Why Technical Interviews Must Evolve in the AI Era

AI is changing technical hiring by accelerating every stage of the funnel and making it harder to identify genuine software engineering skills from AI-assisted output. Candidates can now easily produce polished resumes and correct interview answers, even if they lack the necessary skills or knowledge. This degrades hiring signals and is forcing companies to rethink how they measure engineering abilities in the AI era. 

Interview loops aren’t adapting fast enough. Organizations are using AI to automate sourcing, screening, coding challenges, and candidate engagement, allowing them to move faster and handle increased hiring needs. This is incredibly important as applications have increased dramatically, but it comes at a cost.

AI also creates signal loss. It makes weak engineers appear strong and strong engineers harder to identify. Enterprise hiring loops get overwhelmed by noise, making it difficult to accurately assess engineering skills at scale. 

For engineering and talent leaders, successful hiring requires adapting to this change. In this post, we’ll explore how AI is changing candidate behavior, where hiring funnels are breaking, and why traditional interviews no longer work. We’ll also look at how Karat NextGen modernizes hiring for the AI era with its human + AI solution.

AI Has Changed the Hiring Funnel From Top to Bottom

AI hasn’t just changed how software engineers work. It has also changed how they interview. Candidates can move from application to the final round much faster by using AI tools that help them create tailored resumes, correct solutions to take-home assessments, and answers to interview questions. 

This impacts every stage of technical hiring across industries, from financial services to enterprise tech. The signals that organizations have historically relied on now carry less reliable information about a candidate’s true abilities. They face more noise and false positives, which has actually made the hiring funnel less efficient at revealing which candidates are most likely to succeed when hired. 

• AI-assisted resume writing inflates top-of-funnel volume by letting candidates apply to dozens of roles with minimal effort. These tools also make resumes look more polished and aligned with the job description, making it harder to identify genuine experience.
• LLMs help candidates generate solutions to take-home tasks, masking true skill. Candidates can now paste entire assignments into AI tools to generate working solutions, refactor code, or explain complex concepts, even if they don’t fully understand the underlying logic. This problem is pervasive, as one tech leader that we spoke to said they suspect more than 80% of their India-based candidates use LLMs on top-of-funnel code tests despite being told not to. 
• AI-enhanced interview prep makes rehearsed answers appear strong. Candidates can use AI to generate responses to common behavioral and technical questions. By memorizing these answers, they appear confident and knowledgeable. 
• LLM-generated code in interviews creates false confidence in weak candidates. During live coding interviews, candidates who lack fundamental understanding can still produce working code by leaning on AI tools. However, when interviewers dig deeper, many of these candidates will struggle to explain what the code is really doing or their problem-solving process.

How AI Is Changing Candidate Behavior (and Why It Matters)

AI has created a world where on the surface, strong and weak candidates can look identical. This is a growing challenge, as we’ve seen a five-fold increase in cheating detection rates over the past two years. Unauthorized assistance is one of the most common types of cheating, and it’s become easier to do with the proliferation of AI tools. When organizations don’t address this behavior, it can destroy their hiring signal. 

Distinguishing between authentic capability and AI-assisted performance is now more critical and difficult, but it can be done when interviews probe deeper to uncover how candidates use AI and how they think.

Strong candidates:

• Use AI to enhance their thinking and communication. They treat AI as a collaborative partner, using it to explore alternative approaches faster or explain complex concepts more clearly. AI increases their productivity and amplifies their skills while strong candidates maintain ownership over problem-solving and decision-making.
• Validate and critique AI suggestions. Rather than accepting AI outputs at face value, strong candidates identify potential issues, consider edge cases, verify logic, and adjust solutions as needed. 
• Stay calm when AI outputs break. When AI generates buggy code or suggests a flawed approach, strong engineers are comfortable adapting and can still work effectively. They debug or change their prompts to get better outputs. 

Weak candidates:

• Rely on AI to generate answers. Instead of using AI as a support tool, they use it to replace their own thinking. They ask AI for complete solutions and copy those outputs directly into their work without understanding the underlying approach or reviewing the output.
• Copy and paste without understanding. They present the correct code or answer, but they’re unable to justify design choices, explain why the solution works, or anticipate potential issues. When AI use is prohibited in interviews, this can also be a sign of cheating.
• Collapse when follow-up questions require independent reasoning. When interviewers probe deeper or ask candidates to adapt their approach, weak candidates struggle. Without AI generating the answer, they lack the knowledge and skills needed to truly succeed.

AI Is Changing the Skills Needed to Be a Strong Software Engineer

As AI becomes integral to engineering work, the skills that matter most are shifting. Writing code and memorizing syntax are less important. Engineers now need to have strong AI literacy, problem-solving, communication, systems design, and debugging skills. 

Interview rubrics that were built for the pre-AI era don’t include these new competencies. Organizations must update their rubrics, or they risk optimizing for skills that no longer predict success. 

