Stop Hiring for LeetCode: Find Real Developers for Real Work

Introduction

Modern companies continue to face a serious challenge in hiring employees: candidates spend months preparing for interviews, solving LeetCode problems that often have little to do with real workplace challenges. With the rise of AI-powered tools, remote work, and rapidly shifting business needs, the gap between interview tasks and real job requirements has become even more pronounced. As a result, businesses end up with specialists who can solve artificial tasks but struggle with real projects.

Recently, Sam Altman, the CEO of OpenAI, noted that mastering AI tools has now become even more important than simply being good at coding. In his words, “the obvious tactical thing is just get really good at using AI tools… that’s the new version of learning to code.” This signals that it’s time to fundamentally rethink our hiring approaches and shift the focus from artificial interview puzzles to practical skills and adaptability in an AI-driven workplace.

The Drawbacks of LeetCode-Style Assessments

Screening candidates with algorithmic challenges like those on LeetCode has a number of fundamental flaws:

• No connection to real work: LeetCode tasks check your ability to solve abstract algorithmic problems, which rarely arise in real jobs.
• Selection for preparation, not talent: Those who perform best are often those who spend the most time practicing, not necessarily the best engineers.
• Ignores creative and strategic thinking: LeetCode doesn’t measure creative or strategic thinking, the skills most valued today.
• Losing promising candidates: Many talented professionals skip such interviews because they lack the time or motivation to prepare for meaningless exercises.

The Key Qualities of the “Ideal” Specialist Today

Today’s challenges require employees with fundamentally different characteristics:

• Practical problem-solving: The ability to quickly and effectively find solutions to real business problems.
• Adaptability and fast learning: Willingness and ability to rapidly master new technologies and tools, including AI-powered solutions.
• Creative and strategic (Inventor) mindset: The ability to suggest unconventional approaches, improve processes, and create new products.
• Effective communication and teamwork: The capacity to clearly communicate ideas and collaborate productively within a team.

A Better Hiring Process: Step-by-Step

Step 1: Real-World Challenges Instead of Abstract Algorithms

• Idea: Instead of abstract algorithmic puzzles, give candidates real cases from your company — the kinds of challenges they’ll actually face at work. This allows you to assess not just technical knowledge but the ability to think in real context.
• Example: Instead of “implement quicksort,” give a scenario like: “In our delivery service, some customer orders are occasionally lost from the queue when several operators make changes at once. How would you investigate? What questions would you ask, and how would you look for a solution? If you like, sketch out some code or a diagram.”
• Why it matters: This approach assesses candidates’ thinking in conditions close to your business logic. It shows how a person frames the problem, works with constraints, and weighs trade-offs — all vital for real work. It identifies people who can see beyond the code and grasp the meaning of what’s happening — those who can analyze and propose thoughtful solutions, not just “solve puzzles quickly.”

Step 2: Stress-Testing — Introducing Uncertainty and Constraints

• Idea: Along with real cases, introduce an element of surprise, like a mid-task requirement change (just as in real work). This reveals how candidates handle uncertainty and pivot their thinking.
• Example: Assign a small development task, then halfway through, shift the priority — say, optimize for speed instead of scalability. Assess how the candidate adapts and justifies their decisions.
• Why it matters: Real work rarely follows a script. The ability to switch gears fast is a critical skill.

Step 3: Assessing Ability to Learn Quickly

• Idea: Allow candidates to use the internet, documentation, and real tools, just as they would on the job.
• Example: Let them use reference materials, Google, or IDE suggestions as they work. Afterwards, ask them to briefly explain how they searched for information, why they chose their solution, and what they learned in the process.
• Why it matters: In modern development, it’s not about knowing everything by heart — it’s about learning and applying new things fast. This reveals candidates who are comfortable with new technologies and can learn “on the fly,” not just recite what they already know.

Step 4: Testing for “Meta-Learning” Skills

• Idea: Rather than only testing information-finding, check how a candidate structures their own learning process. For example, ask them to master a new tool or library they’ve never used before, and explain how they went about it.
• Example: Give them 30 minutes to explore documentation for a framework (e.g., FastAPI), then ask them to implement a simple function and describe how they chose what to read and how they organized their learning.
• Why it matters: In a world of ever-changing tech, “learning how to learn” is more important than what you already know.

Step 5: Evaluating the Ability to Use Artificial Intelligence Effectively

• Idea: Make it part of the task to use AI tools such as ChatGPT, Copilot, or similar, not just for technical skill, but for leveraging modern assistants.
• Example: Assign a task that requires using AI: for instance, use Copilot to draft prototype code, or formulate an optimal prompt for ChatGPT to generate tests or quickly check a hypothesis. Ask the candidate to explain why they chose that approach, what prompts they used, and what they trusted the AI to do.
• Why it matters: Being able to use AI correctly is now an essential developer skill. It shows how flexible, up-to-date, and self-aware the candidate is — do they use tools to boost productivity, or just “outsource” the whole job?

