove unnecessary complexity, and ensure the implementation aligns with engineering standards.

The interviewer isn't evaluating whether AI can write code. They're evaluating whether you can recognize good code from bad code.

Working with AI Agents

Modern software development is moving beyond a single AI chatbot. Instead, developers are beginning to use specialized AI agents that each perform a specific role:

• One agent may analyze a codebase.
• Another may generate unit tests.
• Another may review security issues.
• Another may update documentation.
• Another may suggest refactoring opportunities.

Instead of doing everything manually, engineers will increasingly coordinate these specialized agents to complete complex tasks.

Future interviews may evaluate whether candidates understand how to delegate work effectively between themselves and AI.

Understanding MCP and AI Tooling

AI is becoming much more powerful because it can interact with external tools instead of relying only on its internal knowledge.

Technologies such as Model Context Protocol (MCP) allow AI systems to access repositories, documentation, issue trackers, testing frameworks, databases, code graphs, and many other development tools.

Imagine asking AI to understand an entire codebase before making a change, automatically execute tests, inspect dependencies, or review a pull request with full project context.

Candidates who understand how to integrate AI with these tools will likely have a significant advantage over those who only know how to chat with AI.

System Design Still Matters

Some people believe AI will replace system design interviews. I don't.

AI can generate architecture diagrams and suggest different approaches, but it cannot fully understand business priorities, operational constraints, scalability requirements, cost considerations, or long-term maintenance without human guidance.

Engineers still need to make architectural decisions. They must understand distributed systems, databases, caching, messaging, APIs, fault tolerance, scalability, and engineering trade-offs.

AI may generate possible solutions. The engineer decides which solution should actually be implemented.

Strong Engineering Foundations Remain Essential

Perhaps the biggest misconception is that AI removes the need to learn computer science fundamentals. In reality, the opposite may be true.

Without understanding algorithms, data structures, networking, databases, operating systems, concurrency, and software engineering principles, it's impossible to judge whether AI's answer is correct.

AI can confidently produce incorrect solutions. Only someone with strong fundamentals can recognize those mistakes.

The better your engineering foundation, the more effectively you'll be able to collaborate with AI.

Final Thoughts

I don't believe Data Structures and Algorithms are disappearing. I don't believe system design interviews are disappearing. I don't believe computer science fundamentals are becoming irrelevant.

Instead, I believe they're becoming the foundation upon which a new skill is being built: AI-assisted software engineering.

The strongest software engineers of the future won't simply be great programmers. They'll know how to write effective prompts, control token consumption, review AI-generated code, coordinate AI agents, use MCP-powered development tools, design scalable systems, and apply strong engineering fundamentals to validate every AI-generated solution.

The era of manual coding interviews is giving way to AI-assisted interviews. I believe the next generation of technical interviews will reflect exactly that.