When we opened our latest software engineer role, we had 100 applications within 24 hours. By the end of the first week, without running a single ad, we had 160 candidates in the pipeline.

That’s a lot of signal to sort through. And it forced us to be honest about something every engineering leader eventually confronts: a hiring process is only as good as its ability to protect your team’s time while treating candidates fairly.

We’re CoderPad. We build technical hiring software. So yes, we use our own product. Here’s what that process actually looks like.

Start with the right goal at the top of the funnel

Maxime Cheramy, our Director of Engineering, was direct about it: “The goal is simply not to waste time with candidates who cannot produce anything.”

Not “find the best developer.” That comes later. To start, we focus on filtering out candidates who aren’t ready, without filtering out candidates who are just stressed. Those are different problems, and conflating them leads to a screen assessment that’s too hard, that maybe you’re proud of, but that your ideal hire fails on a bad day.

The screen exists to protect your live interview capacity. Save your judgment about “great” for when the humans in the interview together.

Design for layers in the process, not one knockout question

Max’s screen has five layers, each with a specific job:

1. Easy multiple choice only. If a candidate fails easy questions, that’s signal. If they pass a hard one, you don’t know if they knew it or asked an AI. At the screening stage, easy questions have more diagnostic value.
2. Simple coding exercises, like SQL. Quick, low-friction, still revealing.
3. Two CoderPad Projects. Job-realistic work: build a backend API, work within a real codebase structure. Projects are where candidates demonstrate they can actually ship something, not just solve a puzzle. CoderPad’s Projects feature lets you build these assessments and review the work in a shared environment, including replaying how a candidate approached the problem.
4. A video question. Not a behavioral prompt about “a time you showed leadership.” Candidates are asked to explain something they just built in the assessment. It’s specific, it’s fresh, and it’s much harder to prep a generic answer for.
5. A puzzle element. Some signal about how someone handles ambiguity is still worth having.

Don’t test for surprise – Tell candidates what’s on the test

Candidates are told in advance what to expect: React, SQL, a project, a video question. This is intentional.

If someone fails after knowing what’s coming, that’s a bad sign. If they prepare and pass, that’s fine. You’re not testing surprise. Amazon sends detailed prep documents to candidates, including the questions, which is a good example. Preparation is a proxy for how someone approaches real work.

Connect the screen to the live interview

The live technical interview includes a brief debrief on the screen assessment. Not a deep review, but enough to see how candidates explain their own choices and whether their verbal technical reasoning holds up. CoderPad’s playback lets interviewers review how a candidate worked through a problem before the live round, so you’re not starting cold.

Max still asks non-technical questions in the technical interview, because it’s often the only real conversation before a decision gets made. Specifically: are you more of a builder, someone who cares about what gets made and why, or a craftsperson, someone who cares about how? Neither is wrong. With a smaller team, that distinction matters as we have different needs at different times.

The AI question

Every technical hiring team is sitting with the same problem: how do you evaluate candidates when AI can write production-level code?

Max’s current answer: easy multiple choice is more valuable now, not less. Project questions shift from “can they write this” to “do they understand what they built.” Video questions create accountability to the work product even if AI assisted. Live interviews let you probe in ways that reveal whether the candidate understands their own output. We test for AI at every step.

The goal isn’t to catch people using AI. It’s to design an assessment where AI assistance doesn’t mask the signal you’re actually after.

What we’re learning by doing this

160 candidates in a week is a real volume. Working through it with our own product keeps us honest about where the friction is and what it feels like to be on the other side of what we build.

If you’re designing a technical screen and starting from “what’s the hardest problem I can give them,” try starting from “what’s the minimum I need to see to protect my team’s time?” That question usually gets you somewhere better.

If you’re running a process, think of different types of questions to understand the skill level you need. Be focused on job-specific questions over puzzles – unless your company actually builds puzzles.

Overall Lessons

• Dogfooding works best when you’re designing a real process under real constraints, not a demo. Max had 160 candidates and a hiring need — that pressure produced better product insight than any internal exercise would have
• The test is a means to an end. The goal is to protect the live interview stage for candidates who are worth the time, not to find the hire through the screen alone
• Question design can’t be separated from process design. Picking questions in isolation — or looking for one golden question — misses the point. Each stage has a job to do, and the questions should serve that
• How you frame a question changes what you learn. The video question works because it asks candidates to reflect on work they just did, not perform under abstract pressure. Small framing decisions have a big impact on signal quality
• Preparation transparency is a feature, not a risk. Letting candidates know what’s coming filters for the right things and produces more authentic responses. Surprise is not the point
• With AI in the picture, the profile of a strong hire is shifting. Deep technical experts and strong builders are both valuable. Generalists who are neither are harder to place on a team that’s moving fast with AI assistance

We spoke with Farhan Thawar, VP and Head of Engineering at Shopify, about why the company 10x’d its internship program, what AI-native hiring really looks like, and the three-part framework reshaping how technical talent gets evaluated.

The conventional wisdom goes something like this: AI is coming for developers, and the first casualties will be the junior engineers — the interns, the new grads, the people who used to spend their days writing boilerplate and fixing typos in documentation. Why hire a cohort of entry-level engineers when a model can do their work in seconds?

Shopify isn’t buying it.

Last year, Shopify didn’t quietly trim its internship program. It exploded it — growing from roughly 100 interns a year to over 1,000. This year, they’ll continue the program with plans to hire 1,000 interns. We spoke with Thawar to understand why, what it tells us about the future of technical hiring, and what early-career engineers can do right now to stand out.

