The hiring crisis in an AI-driven world

Technical hiring has been tenuous for some time; Leetcode style assessments were always a half-measure. Better than nothing, but hardly a perfect predictor of on-the-job performance.

Fast-forward to today however, and AI has come along to completely shatter this fragile compromise.

As the role and ideal skill profile of the developer rapidly evolve, and the tools available to help candidates game the system multiply, the old technical screening and interviewing model is in acute crisis. And if you’re still relying on algorithm-heavy tests and gameable interfaces that don’t reflect the real world, you’re probably feeling the pressure.

Don’t worry – you’re far from alone.

What’s wrong with today's hiring process?

1. Trust issues are skyrocketing

AI is amazing, but it’s also making it easier for candidates to game the system. Hiring managers report candidates acing assessments with polished answers that can’t be explained when challenged later on - sometimes only realizing that the person has no fundamental understanding of their work weeks after coming aboard.

Just how pervasive is this issue?

In our 2025 Developer Skills Survey, 56% of developers admit to using AI on their coding assessments. This data corroborates with our internal plagiarism detection currently flagging 6 out of 10 candidates for potential plagiarism.

Plagiarism isn’t the only concern. Impersonation is a growing issue too. Without robust identity verification, there’s no guarantee that the person breezing through your coding test is the person you’ll end up hiring. The same technology social media influencers are using to create their AI avatar, developers are using to game their technical interviews.

A simple YouTube search gives you step by step tutorials on how to do so.

Trust in the process has gotten so bad from both hiring managers and developers that 3 out of 4 developers report feeling like they’re at a disadvantage if they don’t use these tools during the interview.

As a result, companies are reverting to high cost solutions like bringing all candidates onsite or paying for live human proctoring. Costs for these services range anywhere from $25-$100 an hour.

Companies that have not broadly invested in modern proctoring tools are feeling the financial burden of integrity issues skyrocketing while trying to bandaid with high cost solutions.

2. Assessments Don’t Reflect Real-Life Workflows 

Ask any software engineer what their day looks like, and it’s not solving algorithmic puzzles on a whiteboard. Engineers rarely solve abstract trees and graphs on the fly; they work within teams and complex systems, using real codebases, and leveraging AI assistants to solve real problems. Yet, most hiring tests evaluate skills in artificial, outdated ways that fail to replicate actual job tasks.

We get it – it’s a lot less daunting to grade and legally defend a multiple choice test than have recruiters evaluate test cases submitted via an IDE. But with AI writing code these days, algorithmic or basic coding skills are literally becoming obsolete, being replaced with higher order thinking tasks.

Imagine you're hiring a taxi driver, and for decades, the gold standard has been testing their ability to memorize every street, turn, and landmark in the city.

But then, GPS comes along. Now, a driver doesn't need tomemorize routes; they need to understand how to use GPS effectively, navigate traffic, and ensure a smooth ride for passengers.

Yet, your hiring process still insists on testing memorization. You reject great drivers because they don’t know every street off the top of their head, even though what really matters now is their ability to use modern tools, make good decisions, and adapt to road conditions.

This isn’t just a thought exercise. It is exactly what’s happening in software hiring today. AI can generate code, just like GPS can provide directions. The value of a developer isn't writing syntax from scratch anymore—it’s understanding architecture, debugging, problem-solving, and knowing how to guide AI to create the right solutions.

The disconnect between the coding interview and a developers actual job is creating a real candidate satisfaction problem with 62% of developers feeling forced to overprepare.

3. AI Skills Go Untested 

97% of developers use AI assistants, and 61% now use two or more AI tools at work. If nearly all developers are using AI, why aren’t companies assessing their AI collaboration abilities?

You wouldn't reject a chef because they use a blender, sous-vide machine, or food processor to speed things up. Instead, you'd test how well they use these tools to enhance their cooking.

Today, the real skill lies in knowing how to prompt and orchestrate AI tools effectively. Most hiring evaluations aren’t built to measure this crucial ability, simply because if you’re not using a real-world IDE platform, the technology to assess these emerging skills hasn’t existed.

You wouldn’t penalize a chef for using modern tools. However, in the same situation, you wouldn't hire a chef solely based on their ability to microwave pre-packaged meals. If all they can do is press buttons on a machine, but they don’t understand flavors, techniques, or food safety, they aren’t really a chef—they’re just a kitchen operator.