Step 6: Assessing Ethical AI Usage

• Idea: It’s not enough to use AI effectively — developers also need to use it responsibly. Add an ethical or responsibility component to the AI assessment. Ask candidates to identify risks associated with incorrect or biased AI outputs and propose ways to minimize these risks in real-world work.
• Example: Give a task where the AI (e.g., ChatGPT) produces partly incorrect code or solutions. Ask the candidate not only to fix the issue, but to explain how they detected the error and what steps they would take to prevent similar problems in a real project.
• Why it matters: As reliance on AI grows, employees must understand the limitations of these tools and use them responsibly. Just as importantly, they need to develop the habit of critically evaluating AI-generated content, not only for ethical risks, but also to prevent technical errors and reduce business risks.

Step 7: Soft Skills and Teamwork

• Idea: Simulate collaboration — have candidates discuss a problem in a mini-group or participate in a group project brainstorm.
• Example: Divide candidates into small teams and give them a joint project (such as designing a service architecture or rapidly improving a function). Watch how each member articulates ideas, listens to others, and gives and receives feedback. Consider adding a contentious issue or disagreement to see how candidates behave in a discussion.
• Why it matters: Even the best technical specialist can’t deliver a complex project alone. The ability to communicate, listen, and resolve disputes directly impacts effectiveness and team atmosphere.

Step 8: Evaluating Leadership Potential

• Idea: In the team simulation, add an element where a candidate needs to show initiative or propose a direction. This reveals not just communication but also leadership ability.
• Example: Give the group an ambiguous task with no appointed leader, and observe if the candidate takes initiative, delegates, or proposes a working structure.
• Why it matters: Even in non-managerial roles, leadership qualities (initiative, ability to motivate) often determine project success.

Step 9: Assessing Real-World Experience and Achievements

• Idea: Focus on real cases from the candidate’s past work to understand their actual impact, not just “titles” or lists of technologies on their CV.
• Example: Conduct a deep interview on key projects: ask the candidate to describe their role, what tasks they personally solved, and provide specific examples of successful implementations, optimizations, or changes. Be sure to clarify how these impacted the business, how results were measured, and ask about failures, and what was learned.
• Why it matters: This helps you distinguish between candidates with real experience and mature professional thinking, and “theorists” with a pretty résumé. The ability to analyze successes and mistakes, draw lessons, and grow is the mark of a high-value hire.

Step 10: Checking for Cultural Fit

• Idea: Evaluate how well the candidate’s values align with your company’s culture. Discuss their approach to resolving conflicts, setting priorities, or responding to hypothetical value-related scenarios.
• Example: Ask, “What would you do if your manager insisted on a decision you believe is unethical?” or, “Tell us about a time when you sacrificed short-term benefit for a long-term goal.”
• Why it matters: Even the most talented specialist may not thrive if their values and work style don’t align with the company’s culture. At the same time, companies should periodically review their own culture for inclusivity and flexibility, ensuring it enables diverse talents to thrive.

Practical Tips for Companies Implementing the New Interview Format

• Create working groups of technical staff and HR to design real case studies and evaluation criteria.
• Train interviewers in the new format, emphasizing the importance of assessing candidates’ approaches and thinking processes.
• Regularly update your cases to keep interviews relevant to current company challenges.
• Use gamification: Turn some interview stages into quests or story-driven problems set in realistic business situations. This reduces stress and makes the process more engaging and less formal.
• Automation and analytics: Use automated tools for initial candidate screening. For example, set up a platform where candidates can upload solutions or take short simulations, allowing HR to filter those who meet baseline requirements, saving time during the initial screening stage.
• Solicit feedback and refine: After each interview, collect anonymous feedback from candidates on the process. This helps identify weak points and improve your approach, attracting more strong candidates.
• Be transparent with candidates: Clearly outline the process, stages, and evaluation criteria up front, and provide sample problems if possible. This builds trust and attracts candidates who value openness.

Conclusion

Adopting a new interview format is not just desirable — it is essential. Using this approach, companies can find employees who quickly solve real-world problems, think creatively, and work effectively in teams. This will help businesses respond to current challenges and confidently move forward in the age of artificial intelligence and rapidly evolving technology.

Even partial implementation of these steps can raise the quality of your hiring process.
Companies that are first to adopt the new interview format will have a competitive edge in attracting the most talented specialists.