Why +10x the internship program — and why now?

Shopify has always run an internship program. The philosophy hasn’t changed: bring in next-generation talent, keep the company thinking with fresh eyes, and make sure there are always people in the building willing to ask “why do we do it this way?”

But the scale shift is new, and AI is part of the reason. Thawar draws a direct parallel to the mobile era: when smartphones took over, Shopify deliberately hired people who had grown up on mobile — people for whom the phone-first mental model was instinctive, not learned.

“These folks coming out of schools now are growing up with AI,” Thawar says. “They are in school with AI the entire way through. In many ways, they’re AI native. We wanted to bring those types of people in to reimagine what it looks like to build knowing they’ve grown up with AI.”

There’s also a cultural dimension. Interns bring intensity, curiosity, and energy — and a cohort of 350 spreads that energy far more effectively than 25 ever could. “Twenty-five engineers will have a harder time impacting us than 350,” Thawar notes. The interns are distributed across teams, not siloed into a single program track.

And practically: Shopify converts a significant number of interns into full-time hires. The four-month internship functions, in Thawar’s words, as a “two-way interview” — the company evaluates how candidates think about problem-solving, how they wield AI tools, and how they operate under real conditions. The interns get a genuine sense of Shopify’s engineering culture and the resources available to them.

There’s one more benefit Thawar didn’t expect to become a talking point: the energy in the office. Shopify is a remote-first company that gathers intentionally rather than requiring daily attendance — but interns come in every day. “You’re at lunch and you see all these amazing interns around and you can pick their brain,” Thawar says. Leadership teams flying in for company gatherings now overlap with a room full of curious, energetic early-career engineers. That collision has shaped Shopify’s in-person culture in ways the team didn’t fully anticipate.

What does ‘AI-native engineer’ actually mean at Shopify?

Some companies have responded to the AI moment by rebranding their engineers. “AI engineer” is showing up on job descriptions everywhere. Shopify doesn’t do this — and the reason is telling.

“For us, it’s implicit,” Thawar explains. “If I call some people AI engineers, then other people will be like, ‘does that mean the finance person is not an AI finance person?’ Everyone should be using this tool.”

The expectation at Shopify is that every engineer understands how to build software — and that they also understand when to use AI, when to trust it, when it’ll give them a novel approach, and how to validate its output. Critically, whoever submits a pull request owns that code.

This isn’t about limiting AI use — it’s about not outsourcing judgment. Shipping fast matters. So does shipping right.

What can engineers do now that they couldn’t do two years ago?

Thawar gets enthusiastic about this one. Three things stand out in his answer.

First: managers can code again. “The ramp-up is so much lower now. I might have 15 minutes between meetings and I can pull up Claude Code or Cursor and quickly build a prototype, or ask it questions about the existing codebase.” In the past, meaningful code contributions required blocked-off time — a full day, a sprint week, a dedicated “no meetings” stretch. AI pair programming has shrunk that activation energy to nearly zero, enabling managers, directors, VPs — and at Shopify, the CEO — to contribute working code regularly.

Second: the work you always wanted to do but kept putting off. Writing unit tests. Refactoring gnarly legacy code. Wholesale rewrites of the modules that everyone quietly dreaded. “You can have a conversation with your agent: let’s reimagine this domain. How would you approach it? Then say: let’s code that — and actually look at the prototype.” That was never accessible before because the activation energy was too high. Now it is.

Third: ambition has no excuse. “In the past it would’ve taken me weeks and weeks to learn a new tool chain. With AI, you can just go after it.” Nine out of ten prototypes might go nowhere — but one might hit, and now the barrier to finding out is almost zero. Engineers and non-engineers alike can pursue the feature they always thought was missing.

Shopify’s hack days have transformed as a result. “It’s more likely now that you’re going to actually build something,” Thawar notes. “People used to spend hack days learning how to use data at Shopify or how to use a tool. Now it’s: I’m going to learn it and build something I can at least demo.”

How Shopify evaluates technical talent in the AI era

Here’s where Thawar gets refreshingly candid: “I have an answer I don’t love. The honest answer is: we don’t know. No one has really figured out yet what it means to evaluate the next generation of technical talent with these tools.”

What they do have is a framework — one Thawar borrowed from the University of Waterloo — that’s starting to shape how Shopify thinks about interviews. It has three modes:

No AI allowed. Can you write code by hand? Do you understand what’s happening at the layer below where you’re working? This is how software was done just a few years ago, and Shopify still wants to know you can operate there.

AI optional. Do you know when to use it? Can you make a judgment call about whether to pull in the AI or push further on your own thinking first? This is about discernment, not just capability.

AI mandatory. The project is too big for the time available. The candidate has to wield the tool effectively — scope, prompt, validate, ship. Imagine being asked to build a full Twitter client in an hour. With AI, you can get surprisingly far. Without it, you can’t.

The framework is appealing but imperfect, and Thawar is the first to admit it. There may be a new category of developer — people who don’t deeply read the underlying code but can build extraordinary things through an agentic loop they’ve mastered. Whether that style of work should pass a traditional evaluation is an open question. So is the more fundamental one: for candidates who score well on these techniques, do they actually perform well on the job afterward? “I don’t think anyone has cracked that in any part of the software industry yet.”