Likewise, if a developer only knows how to prompt AI but doesn’t understand software architecture, debugging, or how to validate AI-generated output, they could create messy, insecure, or unmaintainable code.

This is where the importance of standardization comes in. Without clear expectation settings for hiring managers and candidates, one candidate might undergo intense proctoring while another has a casual phone interview.

Because individual hiring manager’s opinions on the use of AI in technical interviews varies so greatly, without company-wide standardization on when and how to bring AI into the hiring process, the lack of consistency creates an uneven (and often unfair) experience for candidates.

The Risk of Not Adapting

Candidates frequently report that current processes are draining and uninspiring. Tests are long, tedious, and irrelevant to the skills they’d actually use on the job. It’s not uncommon for candidates to jump through hoops for weeks and never hear back. Unsurprisingly, great talent opts out, costing companies their best prospects.

The Next Generation of Hiring

The future of hiring isn’t about throwing more people at the problem or sticking to what’s “worked” before. It’s about evolving alongside engineering itself. It is built to be able to quickly find, hire, and upskill AI-first developers.

The good news? There’s a better way. Next-gen hiring methods are here to help businesses like yours solve these challenges.

1. Use Technology to Uphold Integrity

The same technology that skyrocketed integrity issues can affordably solve them. Invest in technology, like HackerRank’s proctor mode which runs discreetly in the background, monitoring candidate activity and flagging behaviors like tab switching, copy-pasting, or unauthorized monitor use. Real-time nudges prevent violations before they occur, saving both time and face.

2. Use Simulations That Mirror the Real World

Tools like real-world code repositories give candidates the chance to solve realistic engineering challenges. These assessments replicate working environments, showing how candidates debug, problem-solve, and integrate changes into live codebases.

Hiring a developer based on a code repository assessment is like testing a pilot in a flight simulator instead of just asking theory questions. A simulator lets pilots experience real-world challenges, like turbulence or engine failures—just as a code repo assessment puts candidates in a real engineering environment.

Code repositories also come with a built-in AI assistant—just like today’s developers use in their daily workflows. This allows hiring managers to evaluate how candidates interact with AI tools, offering a clear picture of their ability to integrate these technologies productively.

When given the chance, developers (especially mid-to-senior levels) would opt for a real-world coding task as their assessment format.

3. Use AI Assistants in Your Process

If there has ever been a win/win for candidates and companies it is the invention of the AI interviewer. For companies, the AI interviewer means that first-round interviews no longer drain your senior engineers’ time. The interviewer, which automates early-stage candidate screenings, is not just a chatbot. This AI evaluates fundamentals through conversational prompts, assessing candidates’ thought processes and decision-making skills—then delivers structured reports with actionable insights.

This frees engineering teams from repetitive interviews, allowing them to focus on later stages and hiring the right person.

There are also major advantages for candidates, especially the not-so-extroverted type. Imagine you're at a grocery store. Sometimes, you just want to get in and out quickly, without small talk or feeling judged. Self-checkout lets you move at your own pace, avoid awkward interactions, and get the same result—buying your groceries.

Similarly, AI interviews can offer a stress-free, unbiased experience, letting candidates focus on their skills without human bias, pressure, or small talk. Just like self-checkout, it’s efficient, consistent, and lets people perform at their best without worrying about external judgments.

Consistency matters. Next-gen systems provide clear guidelines on expectations (e.g., “AI tools are allowed, but explain how you used them to solve X problem”) at every stage of the hiring funnel. Combined with AI proctoring and real-world simulations, this ensures fairness and trust for both candidates and employers.

The Next-Gen Hiring Process

We recommend a standard process of starting with a proctored assessment without the use of AI to measure a candidate’s fundamental skills. After successfully demonstrating their skills without assistance, we recommend moving to an AI Interviewer round to assess their ability to work alongside a copilot. If all goes well, now a live code-pair interview with a developer on your team to gauge soft skills and team fit.

You can imagine how steps 1 and 2 being automated via technology can dramatically speed up your time to hire. No more waiting for calendars, the candidate can complete both rounds at the time that works best for them.

Why It Matters

The impact of adopting next-gen hiring can’t be overstated.

• For candidates—you’ll no longer have to cram for an algorithm assessment
• For hiring managers & engineering leaders—you’ll save time while hiring smarter. 
• For recruiters & talent acquisition pros—you’ll speed up hiring cycles while ensuring fairness and integrity. 
• For CHROs & CTOs—you’ll build a scalable, cost-effective process that prepares your organization for the future of work.