- AI just broke the hiring playbook: fake profiles, proxy interviews, and vibe coders are exposing the cracks in old methods.
- Meta says “bring AI to the interview,” Amazon says “you’re out if you do”: the industry is split with no clear consensus. Big tech (aside from Meta) tends to preserve the status quo, while startups lean toward innovation and flexibility with AI in hiring.
- Something has to change: there’s broad consensus that tech hiring is shifting, and companies must act to avoid being left behind. That’s why we built inMOVE™ by Inclusion Cloud—a methodology designed to separate real engineers from AI-generated noise.
AI Just Crashed the Interview: What It Means for Tech Hiring
It starts with a suspicious pause.
A candidate nails the coding challenge in record time, but when the interviewer asks a simple follow-up — “what are your hobbies?” — the screen goes silent. Eyes flicker off-camera. The polished answers stop flowing. Minutes later, it’s clear: the candidate wasn’t coding, they were reading. Another case of AI-assisted “ghost” interviewing.
This isn’t a one-off. Reports show that 65% of job seekers already use AI somewhere in the hiring process, and a growing black market of proxy interview tools, deepfake voices, and even paid “bait-and-switch” candidates are reshaping what fraud looks like in tech hiring.
And yet, companies are divided.
- Meta is experimenting with allowing candidates to bring AI into interviews, arguing it mirrors real working conditions.
- Amazon takes the opposite path, threatening disqualification if AI is detected — calling it an “unfair advantage.”
Meanwhile, the broader economy adds fuel to the fire. The end of the ZIRP era forced big tech to slash costs, unleashing waves of layoffs and putting more pressure on candidates to stand out — by any means necessary. That pressure, combined with ever-cheaper AI tools, is what’s driving this surge in “fake candidates.”
So the question is no longer if AI is reshaping tech hiring — it’s how companies will adapt. Do we go back to in-person trial days and longer probation periods? Do we build new processes to spot fraud? Or do we accept AI in interviews and evolve how we evaluate talent?
One thing is clear: hiring can’t remain a lottery. We need precision, not guesswork. That’s why at Inclusion Cloud we’ve rethought the playbook, building methods and tips to help companies spot fake candidates, minimize the cost of a bad hire, and keep the gates open without falling into gatekeeping.
Hiring in murky waters: how economics and AI blur the talent pool
The hiring landscape in tech has drifted into murky waters. As Business Insider reported, the end of the ZIRP (Zero Interest Rate Phenomenon) era pushed big tech toward an “efficiency mindset,” reshaping an industry once synonymous with cushy perks and stability. The result is a tighter, more competitive market where candidates face greater pressure to stand out.
At the same time, AI tools have lowered the barriers to entry. With just a few prompts, job seekers can generate polished résumés, scripted answers, or even passable code. What once depended on memorizing LeetCode questions is now fueled by real-time assistance from chatbots and proxy services. The talent pool looks deeper than ever—but harder to read.
At Inclusion Cloud, we see this every day. More profiles, faster responses, cleaner tests—but also more uncertainty about what’s real and what’s generated. The challenge is no longer just finding talent; it’s finding signals you can trust. And that’s why we believe hiring practices must evolve, because companies can’t afford to rely on luck when making critical engineering hires.
What Are the Two Biggest AI Challenges in Tech Hiring Today?
Finding strong engineers has always been a challenge. But with AI, new layers of complexity are reshaping the game. Companies now face two possible paths:
- Understand the threats—so they can adapt their recruiting processes to this new context.
- Partner with specialists—firms that combine deep knowledge of technology and recruiting to cut through the noise.
We’ll come back to that second path later. First, let’s look at the two challenges that AI has introduced into tech hiring.
1. The rise of fake candidates
A market of tools and services is quietly emerging with one goal: help candidates cheat their way into jobs.
- Proxy interviews 2.0: Services now exploit apps like Otter.ai—originally designed for meeting transcription and productivity—to transcribe interview questions in real time and feed polished answers back to the candidate, who simply reads them aloud. BI reported that startups such as Final Round AI market “magical teleprompters” that whisper AI-generated responses during calls.
- Deepfake job seekers: As CNBC revealed, different companies have already caught impostors using face-swap software and voice clones to pass video interviews. In one case, the candidate’s lip movements were just slightly off—a telltale sign of a deepfake. Gartner warns that by 2028, 1 in 4 job candidates globally could be fake profiles.