What early-career engineers should do right now

Thawar’s advice for students and early-career candidates hasn’t changed in 30 years of working in the industry — but AI has removed the last remaining excuses for not following it.

Build tools for yourself. Build tools for people you know — Thawar built an options-trading algorithm for his 84-year-old father (who declined to use it, preferring paper and pen, but that’s beside the point). Put it on GitHub. See if other people need it. Contribute to open source — find issues, try to fix them. Build against Shopify’s API if you want to work at Shopify.

“There is no shortage of work to do in the world. Start working on it. You don’t need a job to do that.”

The portfolio matters more than the resume. As Thawar’s former CTO observed: a resume tells you what someone did, but never why. Working software tells you much more — and for intern evaluations, the ultimate signal is real impact. An intern who deleted six lines of code and saved Shopify $600,000 in infrastructure costs didn’t show up in any activity metric week to week. But the impact was undeniable.

What this means for technical hiring broadly

The dominant narrative around AI and developer jobs runs something like this: AI will automate the routine work, and the routine work is what entry-level engineers do. Ergo, fewer entry-level engineers.

Shopify’s internship expansion represents a direct challenge to that story. Thawar’s bet isn’t that AI makes junior developers redundant — it’s that AI makes developers who grew up with AI more valuable than ever. The people who need the least convincing to wield these tools, who find the mental model natural rather than foreign, who ask “why are we doing it this way?” with genuine curiosity rather than learned skepticism.

That’s a different kind of talent signal. And if the University of Waterloo’s three-part framework — no AI, AI optional, AI mandatory — becomes an industry standard for evaluating technical candidates, platforms like CoderPad are positioned to surface exactly that signal: not just whether a candidate can code, but whether they know when to reach for the tool, when to set it down, and what to do when it’s gone.

The developers who figure that out first won’t be the last to be hired. They’ll be the first.

AI interviewers are having a moment. Every vendor is selling the same dream: automated conversations, instant scoring, no more phone screens. Faster. Cheaper. Scalable.

And if you’re a TA leader staring down a pipeline of 2,400 applicants for 12 open engineering roles, that pitch lands.

Here’s the problem nobody’s saying out loud: AI interviewers aren’t showing you skills, and now candidates are using AI to answer your AI interviewer so you’re not evaluating engineers anymore. You’re running a tournament for chatbots. The best liar wins.

That’s not a hiring process. That’s theater.

The automation you actually need isn’t the automation they’re selling

When TA leaders say they want to automate screening, they mean they want to stop spending time on candidates who can’t do the job. They want faster signal on real ability.

AI interviewers give you something else entirely: a faster way to collect answers to scripted questions. That’s not the same thing. Not even close.

Scripted questions are the easiest part of hiring to fake. Always have been. The difference now is that candidates don’t even have to try. Tools built specifically to feed real-time answers to AI interviewers are widely used and openly advertised. 20% of candidates admit to using AI secretly in interviews. The actual number is almost certainly higher.

The upfront screen hasn’t gotten harder to game. It’s gotten easier. You just can’t see it happening.

You’re paying for noise, not signal

Here’s what an AI interviewer actually evaluates: how well a candidate performs a scripted conversation with a bot.

Not how they code. Not how they think. Not how they communicate when the problem is harder than they expected. Not whether they can actually do the work you’re hiring them to do.

You’re spending money to collect data that has almost no relationship to job performance. And you’re doing it at scale, which means you’re making more bad decisions faster.

That’s not automation. That’s automation-shaped noise.

Your employer brand is taking a hit you’re not measuring

62% of technical candidates prefer a structured assessment over an AI interview.

Highly skilled engineers don’t need your job. They’re fielding multiple offers. The way you screen them is the first real signal they get about your engineering culture. A bot interview says: we care about throughput, not your time. We built this process for us, not for you.

Strong candidates opt out. They have the luxury of doing that. The ones who don’t are the ones you end up with.

The companies winning engineering talent right now aren’t just moving fast. They’re running a process that respects what candidates are being asked to do. That gap compounds over time.

What good screening automation actually looks like

The goal was never to automate the interview. The goal was to protect your engineers’ time while still getting real signal on real ability.

CoderPad Screen does that by automating everything except the judgment call.

Candidates complete an actual take-home project in a real multi-file IDE — the same tools they’d use on the job. Then they record a video walkthrough explaining their own work. Not a scripted answer. Their own code, in their own words.

Your team reviews async: code output, video, session replay showing exactly how they worked, what AI they used, where they got stuck, and how they recovered. Scored against shared rubrics. Shareable with hiring managers in one click.

No scheduling. No phone screens. No waiting on engineering to weigh in before the process can move.

The outcome: 90% faster than traditional phone screens. 70% lower cost per hire. And engineers who reach the live interview have already proven they can do the work.

The live interview becomes a conversation, not a gamble.

The honest question

Every AI interviewer vendor will show you metrics on speed and cost. Ask them one question they won’t have a clean answer to: what are you actually measuring?

If the answer is conversational performance with a bot, you already know what that’s worth.

The best TA leaders aren’t asking how to automate more of the process. They’re asking how to get better signal faster without burning their team or their candidates. Those are different questions with different answers.

One of them leads somewhere.

See how CoderPad Screen works. Candidate experience, reviewer workflow, ATS integration — 20 minutes, no migration required.

Your latest candidate looks like a slam dunk.

