If you’re a founder or hiring lead at a fast-moving startup, chances are you’ve fine-tuned your tech interview process. Maybe it’s a slick combo of coding challenges, take-homes, and pair programming that helps you spot top-tier engineers.
But let’s be real for a second. There’s a major shift happening, and it’s probably already crept into your process: candidates are leaning hard on AI during interviews. Whether it’s quietly firing up ChatGPT during a live coding round or letting GitHub Copilot do the heavy lifting on take-home projects, AI for interviews is becoming the norm, not the exception.
The line between what a candidate can do and what their AI assistant can do? Yeah, it’s getting fuzzier by the day. And if you’re not already thinking about how AI interviews are changing your hiring signals, you’re gonna feel it soon.
AI in Interviews: The Data Founders Can’t Ignore
Nearly half of companies are jumping on the AI interview train
A survey from Resume Builder showed that only about 10% of companies were using AI for interviews but 43% said they were planning to roll it out the next year. So yeah, that’s 43% of teams either already using AI or getting ready to plug it into their hiring process. (Source)
Candidates are using AI in interviews and flying under the radar
Interviewing.io ran a little experiment where devs used ChatGPT during mock tech interviews. Out of 32 sessions, not a single interviewer realized the candidate was getting AI help. Zero. That’s how invisible it can be. (Source)
The New Reality: How Engineers Are Using AI in Your Interview Process
Alright, let’s talk about what’s really going down in interviews across the startup world. Whether you’re hiring your first few engineers or scaling up a dev team, it’s time to accept a new reality: AI is already sitting in on your interviews, just not on your Zoom invite list.
Live Coding Interviews? AI’s in the Room.
When you’re running remote interviews, don’t be surprised if the candidate’s not flying solo. A lot of engineers are quietly running an interview with AI on the side, quite literally. Think dual monitors, ChatGPT open in a second window, or a quick Google Bard prompt on their phone to untangle a tricky algorithm mid-call. It’s happening more than you think.
Take-Home Assignments = AI Playground
Take-homes are where AI interviews really show up. Engineers are using tools like GitHub Copilot or Claude to knock out entire assignments, sometimes without even looking at the problem first. And no, most aren’t raising their hands to mention it. For founders trying to gauge actual skills, this makes it tricky to know where the human ends and the AI begins.
Discover which method is best for your startup. Take Home Assignments vs. Pair Coding? Read it here.
System Design? AI’s Sketching the Diagrams
Even the traditional whiteboard-style system design interviews aren’t off the table anymore. Candidates are leaning on AI to mock up advanced architectures that sound great, but things fall apart when you ask them to tweak something on the fly. That’s when it becomes clear they didn’t fully wrap their heads around the system, they just plugged a prompt into an AI and memorized the output.
Tech Assessments: AI Speed Runs
Multiple-choice quizzes and online tests? Those are a breeze when you’ve got AI doing the thinking. Candidates are screen-grabbing questions, tossing them into ChatGPT, and breezing through assessments in record time. The scores look amazing, but then you realize the candidate couldn’t replicate that performance in a live setting.
So yeah, how to use AI for an interview isn’t just a niche blog topic anymore. It’s what candidates are already doing, and if you’re not adjusting for it, you might end up hiring the AI, not the engineer.
The Hidden Consequences of AI-Heavy Interviews on Your Startup
Sure, the big red flag is obvious: hiring someone who can’t really code without AI help. But there’s more going on under the hood, and some of these hidden risks can mess up your startup way faster than you’d expect.
The False Positive Trap
Startups with tight budgets and even tighter timelines are especially vulnerable to false positives. That engineer who crushed the AI interview might’ve had ChatGPT whispering answers the whole time. Then they join your team… and can’t actually deliver without a prompt window open. Meanwhile, your product deadlines slip, you burn through precious cash, and by the time you realize what’s up, the damage is already done.
When AI Hires Wreck Team Culture
It’s not just about bad code. It’s about what it does to your team. When someone coasts through interviews with AI help and then underdelivers, your existing team notices. Fast. Your top engineers start wondering why leadership brought in someone who can’t hang. That kind of thing kills morale, slows the team down, and can even push your best people out the door. Not ideal when you’re trying to move fast and build trust.
The Never-Ending AI Dependency Loop
If someone leaned on AI during the interview, chances are they’ll lean on it even more on the job. That might fly during chill dev cycles but what about during an urgent production issue at 3AM? Or when you’ve got five hours to ship a fix and AI tools just aren’t cutting it? Knowing how to use AI for an interview isn’t the same as knowing how to troubleshoot under pressure without a crutch. And that’s where things get risky.
Bottom line? AI for interviews might help candidates look great in the moment but it can quietly sabotage your team if you’re not careful.
Why Letting Candidates Use AI in Interviews Might Actually Be a Smart Move
Yeah, there’s plenty of concern around AI for interviews, and for good reason. But some founders are starting to lean into it instead of fighting it, and honestly, they might be onto something.