- Bait-and-switch hires: A qualified proxy takes the interview, only for an underqualified (or entirely different) person to show up once the offer is accepted. This isn’t new, but AI has made it easier to coordinate. In some cases, candidates even mime silently while a proxy speaks for them in the background, or use hidden earpieces to be fed real-time answers. As shown in a curious video circulating online, you can see a candidate awkwardly lip-syncing responses while another person—presumably seated right next to them—does the actual talking.
2. The AI-inflated candidate
The second challenge may not be as scandalous as fake profiles, but it’s arguably harder to spot—and it requires sharper training to detect. Here, companies face two scenarios: they can either allow AI assistants openly during interviews, or ban them while candidates may secretly use them anyway. In both cases, the same doubt lingers: does the candidate really have the skills, or is the AI doing the heavy lifting?
In other words, will the candidate flop when faced with problems that go beyond what today’s AI tools can solve?
Of course, this isn’t an entirely new issue. For years, candidates could “game” technical interviews by memorizing common challenges from platforms like LeetCode or HackerRank. The line between memory and genuine problem-solving was already blurry. Now, generative AI has collapsed that line further by producing entire solutions in seconds.
Some candidates now prepare by feeding job descriptions into GenAI to generate tailored Q&A scripts. Others go further, using real-time “teleprompter” apps that whisper answers during interviews. Research shows that around 65% of job seekers already use AI somewhere in the hiring process.
The result is an interview that often feels smoother than ever—fluent answers, clean code, little hesitation. But this polish makes it harder to tell whether you’re evaluating the candidate’s own problem-solving ability or the output of a well-tuned prompt. And here’s the paradox: someone struggling through a messy but authentic answer might actually be demonstrating deeper skills than the candidate delivering a flawless AI-scripted response.
That’s where bias creeps in. As interviewing.io’s Aline Lerner has noted, interviewers may overvalue “perfect” answers while undervaluing genuine, in-the-moment reasoning. The danger is mistaking polish for competence—and hiring someone who can pass a test but can’t perform when real complexity shows up.
Should AI Join the Interview? Why Meta Says Yes and Amazon Says No
If there’s one area where the industry hasn’t reached consensus, it’s whether AI belongs in the interview room. Let’s look at the dominant approaches across big tech and smaller companies:
- Meta is running pilots that allow AI use during coding interviews. Mark Zuckerberg even compared AI to “a sort of mid-level engineer” and argued that allowing candidates to use it actually reduces cheating—since it mirrors the conditions under which they’ll work if hired.
- Amazon takes the opposite stance. Their internal guidelines explicitly ban AI use in interviews, warning candidates that it’s an “unfair advantage” and grounds for disqualification.
- Startups tend to be more flexible. Many treat AI as just another tool, arguing that banning it is like banning a calculator in a math test. What matters, they say, is how candidates use it—whether they can integrate AI into workflows responsibly.
- Anthropic offers perhaps the most thoughtful middle ground with their published guidelines. They encourage candidates to:
✅ Use Claude to strengthen applications, prepare for interviews, and practice storytelling.
❌ Avoid using AI to fabricate experiences or generate code during live interviews (unless explicitly allowed).
✅ Be transparent about how AI is used.
❌ Treat Claude as a substitute for their own thinking.
They also disclose how they themselves use Claude in hiring—drafting job descriptions, refining interview questions, and analyzing metrics—while stressing that final decisions are always made by humans. As Anthropic puts it: “Use Claude to refine your ideas, not replace them. We want to see your actual experience and how you think—not AI-generated responses.”
What these different stances show is an industry still searching for direction. Some want a “pure” measure of raw skill, others want to reflect real working conditions, and some are trying to balance both by setting clear boundaries.
We opened this discussion recently in a Reddit thread that quickly gathered dozens of perspectives. Some argued that AI in interviews is inevitable—“like banning Excel for finance roles.” Others warned that banning it can feel archaic. And many agreed that the real test should shift: from whether a candidate uses AI to how they use it.
Is presenciality the solution?
When fraud shows up in remote hiring, many companies fall back on the same instinct: ‘If remote is the problem, let’s bring people back into the office for interviews.’