Their resume matches the job description perfectly. Their LinkedIn is polished, complete, and compelling. Every skill checks out. And yet, more often than teams want to admit, that “perfect” candidate doesn’t hold up in a real conversation.

What’s happening in hiring today isn’t just resume inflation. It’s a more advanced, harder to detect form of misrepresentation driven by AI, proxies, and increasingly polished candidate narratives. From “AI amplified exaggeration” to candidates being actively assisted during interviews, the top of the funnel is getting noisier, and traditional signals are becoming less reliable.

The Real Problem Isn’t AI, It’s How We’re Using It

It’s easy to look at all of this and jump to the wrong conclusion.

AI is ruining hiring. Candidates are cheating. Interviews are broken.

But that framing misses the bigger opportunity.

AI isn’t going away, and more importantly, it shouldn’t. The way candidates use AI today is a preview of how they’ll work on the job tomorrow. If anything, banning or fearing AI in the interview process creates a disconnect between how you hire and how work actually gets done.

At CoderPad, the belief is simple: AI isn’t the problem to solve for, it’s the environment to hire for.

Stop Fighting AI. Start Hiring for It.

The strongest hiring teams aren’t trying to eliminate AI from the process. They’re designing interviews that make AI usage visible, intentional, and evaluative.

Instead of asking, “Did this candidate use AI?” the better question becomes, “How did they use it?”

Did they:

• Rely on it blindly, without understanding the output
• Use it as a shortcut to avoid thinking
• Or use it as a tool to accelerate, validate, and improve their work

That distinction matters far more than whether AI was involved at all.

Because in real roles, especially in engineering, AI is already part of the workflow. The candidates who know how to use it effectively, while still demonstrating judgment and ownership, are the ones who will perform.

Redefining What a “Good” Interview Looks Like

This shift requires rethinking the structure of interviews.

If your process is built around static questions with predictable answers, then yes, AI will outperform your signal. But if your interviews are grounded in real work, collaboration, and decision making, AI becomes just another variable in the conversation.

This is where approaches like live coding, pair programming, and open-ended problem solving become critical. When candidates are asked to think out loud, explain tradeoffs, and adapt in real time, you’re no longer evaluating whether they can produce an answer. You’re evaluating how they arrive at one.

And that’s where the real signal lives.

CoderPad’s approach leans directly into this reality. By creating environments that mirror how engineers actually work, including the use of AI, teams can see not just what candidates know, but how they operate. It turns the interview from a test into a simulation.

If you want to explore how this works in practice, CoderPad outlines it clearly here:
https://coderpad.io/use-case/ai-enabled-hiring/

Final Takeaway: This Is a Hiring Evolution, Not a Crisis

Yes, candidate misrepresentation is increasing. Yes, AI has made it easier to game traditional hiring processes.

But this isn’t the end of effective hiring. It’s a forcing function. It’s pushing teams to move beyond surface-level evaluation and toward something better, interviews that reflect real work, real tools, and real decision making.

The goal isn’t to remove AI from the process. It’s to understand it, incorporate it, and use it as a lens to evaluate how candidates will actually perform on the job.

Because the companies that win in this next phase won’t be the ones trying to control the environment. They’ll be the ones who evolve with it.

Want to go deeper?
Watch the full webinar here to hear directly from talent leaders at Cedar, Zip, and CoderPad on how they’re navigating candidate misrepresentation and adapting their hiring strategies.

Ready to pressure test your own process?
Book a technical hiring audit here with the CoderPad team to identify gaps in your current interviews, uncover where AI may be distorting your signal, and walk away with a clear plan to improve how you evaluate real-world skills.

High volume hiring sounds like a good problem to have, right?

The reality is, when technical recruiting teams are flooded with applications, resumes become harder to trust, recruiters have less time to evaluate candidates effectively, and engineering teams end up spending valuable interview time with people who are not actually qualified for the role. The challenge is not just the number of applicants. It is the difficulty of identifying real technical ability early enough in the process to protect time, improve hiring quality, and move faster.

That was the focus of a recent CoderPad webinar on high volume technical hiring, where CoderPad’s Senior Manager of Product Marketing, Bayo Ojuri, and VP of Engineering, Nathan Sutter, were joined by Jay Balasubramaniam, Talent Acquisition Manager from Netskope to discuss what breaks in traditional screening processes, and what hiring teams can do differently.

The real problem is not volume, it is low signal

At CoderPad, teams often define high volume hiring as anything above 150 applicants per role. Once you cross that threshold, manual processes start to break down and screening becomes much harder to manage consistently.

The truth is that regardless of volume, there are a lot of factors that can create a noisy screening process that makes it hard to get good signal and identify true skill.

That noise can take a few different forms. Some candidates have polished, AI assisted resumes that look stronger than their actual technical skills. Some assessments produce inflated scores that do not correlate with on the job performance. And some hiring teams rely on early stage screens that are too generic to reveal whether a candidate can actually succeed in the role.

As Nathan Sutter put it during the webinar, “The most expensive failure is passing unqualified candidates into engineering interviews.” Once an engineer is spending an hour evaluating someone who should have been filtered out earlier, the cost of poor screening becomes very real.

What Netskope learned

For Netskope, these challenges were very real.

Hiring at scale in a large market meant managing a constant influx of candidates. The biggest shift came from introducing a more structured screening process that surfaced stronger technical signal earlier.