It’s How Real Engineers Work Now
Let’s face it: most engineers already use AI tools on the job. Whether it’s Copilot auto-completing boilerplate or ChatGPT helping unblock a tricky bug, AI is baked into the modern workflow. So if your AI interview process mimics that reality, you’re actually evaluating candidates on how they really get things done, not on how well they’ve memorized Leetcode.
Prompt Engineering Is the New Debugging
Being able to work with AI is becoming just as important as knowing how to write code from scratch. Figuring out how to use AI for an interview, asking the right questions, filtering out bad outputs, and knowing when to trust the results is a legit skill. If a candidate can show they know when to rely on AI and when to go manual, that’s a win, not a red flag.
More Inclusive, Less Elitist
Old-school interviews with brain-bending algorithms tend to favor candidates from top-tier schools or big tech companies where they’ve been groomed for those exact questions. But when you open the door to AI interviews, you create space for self-taught devs and folks from non-traditional backgrounds to show off how resourceful they really are. It’s not about gaming the system, it’s about updating the system to reflect how modern teams actually operate.
Bottom line? Interviewing with AI isn’t just the future, it’s kind of the present already. The key is figuring out how to evaluate what really matters: the thinking behind the tool, not just the tool itself.
The Flip Side: Why You Might Want to Keep AI Out of Interviews
Sure, AI for interviews can be a game-changer. But before you give every candidate a free pass to bring ChatGPT to the table, it’s worth considering why some startup teams are still drawing the line.
You Can’t Outsource Core Engineering Thinking
There’s a difference between solving a problem and understanding it. When systems go sideways in production or when your app starts doing something wild at 2AM, you need engineers who can reason from first principles, not just throw errors into a prompt window. Relying on AI in interviews can hide the fact that someone’s missing those deeper, hard-earned skills.
AI Doesn’t Always Have the Answers
Startups move fast. One week you’re building with familiar tech, the next you’re knee-deep in something brand new. Engineers who depend too much on AI tools can get tripped up the second they hit a weird edge case or a domain that AI hasn’t seen before. How does an AI interview work when the problem is truly unique? Often, it doesn’t and that’s where gaps show up.
It’s Not Just About the Code
Let’s be honest: great engineers aren’t just good at shipping, they’re good at working with people. Whether it’s talking through a tricky bug in a whiteboard session or pairing on something gnarly in production, collaboration matters. If someone crushed their interview with AI but struggles to communicate or work through problems without a digital crutch, it’s going to show up fast on the team.
Bottom line? While AI interviews can surface some cool skills, they also risk hiding the stuff that really matters when you’re building something from scratch under pressure. The key? Figure out what you’re really hiring for and test for that, with or without AI.
How Startups Are Actually Handling AI in Interviews: 4 Real-World Strategies
As ai for interviews becomes more common, startup teams are starting to pick sides—and some of the top players are getting creative. Here’s a look at how different startups are tackling the whole interview with AI thing without letting it mess up their hiring signals.
1. The “Be Real With Us” Approach
Some startups are leaning into transparency instead of banning AI. Their rule? Use whatever tools you want during the interview, just don’t hide it. Candidates are asked to share their screen, talk through what they’re doing, and explain why they’re prompting AI a certain way. It turns the interview into more of a collaboration test: how do you think through a problem, and how do you use AI as a tool, not a shortcut?
2. The Controlled Sandbox
Other teams are building out locked-down interview environments where specific AI tools are already baked in. Everyone gets the same setup, and the interviewers get to see how candidates interact with those tools in real time. It’s a smart way to keep things fair, while still reflecting the reality that modern devs do use AI tools on the job. So if you’re wondering how to use AI for an interview without losing control of the process, this one’s worth looking at.
3. The Hybrid Model
One strategy that’s catching on with more mature startups? Split the process. Early rounds are all about raw technical chops, no AI allowed. Then in later rounds, they flip the switch and let candidates bring in tools. This way, they confirm the candidate has solid fundamentals and see how they level up with AI. It’s a great way to balance risk and reward in interviews.
4. The No-AI Zone
Then there are the founders who are like, “Hard pass.” Especially in companies working on deep infra or complex systems stuff, there’s often a strict no-AI policy. These teams care less about whether you can code fast and more about whether you can explain, debug, and architect from scratch. For them, AI tools just aren’t mature enough to handle the kind of thinking they’re hiring for and they’d rather skip the distractions.
Bottom line? There’s no one-size-fits-all answer here, but these strategies show that you can embrace AI in hiring without letting it hijack your whole process. It just depends on what you’re building, who you’re hiring, and how comfortable you are letting AI sit in on the interview.
Framework for Deciding How to Handle AI in Engineering Interviews
If you’re trying to figure out whether to allow, ban, or somewhere-in-between AI usage during interviews, you’re not alone. AI for interviews is here, and it’s not going anywhere. So instead of getting stuck in the “should we or shouldn’t we” debate, here’s a practical framework to help you figure out what actually makes sense for your startup.
1. What Kind of Tech Are You Building?
Your decision starts with the stuff your team is actually shipping.