It’s an understandable reaction.
In-person, it’s harder to imagine someone secretly reading AI-generated answers off a second screen. But here’s the truth: fraud and weak evaluations existed long before Zoom. Whiteboard interviews could be gamed by drilling LeetCode problems until answers became muscle memory. Some companies even faced bait-and-switch hires, where the person who aced the interview wasn’t the same one who showed up on the job.
Geography never guaranteed authenticity.
And forcing in-person processes carries its own costs. Flying candidates in or running trial days slows hiring, raises expenses, and shrinks the talent pool. For many organizations, remote teams are not a choice but a necessity: they provide access to global talent, reduce costs, increase the overall seniority of their teams, and open doors to specialists who may not exist in the local market.
The real issue isn’t remote vs. in-person. It’s that hiring processes are lagging behind the capabilities of AI. Interviews designed for a pre-AI world fail to reveal who’s genuinely skilled and who’s just “looking like a developer” with AI in the background. As Pablo Bucci, Recruiting Lead at Inclusion Cloud, explains: “Too often, we get generic answers that lack context, documentation, or any real architectural reasoning,” says Pablo. These are clear signs that the candidate may be relying on AI without understanding the structure behind what they’re building.
So, can remote interviews still be effective in this new era of augmented software development?
We believe the answer is yes. Remote remains an efficient way to assess candidates — but only if processes are adapted to mirror real working conditions, test for deeper skills, and surface how candidates use AI responsibly. That means structured live challenges, double validation by HR and senior engineers, and cross-checking interview answers against real project history.
In other words, to fix hiring, companies need to rethink how they conduct interviews and adopt new ways of flagging suspicious behavior in candidates. At Inclusion Cloud, we help with that task. These are some of the practices and strategies any company will need to start applying in order to avoid falling for these emerging schemes and to make sure they’re selecting the right people.
How to Spot Risky Profiles—and Surface the Real Talent
Remote recruiting isn’t broken; outdated processes are. The real risk comes from two patterns you’ve already seen:
- Fake candidates.
- AI-inflated candidates.
Both patterns create the same failure mode: someone sails through interviews yet can’t deliver once inside the org.
The cost of a bad hire
A wrong hire doesn’t just waste salary. It burns onboarding time, delays projects, forces high performers to carry the load, and can introduce security and compliance risk. “Trial hires” sound safe, but by the time you discover the mismatch, you’ve already lost weeks or months and now need to restart sourcing, interviewing, and ramp-up.
Detecting the red flags—and what to do next
Below is a practical, step-by-step playbook you can drop into remote processes. It keeps remote efficient and safe, while adapting signals to the AI era.
1) Scripted polish vs. real thinking
What the signal looks like
Fluent, perfectly phrased answers, followed by awkward silences on simple follow-ups. Eyes drift to a fixed spot on the screen. Answers arrive in polished blocks after a short delay.
Why it matters
This is a classic sign of teleprompters or real-time overlays feeding AI-generated text. As hiring stories covered by The Pragmatic Engineer show, candidates can ace LeetCode-style tasks, then freeze on “off-script” questions.
How to act
Use probing follow-ups that expose depth, not memorization. MIT Sloan suggests rotating deeper probes:
- Walk me through the details, step by step.
- Why did you do it that way?
- What else did you consider?
- When would this approach not work as well?
- What would the strongest counterargument be—and how would you answer it?
Pablo Bucci explains how the Inclusion Cloud recruiting team reacts when answers sound generic or over-polished:
“In those cases, we ask real-world questions where they need to apply best practices or explain how they solved a specific problem in a project. Then we cross-check that with their resume and LinkedIn. That’s where you really see if they were hands-on or not.”
Even a simple curveball like “What are your hobbies?” can break the cadence when someone is reading. Authentic experience gets stronger the deeper you go; AI-parroted answers crack quickly.
2) Inconsistent story across résumé, LinkedIn, and code
What the signal looks like
Someone describes backend scaling or data pipelines, but their public history is front-end only. Timelines don’t add up. Claims of long experience on a tool, but zero concrete artifacts.
How to act
Cross-check claims with public footprints and ask for an artifact tied to their name (a PR, a short design doc, a slide deck). Pablo explains: “We always cross-check their answers with their public profiles. Resume, GitHub, LinkedIn. If they describe backend decisions but everything on their resume is front-end work, that’s a red flag.”