According to Jay, CoderPad helped simplify their hiring process, move qualified candidates faster, and reduce the burden on engineers.

He also emphasized candidate experience. A tool that is intuitive and realistic helps candidates perform better and feel more comfortable, which ultimately leads to better outcomes on both sides.

Resume noise is getting worse

One of the top challenges identified in the webinar poll was resume noise.

With LLMs, candidates can now tailor resumes to job descriptions in seconds. That means what looks strong on paper doesn’t always reflect real skill.

This makes early technical validation more important than ever. Resumes should be one input, not the primary source of truth.

What better screening looks like

The solution isn’t more screening, it’s smarter screening.

The best high volume hiring processes introduce structured technical signal before live interviews. That requires close collaboration between recruiting and engineering to design assessments that reflect real job requirements.

Strong processes also rely on standardized evaluation criteria so teams can compare candidates consistently.

Most importantly, assessments should mirror real work. Candidates should use familiar tools and solve problems that resemble what they’d actually do on the job. That’s what creates meaningful signal.

Realistic assessments are now essential

AI has made traditional “puzzle” style questions far less effective, especially in asynchronous formats. If a candidate can generate an answer instantly using an LLM, the assessment no longer differentiates skill.

Project-based, real-world assessments are much harder to game and more relevant for candidates. They not only improve signal, but also create a more fair and engaging experience.

The tradeoff is worth it

Better screening requires more upfront investment, especially from engineering teams. But it pays off quickly.

Teams see fewer wasted interviews, stronger candidates deeper in the funnel, faster hiring cycles, and lower cost per hire. More importantly, they build a process that can scale with growing demand.

The goal isn’t to add complexity. It’s to make the process more intelligent.

Final takeaway

If your team is dealing with too many applicants, inconsistent evaluations, or overloaded engineers, the answer isn’t more filtering.

It’s better test design.

The strongest hiring processes combine realistic assessments, consistent evaluation, and early collaboration between recruiting and engineering. They create better experiences, better efficiency, and better hiring outcomes.

And in a world where volume is increasing and signal is harder to find, that’s what makes the difference.

Generative AI hasn’t killed technical hiring. It just exposed which parts of it were already broken.

Coding screens that test syntax recall and algorithm trivia? Those were always a weak signal. Now they’re basically noise. A well-prompted model breezes through them. If that’s still your bar, you’re filtering for the wrong thing.

But here’s what hasn’t changed: great engineers still think differently. They reason through ambiguity. They make trade-offs under constraints. They know when to build and when to borrow. No AI tool does that for you and no interview format exposes it better than a well-run system design session or a realistic coding assessment that mirrors actual work.

The keyword is realistic. A realistic, take-home project that asks candidates to implement a feature, debug a gnarly issue, or extend an existing codebase? That works. Those tasks require judgment, not just syntax. The formats that are dying are the ones AI can short-circuit without breaking a sweat.

Where the real signal lives now

System design interviews have become one of the most defensible tools in the kit because they’re genuinely hard to fake. When you ask someone to design a messaging system or a rate limiter, you’re watching how they think: do they clarify before they build? Can they size a problem? Do they understand failure modes, not just happy paths?

The best interviewers are also evolving the format itself. Ask a candidate to design the system, then build the most critical component in the same session with AI tools available. Suddenly you’re not just seeing the diagram. You’re seeing whether the design was real, whether they can direct AI purposefully, and whether they catch it when it goes wrong. That’s almost exactly what senior engineering looks like in 2026.

We put together a full best practices guide on how to run system design interviews well, the five-phase framework, how to write prompts that generate real signal, an honest look at whiteboard vs. structured platform trade-offs, and a complete playbook for the design-to-build format.

If your interview process hasn’t been stress-tested against what AI can now do, this is a good place to start.

To answer that, we surveyed 650+ developers, recruiters, and hiring leaders worldwide about how AI is influencing work, skill demand, and hiring practices. The result is our State of Tech Hiring Report 2026 — the most comprehensive look yet at how AI is reshaping the developer workforce and the way companies find, assess, and hire technical talent.

Below are the key insights from this year’s research, and what they mean for your hiring strategy.

1. AI Isn’t Slowing Hiring, It’s Changing It

One of the biggest misconceptions about AI is that it’s reducing the need for technical talent. Our data tells a different story:

• Technical assessments are up 48% globally compared to mid-2023.
• In the U.S., technical hiring activity is up 90%.

Far from slowing down, companies are investing more effort into hiring engineers — especially those who can thrive in an AI-augmented workflow. This means demand is rising not just for engineers who can write code, but for engineers who can think, debug, and solve problems creatively with AI as a partner.

Bottom line: The market still needs developers — and more than ever, it needs the right kind of developers.

2. Developers Depend on AI, But Confidence Isn’t Quite There Yet

AI tools like GitHub Copilot, ChatGPT, and other generative assistants have become ubiquitous:

• 82% of developers say GenAI is useful in their work.
• More than half (54%) say their productivity would drop by at least 10% if they lost access to AI tools.

But adoption hasn’t eliminated uncertainty. Many developers feel less secure about their future roles even as budgets rebound. This paradox — of increased reliance on AI paired with lingering insecurity — is shaping how teams hire, retain, and support talent.

In this context, hiring teams must understand not just what tools developers use, but how they use them. Raw output alone is no longer a sufficient signal of skill.

