A new discussion on r/EngineeringManagers highlights a growing trend: companies allowing candidates to use AI tools during technical interviews. The thread, posted on September 19, 2026, has sparked debate among engineering leaders about the validity of assessing skills in an AI-assisted environment. As large language models become ubiquitous in daily development workflows, the traditional whiteboard algorithm question is facing an existential crisis. Is it cheating to use the same tools you use on the job, or is it the only fair way to test modern engineering capabilities?

The Core Debate

The original poster shared their experience of letting candidates use AI tools, prompting a heated discussion on whether this reflects real-world engineering practices or masks a lack of fundamental understanding. Many commenters argue that since developers use AI daily, testing should mirror this reality. They contend that a candidate who can effectively guide an AI to produce clean, working code demonstrates a higher level of competence than one who struggles to recall a sorting algorithm from memory but cannot build a functional application. However, a significant contingent of managers disagrees. They argue that allowing AI tools removes the ability to assess a candidate's foundational knowledge. If a candidate cannot explain why the AI-generated code works, or how they would debug it if it failed, they are merely acting as a prompt operator rather than an engineer. This group fears that relying on AI during interviews creates a false positive, hiring individuals who cannot perform when the AI hallucinates or fails to provide a solution.

Practical Implications

The conversation has moved beyond abstract philosophy to concrete changes in interview structure. Some managers suggest that the focus should shift from pure algorithmic problem-solving to evaluating how candidates integrate AI into their workflow. This includes assessing their ability to prompt effectively, verify outputs, and debug AI-generated code. One practical approach discussed involves giving candidates a buggy piece of code generated by an AI and asking them to identify the errors and fix them, thereby testing their verification skills rather than their generation speed. Furthermore, the debate has led to new types of questions. Instead of asking candidates to reverse a linked list, interviewers are asking them to design a system architecture and explain their trade-offs, using AI only for boilerplate generation. This shifts the evaluation metric to system design thinking and architectural judgment. Managers are also looking for candidates who can critique AI outputs, identifying subtle logical errors or security vulnerabilities that a naive user might miss. The ability to act as a reviewer of AI code is becoming a more critical hiring criterion than the ability to write it from scratch.

Industry Shift

This conversation reflects a broader shift in the tech industry as AI tools become ubiquitous. Companies are grappling with how to assess talent in a landscape where AI can solve many standard interview questions, potentially rendering traditional methods obsolete. The r/EngineeringManagers thread serves as a microcosm of this larger industry tension. While some organizations cling to traditional LeetCode-style assessments to filter for raw cognitive processing power, others are abandoning them entirely in favor of practical, AI-assisted coding challenges. The divide is often generational and role-dependent. Senior engineers, who have spent years building intuition, tend to be more skeptical of AI-assisted interviews, fearing a loss of depth in junior hires. Conversely, many hiring managers are recognizing that the market has changed. They are realizing that a candidate's ability to navigate AI tools is now a baseline expectation, not a differentiator. The challenge for these leaders is to create assessments that test the human element of engineering—critical thinking, system design, and problem decomposition—without penalizing candidates for using the tools that make them more productive.

Key Takeaways

  • Companies are increasingly allowing AI tools in technical interviews, mirroring real-world workflows.
  • The debate centers on whether AI usage tests actual engineering skill or merely prompt engineering proficiency.
  • Managers are shifting interview focus to evaluating code verification, debugging, and architectural judgment.
  • Traditional algorithmic questions are becoming less relevant as AI solves standard problems quickly.

The Bottom Line

Allowing AI in interviews isn't about cheating; it's about testing for the skills that actually matter in 2026: prompt engineering, verification, and integration. The industry must adapt or risk hiring for a skillset that no longer defines effective engineering.