Why Traditional Technical Interviews Fail in the AI Era

Traditional technical interviews were designed for a world where candidates didn’t have the help of AI tools. Being able to produce the correct code or answer was a good approximation of a candidate’s ability to perform on the job. That’s no longer true for several reasons:

• Coding tasks focused on output no longer work because AI can generate correct code instantly. Candidates can then submit the AI-generated code without needing to understand the underlying logic. 
• Predictable, single-file problems do not reveal reasoning, architectural thinking, adaptability, or debugging ability. This is because they don’t mirror the characteristics of production systems. Real engineering work involves navigating unfamiliar systems, managing dependencies, and working with complex codebases. 
• Automated coding tests can’t distinguish AI output from genuine understanding. They can verify that the code works, but they can’t assess the thinking behind it. There’s no way to tell whether the candidate wrote the code themselves or copy-pasted AI-generated solutions. 

The result is companies experience more false positives (AI-dependent candidates) and false negatives (true thinkers who don’t “game” legacy loops). Our data shows that tech leaders feel it’s now more difficult to measure a candidate’s underlying skills and they are less confident that qualified candidates are the ones getting the job offers. There are also far-reaching consequences, as mishires can lead to declining engineering velocity and low-quality work. 

Why NextGen Is Purpose-Built for Enterprise Tech Hiring

Karat NextGen is the first human-led, AI-enabled talent solution for identifying software engineering talent in the AI era. “Real breakthroughs happen when human judgment and AI capabilities work together. What’s been missing is a way to measure that combination reliably. A human-led, AI-native interview is exactly the kind of solution organizations need to understand who can truly excel in this new model of development,” said Sagnik Nandy, CTO of Docusign. 

NextGen addresses the shortcomings of traditional technical interviews by mirroring real engineering environments and using human-led interviews to reveal whether candidates are AI-ready and have the foundational engineering skills. 

• A real-world environment reveals true capability. NextGen reflects real engineering conditions in large organizations by giving candidates a multi-file codebase, VS Code-based IDE, and a built-in AI assistant. Candidates need to complete multi-file, multi-step tasks that simulate real systems. This exposes their systems-level problem-solving skills and their ability to work with modern, AI-enabled workflows. 
• The AI assistant becomes the differentiator, as interviewers are able to see how candidates use AI. This makes it easy to distinguish between strong and weak engineers. Strong candidates treat AI outputs as starting points. They validate and refine the code, and they’re able to explain why they’re accepting or modifying what AI produces. Weak candidates accept AI output blindly. They can’t explain the reasoning behind it and struggle when asked to defend or adapt the solution. 
• Human-led probing uncovers authentic reasoning. Karat’s expert, certified interviewers lead the interview, actively engaging with candidates throughout and asking follow-up questions to detect shallow thinking. These questions reveal a candidate’s depth of skills, how they handle complexity under pressure, and how well they understand the problem and solution.
• An AI-era rubric measures what truly matters. NextGen uses a rubric designed to assess the skills that are critical for success today. This includes AI literacy, codebase navigation, debugging ability, requirements gathering, architecture reasoning, and communication and collaboration. The rubric produces objective data that you can trust to help you make an informed hiring decision. 

Why Humans Are More Critical in the AI Era

AI doesn’t replace the need for humans in the hiring process. In fact, the role of human judgment becomes more important for several reasons: 

• AI cannot reliably measure candidate reasoning, especially when candidates use AI tools. While AI can check if the code works, it can’t assess the quality of a candidate’s decision-making, the depth of their understanding, or the sophistication of their problem-solving. 
• AI evaluating AI creates a dangerous feedback loop. When AI tools assess AI-generated work, the system optimizes for producing outputs that other AI recognizes as “good.” This creates a self-reinforcing cycle that lowers the hiring bar over time and rewards specific patterns.   
• Candidates expect human-led conversations for fairness and brand perception. Hiring is a two-way evaluation. Candidates judge companies by their interview process, and fully automated assessments can signal that an organization doesn’t value human judgment or candidate experience. This damages your employer brand and drives top talent away.
• Human interviewers provide nuance, adaptability, and judgment, which no AI can replicate. These qualities allow humans to make holistic assessments, and they’re critical for making decisions about cultural fit, a candidate’s potential, and how a candidate will influence the broader engineering organization. 

For tech leaders, keeping human judgment at the center of the hiring process is also important because it helps them avoid hiring mistakes that create code debt, protect engineering velocity, and ensure fairness without sacrificing speed. By combining AI-enabled assessments with expert human interviewers, they can maintain a fast, efficient hiring funnel while making confident hiring decisions that lead to high-performing engineering teams. 

The Complete Loop: How NextGen Works With Karat Core

Karat’s Core and NextGen solutions create the most comprehensive, modern technical interview loop. 