3. Hiring Leaders Are Redefining What “Real Skill” Looks Like

The introduction of AI into the coding workflow has ignited debates about assessment design:

• Some teams ban AI during interviews.
• Others permit it with constraints.
• Still others make decisions case by case.

There’s no universal approach — but there is a clear trend toward assessments that reflect real work. Successful teams are moving away from isolated algorithm puzzles and toward scenarios that mirror day-to-day engineering tasks. These include:

• Debugging AI-generated code
• Explaining trade-offs and system design decisions
• Iterating on and improving AI output collaboratively

These types of assessments give hiring teams a clearer view of how a candidate thinks, communicates, and solves real problems — even when AI is part of the process.

4. Hiring Priorities Are Shifting

When asked about hiring goals in 2026, talent leaders were clear:

• 60% say improving the quality of hire is their top priority.
• 53% expect their hiring budgets to increase this year — the highest level in years.
• Early-career hiring isn’t shrinking — in fact, 28% of teams are prioritizing pipeline growth.

This shift shows that teams are not merely chasing volume. They’re investing in better signals, more thoughtful interviews, and onboarding processes that reflect the realities of modern engineering.

What Talent Teams Should Do Differently in 2026

The takeaway from this year’s research isn’t that teams need to hire more engineers faster. It’s that they need better signal in a noisier market.

AI has lowered the cost of applying, increased the volume of candidates, and blurred traditional signals of skill. In response, hiring teams are already shifting their approach — often implicitly. The data suggests it’s time to make those shifts explicit.

Here are three concrete ways TA teams can adjust their strategy in 2026.

1. Reduce noise so you can focus on quality

Finding qualified candidates remains the top recruiting challenge, but this year, high application volume has emerged as a close second. AI-assisted job applications are flooding pipelines, making it harder to identify strong candidates early.

If quality of hire is the priority (as 60% of hiring leaders report), teams need tools and processes that:

• Filter volume without relying solely on resumes
  
• Surface real technical signal earlier in the funnel
  
• Reduce time spent reviewing low-signal applications
  

This is where technical assessments play a critical role. When used early and designed well, they help teams move past keyword matching and toward evidence of real ability — especially in high-volume pipelines.

2. Shift assessments toward realistic, on-the-job work

Our research shows growing alignment between developers and recruiters on what actually predicts success: live coding, technical discussions, and real-world scenarios.

At the same time, algorithm-heavy tests remain widespread — even though many teams acknowledge they don’t reflect day-to-day engineering work.

In 2026, effective assessments:

• Mirror how engineers actually work (multi-file codebases, debugging, iteration)
  
• Emphasize judgment and problem-solving over memorization
  
• Create space for discussion, explanation, and collaboration
  

CoderPad Projects are designed around these principles. By focusing on realistic tasks instead of abstract puzzles, teams can see how candidates approach problems, reason through trade-offs, and communicate them.

3. Make AI fluency part of the hiring signal 

AI is already part of how developers work. The question isn’t whether candidates use AI — it’s how they use it.

Our data shows that when AI is allowed in assessments, hiring leaders value candidates candidates who can:

• Catch and fix AI mistakes
  
• Explain trade-offs and correctness
  
• Improve AI output through iteration
  

Rather than banning AI outright or ignoring its presence, teams should define what AI fluency means for their organization and assess it directly. By designing interviews that reflect real, AI-augmented work, teams can assess skills that actually matter on the job — while reducing ambiguity and inconsistency in evaluation.

From Uncertainty to Preparedness

AI has introduced real complexity into technical hiring. But the teams that feel most confident aren’t waiting for the market to “settle.” They’re adapting their tools, assessments, and expectations now.

For TA teams, the path forward isn’t about predicting the future,  it’s about designing hiring processes that reflect reality today.

If you’re a talent acquisition leader hiring engineers, you’re constantly balancing speed, signal, and candidate experience.

You need a process that helps you identify real talent quickly. Your hiring managers want confidence in their decisions. Candidates expect interviews that reflect real work — not academic trick questions. And now, layered on top of all of it, there’s AI.

The question isn’t whether candidates are using AI. It’s whether your hiring process accounts for it.

In a recent CoderPad Demo Day with ERE Media, we walked through what modern technical hiring looks like when it’s built around how engineers actually work. This wasn’t just a product walkthrough of a tool, we showed a fundamentally different approach to evaluating technical talent. You can check out the recording below.

Moving Beyond Outdated Coding Tests

For years, technical screening has relied on algorithm-heavy coding challenges that rarely resemble day-to-day engineering work. While those exercises can measure certain fundamentals, they often miss what matters most in real roles: collaboration, problem-solving under ambiguity, debugging, architectural thinking, and increasingly, the ability to use AI tools effectively.

What TA professionals see as a bottleneck — slow feedback loops, inconsistent interviewer scoring, and candidate drop-off — often starts with assessments that don’t reflect reality.

In the demo, we showed how CoderPad replaces abstract testing with realistic, job-relevant environments. Instead of forcing candidates into artificial constraints, hiring teams can evaluate them in settings that mirror actual development workflows. Code runs in real languages. Frameworks align with the role. Interviews feel like working sessions, not interrogations.

For TA teams, this means fewer debates about whether someone “would have done better in a different format.” The evaluation becomes grounded in how the candidate actually performs in a practical environment.

Supporting the Entire Hiring Funnel 

One of the biggest challenges talent teams face is fragmentation. A screening tool for early-stage filtering. A separate platform for live interviews. Manual scoring sheets shared over email. Disconnected feedback. Delays.