If you’re solving hard, weird problems—think deep infra, novel algorithms, or areas where AI interviews still fall flat, then limiting AI use probably makes sense. You want to make sure candidates actually understand what they’re building, not just what a prompt spits out.
If you’re building with well-known patterns—like spinning up APIs or cranking out frontend features, then AI tools are already part of the workflow. So letting candidates use AI in interviews might give you a better signal on how they’ll work day-to-day.
2. How’s Your Team Structured?
Your current team setup plays a big role in how much AI reliance is okay.
Small, scrappy teams—especially early-stage, need engineers who can figure stuff out solo. If a candidate leans too heavily on tools during an interview with AI, that might be a red flag for a startup where everyone wears multiple hats.
Bigger, more specialized teams can allow engineers to rely on AI for routine or repetitive tasks. In that case, it makes sense to see how candidates use AI for an interview, especially if they’re being hired for deep focus roles.
3. What Stage Is Your Company At?
Your hiring needs shift as your company grows, and so should your take on AI interviews.
Pre-seed/Seed? You want builders with strong fundamentals who can work without much support—AI or otherwise.
Series A or later? Efficiency, collaboration, and smart tool usage become more valuable. How well a candidate prompts AI or edits a Copilot suggestion might be more important than raw algorithm skill.
Making It Work: A Practical Implementation for Startups
So you’ve picked a stance on AI for interviews. Now, it’s time to actually roll it out without making the process weird or confusing. Here’s how to keep things smooth, clear, and actually useful.
1. Spell It Out (Seriously)
Don’t make candidates guess the rules. Put your AI policy front and center in your job posts, prep guides, and intro emails. Whether you’re all-in on AI, AI-curious, or totally AI-averse, just say it:
- No AI allowed
- AI’s fine, but tell us when you’re using it
- AI encouraged—show us how you use it
- We’ve already added AI tools into the interview environment
Being upfront earns you trust and saves everyone time.
2. If You’re Not Allowing AI, Be Smart About Catching It
If you ban AI and don’t build in ways to spot it, it’ll still sneak in. You don’t need to go full NSA-mode, but here are some lightweight ways to keep things honest:
- Ask them to explain their code out loud. If it’s AI-generated, this gets awkward fast.
- Throw in a twist. Change the problem mid-interview and watch how they adapt.
- Ask “why’d you do it that way?”—a solid test of actual understanding.
- Look for fishy timing. If a candidate is stuck for 10 minutes and then suddenly nails the problem with a perfect solution, that’s a red flag worth digging into.
It’s not foolproof, but it’ll help catch the obvious cases.
3. If You’re Cool With AI, Update How You Score People
Letting candidates use AI tools? Great. Now you’ve got to evaluate the right things. It’s not about whether they can code from memory anymore, it’s about how they work with the tools at their disposal.
Here’s what to look for:
- Are they asking smart prompts?
- Can they tell when AI gives them bad output?
- Do they clean up and adapt AI suggestions or just copy-paste?
- Are they using AI to enhance their thinking or to avoid thinking?
You’re hiring collaborators, not button pushers.
4. If You’re AI-Cautious, Go Deeper on Fundamentals
If your interviews are more old-school and AI-free, then make sure you’re really testing the stuff AI still struggles with, like:
- Can they break down complex problems without any help?
- Do they reason from scratch instead of regurgitating answers?
- Can they handle messy, edge-casey stuff that AI tends to fumble?
- How do they perform when things go off-script?
This is about separating real builders from folks who’ve just memorized the cheat sheet.
At the end of the day, whether your AI interview process is locked-down or wide open, it’s got to reflect how your team actually works. Set expectations, build the right signals into your process, and focus on what really moves the needle for your startup.
The Future of AI in Interviews
As AI tools keep improving and strategies keep evolving, smart startups are starting to test out some new and advanced interview approaches:
- Custom evaluation AI that tracks how candidates use coding assistants like GitHub Copilot or ChatGPT during the interview
- Simulated production incidents where AI tools are intentionally unhelpful to see how candidates handle tough, real-world problems without the crutch
- Paired interviews where candidates work side-by-side with in-house engineers, using shared AI tools to solve problems together
The bottom line is simple: your interviews need to assess whether candidates can deliver real value in your specific work environment, no matter what tools they’re using.
Key Takeaways for Founders and Hiring Managers
- Embrace the new reality: Engineering candidates are using AI in interviews, whether you allow it or not. It’s just how things are now.
- Make it match how you work: Shape your interview process based on how engineers actually do their jobs at your company, AI or no AI.
- Adapt to your stage: Your approach should fit your startup’s size, growth stage, and technical needs.
- Be clear about your policy: Whether you’re okay with AI or not, make sure your stance is crystal clear to candidates.
- Focus on what matters: Evaluate the skills that will actually drive success at your company, whether that’s AI collaboration or first-principles problem-solving.
The real question is no longer whether candidates should use AI in interviews, but how your process can evolve to find the best engineering talent in a world where AI is just another tool in the toolbox.