• Core evaluates foundational coding and problem-solving skills such as algorithmic thinking, coding proficiency, and engineering fundamentals. It assesses candidates against a baseline standard of technical competence to identify engineers who meet your hiring bar.  
• NextGen evaluates real-world, AI-enabled workflows and how candidates perform in those environments. By recreating how software engineers actually work in an AI-enabled world, NextGen surfaces candidates who have modern engineering skills. 

What Hiring Teams Must Evolve Right Now

Adapting to AI is a must for any organization that wants to maintain a high-performing, productive engineering team with relevant skills. Yet our data shows that only 30% of organizations ranked “updating technical assessments to assess for AI” as a top priority. 

To help you start taking action today, here the key steps that hiring teams should take to evolve their hiring for the AI era:

• Update rubrics to include AI-era competencies. Before AI, evaluation criteria focused on syntax, algorithms, and data structures. These competencies are now less important, and interview rubrics must reflect that. Rubrics should be built around the skills that matter most today, such as problem-solving, communication, systems design, and the ability to work with AI. 
• Train interviewers to evaluate AI-assisted problem solving. Interviewers need guidance on how to probe AI usage. For example, questioning techniques that uncover a candidate’s understanding and how to distinguish between a candidate using AI as a tool versus overly relying on it. 
• Rewrite job descriptions with AI workflows in mind. Job postings should acknowledge that engineers will work with AI tools. This signals that your engineering culture embraces new technologies and adapts as software engineering evolves. It also helps you attract engineers who are comfortable working with AI. 
• Replace single-file tasks with real-world codebase navigation. Move beyond isolated coding challenges to assessments that require understanding existing systems, navigating multi-file codebases, and making decisions that consider broader architectural implications. This better reflects day-to-day work and reveals how candidates handle real challenges that they’ll face on the job. 
• Align fairness frameworks around AI usage during interviews. Decide how AI can be used in interviews, establish clear policies, and then apply those policies consistently. Both candidates and interviewers should know what’s allowed. Otherwise, ambiguity creates an uneven playing field. 

Most importantly, hiring excellence now requires continuous R&D. Once you’ve taken the steps above, this doesn’t mean that you’re done.

Organizations need to treat talent measurement as a continuous system that evolves alongside AI advancements. AI capabilities, tooling, and engineering best practices will continue to change rapidly, which means interview content, evaluation criteria, and interviewer training must be regularly updated to stay aligned with how work is actually being done. 

The Road Ahead: AI-Driven Skills That Will Redefine Hiring Again

The changes that we’ve outlined are just the beginning of how hiring is transforming. New skills are growing in demand while new assessment methods are being created to keep hiring aligned with real engineering work. 

• Agentic AI: Engineers will increasingly manage and orchestrate AI agents that can autonomously execute tasks. They’ll essentially act as AI agent managers, which requires a different set of skills. Engineers will need to set expectations, provide clear direction, and make sure each task contributes to the desired goal. 
• Orchestration workflows: As systems become more complex, engineers need to coordinate multiple AI services, models, and tools while ensuring reliable integration across platforms. Interviews will have to adapt to measure the ability to architect and maintain all these components.
• “Build-an-app” multi-step assessments or multi-step application-building scenarios: Rather than isolated coding tasks, future assessments will involve building complete features or applications. This mimics what software engineers do on the job and reveals how candidates handle ambiguity and iterate based on feedback. 
• Human + AI hybrid evaluation models: The most effective hiring processes will combine AI-powered screening and human judgment for fair, efficient assessment. For example, they may include automated resume screening to accelerate shortlisting or predictive hiring analytics to identify candidates with the most potential and retention likelihood. However, hiring teams will need to figure out the right balance between automated and human evaluation. 
• Continuous evolution to stay aligned with real engineering work: As AI capabilities and engineering practices keep changing, static assessments will become ineffective. Organizations that continuously test, learn, and update their interview process will be best positioned to identify top talent. 

Karat NextGen is built to evolve with AI, keeping customers ahead of every wave. NextGen automatically incorporates new technologies and trends to keep your talent evaluation processes up to date. This means your organization can focus on building great teams while Karat handles the ongoing R&D required to maintain a world-class, future-proof hiring process. 

AI Has Changed Engineering — Now It Must Change Hiring

AI does not simplify hiring; it exposes weaknesses in outdated processes. Traditional interview formats that lack human involvement or focus on isolated outputs no longer generate an accurate hiring signal. At the same time, AI-assisted answers create noise that makes it difficult to distinguish between human and AI ability. 

It’s more important than ever for companies to have a way to evaluate real engineering ability — not just polished AI output. 

NextGen cuts through the noise and produces a reliable hiring signal by making the invisible visible. Every interview is a hands-on session led by a human interviewer. Candidates use realistic dev environments to solve realistic, complex scenarios. This reveals how candidates truly think, collaborate with AI, and will operate on the job.

Book a NextGen walkthrough today to see how it can help you build a high-quality, AI-ready engineering team.