The experience we demonstrated shows how CoderPad supports the full technical hiring journey — from the first screen to the final decision — within a consistent framework.

At the top of the funnel, high applicant volume doesn’t have to overwhelm recruiters. Asynchronous, auto-graded projects allow teams to fairly and efficiently identify qualified candidates. Instead of relying on resume keywords alone, recruiters can move forward with candidates who have already demonstrated applied skills.

As candidates progress, live collaborative interviews bring hiring managers into a shared coding environment where they can observe how someone thinks, communicates, and iterates. Rather than passively reviewing a completed take-home assignment, interviewers engage in real-time problem solving that mirrors team workflows.

By the time finalists are identified, teams have a multidimensional view of each candidate — not just whether their code compiled, but how they reasoned through trade-offs, responded to feedback, and approached ambiguity.

For TA leaders, this continuity translates into clarity. There’s alignment across stakeholders. Evaluations are structured. Feedback is centralized. Decisions move faster.

Measuring More Than Code Correctness

One of the most important themes in the demo was that modern engineering performance isn’t binary. It’s not simply pass or fail.

Strong engineers communicate clearly. They ask clarifying questions. They test assumptions. They refactor. They collaborate. They use AI thoughtfully and verify outputs. They debug strategically rather than randomly.

Traditional coding tests struggle to measure these dimensions. CoderPad is designed specifically to surface them with realistic projects that feel like the actual job.

When interviews simulate real work, hiring teams can observe not just what solution a candidate arrives at, but how they arrive there. Do they explain trade-offs? Do they incorporate feedback? Can they articulate why a particular approach is better for maintainability or scalability?

For TA professionals, this deeper signal reduces reliance on gut feeling. Structured evaluation criteria help interviewers score consistently, which improves fairness and defensibility. That consistency becomes especially important at scale.

Hiring in the AI Era

Perhaps the most significant shift in technical hiring today is the presence of AI-assisted development.

Some organizations attempt to prevent AI use during interviews. Others ignore it entirely. Forward-thinking teams are taking a different approach: incorporating AI into the evaluation process itself.

In the demo, we explored how CoderPad enables teams to assess AI proficiency directly. Instead of asking whether candidates used AI, interviewers can evaluate how effectively they used it. Did they validate the output? Did they understand the generated code? Could they debug it? Could they improve it?

For TA leaders, this matters because AI isn’t a passing trend — it’s embedded in modern engineering workflows. Hiring processes that fail to account for this reality risk measuring an outdated version of developer performance.

The Business Case: Faster Hiring, Better Decisions

While candidate experience and evaluation quality are critical, talent leaders are also accountable to business outcomes.

Teams using CoderPad report measurable impact: reductions in time-to-hire of 20% or more and significant cost savings per hire. Those improvements come from reducing false positives, minimizing late-stage rejections, and giving hiring managers confidence earlier in the process.

When interview feedback is structured and centralized, decision-making accelerates. When assessments reflect real job performance, confidence increases. When recruiters can efficiently filter high volumes without sacrificing fairness, hiring becomes scalable.

For growing engineering organizations, this isn’t incremental optimization — it’s a competitive advantage.

A Better Experience for Candidates…and for TA Teams

The experience candidates have during technical interviews directly affects employer brand. Top engineers are evaluating your process as much as you’re evaluating them.

When interviews feel realistic and collaborative, candidates see a glimpse of what working at your company might actually be like. That perception influences offer acceptance rates and long-term engagement.

At the same time, TA teams benefit from a system that reduces manual coordination, standardizes evaluation, and brings structure to technical decision-making. Instead of chasing feedback and reconciling conflicting opinions, recruiters operate from a shared, transparent framework.

See the Experience for Yourself

The demo day session provides a practical, end-to-end look at how this works in action — from configuring assessments to conducting live interviews to reviewing structured feedback.

If you’re currently evaluating technical assessment platforms and want to understand how CoderPad supports modern, AI-aware hiring at scale, the best next step is to see it firsthand.

If you’re ready to explore what this could look like for your team, book a personalized walkthrough and see how CoderPad fits into your hiring strategy.

Modern technical hiring doesn’t have to feel fragmented, artificial, or uncertain. With the right structure and tools, it can be one of your strongest strategic advantages.

Engineering leaders and talent teams know that a bad interview doesn’t just waste time, it can filter out strong candidates and damage your reputation. But what makes an interview format outdated?

To help you self-assess and course-correct, we’ve created a free, 1-page audit that covers the four key pillars of a modern technical interview:

Real-world relevance

Are you testing how candidates solve realistic, practical, role-specific problems, not just memorized trivia or whiteboard puzzles?

Fairness and inclusion

Do all candidates know what to expect? Are interviewers using a consistent rubric? Structured, transparent practices reduce bias and improve signal.

AI-awareness

In a world of ChatGPT and Copilot, are you evaluating human strengths, judgment, debugging, trade-off thinking, or questions an LLM could ace?

Collaborative style

Is your interview a performance, or a conversation? Interviews that mirror real developer collaboration lead to better outcomes for everyone.

Download the interview audit

Use this free checklist PDF to:

• Score your current process across 16 points
  
• Identify where to modernize, from expectations to prompt design
  
• Start productive conversations between TA and engineering
  
• Set priorities for a more effective, inclusive interview loop
  

Whether you’re hiring one engineer or scaling a global team, this audit helps you align on what matters: assessing real skills, fast and fairly.

