A deeper look at how to actually assess engineering talent today

The Shifting Ground of Technical Hiring

Hiring software engineers has always been a high-stakes process. Companies invest weeks, sometimes months, to identify the right candidate someone who not only knows the tools but can also adapt to evolving systems, collaborate with teams, and solve complex problems under pressure.

But in 2025, the technical interview landscape has shifted dramatically. Artificial intelligence is now woven into the hiring process, whether through candidate-prep platforms, automated screening systems, or even AI “whispering answers” in real-time during interviews. Recruiters and hiring managers increasingly face a difficult question: how do we tell who can actually do the work, and who is leaning too heavily on machines?

The issue isn’t that AI is inherently bad. In fact, AI-powered learning tools have helped thousands of candidates practice, upskill, and enter the job market more effectively. The problem is that in high-stakes interviews, AI assistance can blur the line between authentic skill and borrowed intelligence. For technical hiring where one poor hire can set projects back months this creates a pressing challenge.

This article explores what AI can convincingly fake in a technical interview, what it can’t, and how interviewers can dig deeper to truly assess real engineering ability.

The Rise of AI in Candidate Interviews

Five years ago, the main concern in technical hiring was access: candidates needed prep platforms like LeetCode, HackerRank, or CodeSignal to sharpen their skills and practice standard problems. Recruiters worried about “over-prepped” answers, but the essence of the work was still human effort.

Today, things look very different. With the rise of tools like ChatGPT, GitHub Copilot, and dozens of “interview assistant” extensions, candidates can generate real-time solutions during a live interview. Imagine a coding question posed in a Zoom call:

An AI plugin listens in.
Within seconds, it suggests a clean, optimized answer.
The candidate repeats it back with minor adjustments, often buying time with pauses or filler phrases.

The effect is subtle but powerful. For inexperienced interviewers, it can be hard to tell the difference between a genuinely skilled engineer and a candidate parroting machine-generated output.

This raises two big challenges for employers:

Fairness Are we evaluating the candidate’s actual ability or the quality of their AI assistant?
Accuracy If we can’t tell the difference, how do we make confident hiring decisions?

What AI Can Fake Well

To address the challenge, it helps to be clear-eyed about what AI already does exceptionally well in interviews.

1. Syntax and Boilerplate Code

AI can easily generate correct function definitions, imports, and error-free syntax in virtually every mainstream programming language. Tasks that once tripped up junior engineers forgetting semicolons, misusing array methods, or structuring a basic class are now trivial.

2. Common Algorithmic Patterns

Dynamic programming, two-pointer methods, binary search trees, graph traversal AI has been trained on millions of examples. For common patterns, it can produce near-perfect answers instantly, often more efficient than a human under time pressure.

3. Surface-Level Explanations

Ask “what is the difference between SQL and NoSQL databases?” and AI can provide a polished, textbook-perfect answer. These explanations often sound confident and balanced, masking a lack of personal depth.

4. Buzzwords and Industry Jargon

Terms like “scalability,” “low-latency,” “microservices,” or “event-driven architecture” roll off AI outputs with ease. Candidates repeating these can sound sophisticated even if they’ve never designed a system at scale.

5. Template-Based Behavioral Answers

For questions like “tell me about a time you solved a difficult problem,” AI tools can generate STAR-format answers (Situation, Task, Action, Result) that sound polished but generic. Without probing, these can pass for real experiences.

What AI Can’t Fake (Yet)

Despite its strengths, AI struggles to replicate certain core human attributes that technical interviews are designed to surface. These are the signals hiring managers must learn to recognize.

1. Original Problem-Solving Under Pressure

AI can produce a known solution, but it struggles when faced with a truly novel or messy problem the kind engineers often face in real-world projects. A candidate’s ability to reason through uncertainty, make trade-offs, and adapt on the fly is very hard to fake.

2. Debugging and Error Recovery

Writing code from scratch with AI is easy. Debugging a partially broken system, however, requires intuition, pattern recognition, and contextual memory. A candidate’s process of diagnosing errors, forming hypotheses, and testing fixes reveals authentic skill.

3. Depth of Past Experience

When asked to describe a time they scaled a database or optimized a workflow, real engineers draw from vivid, specific experiences: tools they used, trade-offs they made, failures they learned from. AI-generated answers lack this depth of narrative texture.