And yet – despite how awful this is for everyone impacted – the narrative that ‘tech hiring is dead’ or ‘AI is already coming for your jobs’ is not the whole story. In fact, hiring may be slower but it’s not nothing. It’s a much more nuanced picture.

This is what I see:

1. A return to sustainable tech hiring practices after pandemic-era excess
2. A shift in the skills and profiles companies are seeking
3. Increased investment in junior and AI-proficient technical talent
4. Companies recognizing that AI-augmented developers represent better ROI, driving more hiring, not less
5. An explosion of newly viable product possibilities, thanks to AI, creating demand for people to build them – and, in turn, driving more hiring

Let’s get into it.

A Look at the Actual Data
Over the past five years, we have – without question – seen tech layoffs spike. It’s worth noting that these aren’t just developer roles; they’re jobs across all functions at “tech companies”. In 2023, according to Trueup.io, the number was 430K people impacted; last year, it was 240K. In 2025, the number is projected to reach just over 200K. 

These are real people and real families, and I don’t – for a second – discount the enormous disruption and uncertainty these layoffs have certainly caused them.

But as many have noted, companies are course-correcting after a lengthy period of over hiring and labor hoarding: hiring and holding on to high-value, high-cost workers, a common practice in 2020 and 2021. In fact, some companies “nearly doubled their headcount between 2019 and 2022” as a result. Today’s rebalancing, as The Wall Street Journal observed, may be spurred by multiple factors: the promise of what AI might be able to do in the future, weak demand, and economic volatility. 

As of early November, there are nearly 240K open tech jobs, a decline of roughly 50 percent from tech’s peak and an increase of 45 percent from the industry’s low two years ago. 

That’s not zero. 

That’s a painful normalization after an unsustainable spike.

It tracks with what we’re seeing at CoderPad. When I look at our own data on technical interviewing activity, comparing this time last year to today, we’re seeing usage trending substantially upward, if we compare 2023 to today. 

As you can see, companies are still investing in technical talent. And they’re doing so incredibly intentionally.

Companies Are Reshaping Hiring Based on AI (And That’s Not Bad)

AI isn’t decimating developer jobs so much as it’s transforming them in three fundamental ways:

AI is changing the skills teams need, not eliminating teams entirely

As organizations grow, they’re actively seeking people who can work effectively with AI tools, know how to leverage these capabilities, and bring AI-native thinking to their work. This is driving demand for different skill sets and profiles than we saw five years ago. Companies that relied on basic technical assessments are realizing they need better solutions to evaluate these evolving capabilities.

AI is improving developer ROI – and, in turn, their value to companies

Think about it this way: if a developer used to ship 10 product features a year, and AI makes them 30% more effective, that same person now delivers 13 features annually – for the same cost. 

That’s not a reason to hire fewer developers. 

That’s a reason to invest more in developer talent because you’re getting better returns. 

Research from companies like GitHub shows that developers using AI coding tools are seeing measurable productivity improvements, particularly in reducing time spent on repetitive tasks and code reviews. And we’re seeing this driving an increased demand for developers – both internally at CoderPad and with our customers.

AI is opening the aperture on what we can build today

Features and products that would have taken years to develop – or had been completely impractical to invest in – are suddenly achievable. This isn’t theoretical for us. At my company, we’re experiencing this firsthand: so many ideas that were once in the “someday/maybe/if we have resources”column are now viable. We need more talented people to bring these possibilities to life. People power is critical – and I assure you, we’re not the only company who sees that!

Smart Companies Are Hiring For AI-Savvy Developers

The macro data tells one story. But let me share what I’m hearing directly from customers and prospects in the past month:

Shopify, for example, is expanding its intern and junior developer program by 20x, from 50 hires two years ago to approximately one thousand. Why such aggressive growth in entry-level talent? Three reasons: there’s an enormous amount to build, they recognize they need to invest in junior talent today to develop senior talent tomorrow, and they know younger, AI-native workers will bring fresh perspectives and skills to their culture and processes.

Despite their layoffs, Meta, as you’ve probably heard, is on a significant hiring push. And Amazon currently has thousands of software engineer positions open, also going against the Amazon headlines. Walmart is hiring aggressively for developers.

Take Heart – And Action

The full picture helps people make informed decisions about their hiring strategies, assumptions about where the market is heading, and their careers.

If you’re a developer: the skills you have and continue to build matter. Particularly if you’re learning to work effectively with AI tools, you’re making yourself more valuable, not less. Companies need you.

If you’re a hiring manager: look at your actual business needs and the ROI of technical talent in an AI-augmented world. The math may surprise you. And if you need to hire more devs, call me.  

If you’re a junior developer: know that companies are actively seeking AI-native talent who can bring new approaches. Your fresh perspective has value.

And if you’ve been laid off: this is painful,and I’m sorry. Keep going. The data suggests opportunities are still out there. The market is shifting, not disappearing.

The question isn’t whether technical hiring is dying. It’s how we’re preparing for the kind of technical hiring that’s emerging.

Ready to start modernizing your technical hiring for the AI-era? CoderPad enables AI-aware, realistic assessments that measure how candidates actually work with modern AI tools—so you get stronger signal, fairer evaluations, and faster decisions without encouraging “no-AI” test behavior. Get in touch with our team here.