4. Non-Verbal Communication and Confidence

Pauses, hesitations, or stiff body language often reveal when a candidate is relaying machine-fed answers. Skilled engineers tend to be more fluid in explaining their reasoning, weaving code with rationale in real time.

5. Collaborative Mindset

Engineering is not solo heroics; it’s teamwork. AI cannot convincingly simulate the nuances of collaboration negotiating with product managers, mentoring juniors, or resolving disagreements. When candidates speak about teamwork, authentic stories ring true in ways that canned AI scripts cannot.

6. Curiosity and Questions

Great engineers ask great questions. They probe requirements, clarify edge cases, and seek context. AI-fed candidates may sound like they have answers, but they rarely show the intellectual curiosity that defines strong hires.

Red Flags for Interviewers

So, how can hiring managers detect when AI is in play? A few patterns stand out:

Delays before answering Candidates may pause unnaturally while waiting for AI suggestions.
Overly polished answers Responses that sound rehearsed, lacking personal detail or struggle.
Inconsistent depth A candidate may nail a complex algorithm but stumble on follow-ups or basic variations.
Shallow storytelling Behavioral answers feel vague, with no clear “messiness” of real projects.
Rigid communication Instead of thinking out loud, the candidate recites full blocks of text or code.

None of these alone prove dishonesty. But taken together, they should prompt interviewers to dig deeper.

How to Dig Deeper: Interview Tactics That Work

If AI makes surface-level evaluation harder, the solution is not to ban AI but to design interviews that test for authenticity. A few proven strategies:

1. Live Debugging Sessions

Give candidates a broken codebase and ask them to fix it collaboratively. Watch their reasoning process rather than just the final answer.

2. System Design Questions

These are less about syntax and more about trade-offs. Ask how they’d design a messaging system for millions of users. Probe on choices: Why Kafka vs RabbitMQ? How would they handle retries? AI struggles to improvise here.

3. Behavioral “Depth Drilling”

Don’t stop at the first STAR-format answer. Ask follow-ups: “What was the hardest trade-off you made? How did your team react? What did you wish you’d done differently?” Authentic stories get richer with probing. AI-generated ones collapse.

4. Pair Programming

Work through a problem together. Encourage candidates to explain their thinking aloud. This mimics real engineering collaboration and makes it harder to simply regurgitate AI-fed answers.

5. Ask “Why?” Multiple Times

Even strong candidates may falter under repeated “why” questions, but their reasoning process becomes clear. AI-based parroting usually crumbles after the second or third layer of probing.

Case Study: Two Candidates, One Coding Challenge

Consider a scenario where two candidates are asked to solve a graph traversal problem.

Candidate A immediately produces a flawless depth-first search implementation. When asked why they chose DFS over BFS, they stumble, saying only, “DFS is usually faster.” Probing reveals they cannot explain time complexity trade-offs.
Candidate B takes longer, outlining their thought process: “DFS works here because we’re searching deep paths, but if the graph were weighted differently, I’d consider Dijkstra’s.” Their code has a minor bug, but they catch and fix it mid-way.

Who would you hire?

Candidate A may have leveraged AI. Candidate B demonstrates authentic reasoning, adaptability, and error recovery. In real-world engineering, those traits matter more than instant perfection.

The Future of Technical Interviews

We are entering an era where AI will be as common in job interviews as calculators once were in math classes. Eventually, companies may even encourage candidates to use AI tools, since real-world engineering already involves Copilot, Stack Overflow, and other aids.

But the goal of interviews is not to test whether someone can memorize algorithms; it’s to evaluate judgment, collaboration, and resilience in problem-solving. These human qualities remain stubbornly difficult for AI to fake.

The challenge for hiring managers is to adapt their playbooks. Over-reliance on rote coding challenges will only lead to false positives. Instead, interviews must emphasize process over product, probing how candidates think, question, and learn.

Back to the Human Core

AI has undoubtedly raised the bar for technical hiring. It can mimic fluency, speed, and even structured storytelling. But it cannot yet replicate the messy, creative, human aspects of engineering: the “aha!” moments of debugging, the collaborative give-and-take of teamwork, the curiosity that drives innovation.

For hiring managers, this means the task is not to outwit AI but to design interviews that reveal the qualities AI cannot fake. For candidates, it means leaning into authenticity sharing stories, thought processes, and genuine curiosity that no machine can provide.

In the end, the future of technical interviews may not be about eliminating AI but about rediscovering what makes humans unique problem-solvers.