3) Take-homes that compile—but nobody owns
What the signal looks like
Clean code, no README, generic structure, brittle edges. In one case reported by The Pragmatic Engineer, “honeypot” instructions hidden in the prompt quietly reappeared in the code—clear evidence an LLM generated it and nobody reviewed it.
How to act
Keep the assignment tight and realistic. Require a short README with trade-offs and “what I’d do next.” Then run a live extension: add a feature, change the contract, introduce a failure case. Pablo’s team reviews every submission: “Our technical team can tell when a solution makes sense and when it’s just generic—which is often the case with AI-generated answers.”
4) Invisible helpers during live coding
What the signal looks like
Repeating questions aloud, glancing to the same corner, perfect paragraphs after a beat.
How to act
Ask them to think aloud, then switch to tasks AI struggles with: debugging noisy logs, scaling under constraints, or extending fragile code. Pablo says: “We use live code challenges to observe in real time how candidates perform. If they’re typing in another window, taking too long, or constantly searching for things, it becomes obvious.”
5) Possible identity spoofing (rare, but costly)
What the signal looks like
Lip-sync slightly off; voice/face filters; VoIP-only phone numbers; zero digital footprint for a “senior.”
How to act
Ask candidates to turn off filters, make natural face motions (like turning their head side to side or briefly passing a hand in front of their face), and verify if the phone number provided is carrier-based or VoIP. The absence of a digital footprint is a red flag. Combined with mandatory video-on policies, these checks close easy loopholes without punishing real candidates.
6) Evaluate AI use directly
What the signal looks like
Not all AI use is equal. Some candidates will openly discuss how they leverage tools; others might get nervous when asked, especially if they’ve been quietly relying on them during the process. That hesitation itself can be a signal: if someone freezes or gets defensive when you bring up AI, it may mean they’ve been leaning on it without declaring it.
Why it matters
The two big threats we’ve seen — fake candidates and AI-inflated ones — both converge here. One hides behind tech, the other looks more senior than they are thanks to AI polish. In both cases, understanding how someone uses AI is critical. Even if AI is banned during interviews, it’s still part of the real job, and you need to know whether candidates can integrate it.
How to act
Ask directly: “How do you use AI tools in your day-to-day work?” or “Can you give me an example of when AI saved you time, and when it caused problems?”
Pablo explains the split in approaches:
- Low-code platforms with embedded AI (ServiceNow, Oracle, SAP, Salesforce):
“In these environments, most of the heavy lifting is done by the platform itself. What matters is asking what automations the candidate built and how. But it’s just as important to know if they understand the business and workflows, because that’s how you know if the automation made sense.”
Here, certifications and project depth are strong signals. Pablo adds: “If they’re certified and have experience working on real projects, you can dig deeper: what did they build, what problems did they solve, and how would they approach similar situations today?”
- Generative AI + custom code (backend, frontend, full-stack with ChatGPT/Copilot):
“This is where things get trickier. We’ve had interviews where candidates are clearly reading AI-generated responses or taking long pauses because they’re prompting something in another tab.”
His team adapts: “In those cases, we ask real-world questions where they need to apply best practices or explain how they solved a specific problem in a project.”
By making AI use an explicit topic, you achieve two things: you expose the over-polished candidates who struggle when asked to go deeper, and you identify the truly AI-augmented developers who know when to lean on tools and when to rely on their own judgment.
7) The Elon Musk test
Elon Musk’s favorite interview question is effectively simple: “Tell me about one of the hardest problems you’ve worked on—and how you solved it.”
The power of this question is that it forces candidates to move beyond rehearsed answers. Someone who has truly been involved in solving a difficult problem can recall specifics: the context, the alternatives they considered, the trade-offs they made, and even the mistakes along the way. For example, a genuine candidate might say: “We were scaling a payment system under heavy load, and the caching layer we used started causing race conditions. We tried X first, but it created latency spikes, so we pivoted to Y. That solved throughput but forced us to rethink error handling.” Those details are very hard to fake.
By contrast, candidates leaning on AI or superficial prep tend to stay abstract. They’ll repeat generic statements like “I improved system performance by optimizing the code” without being able to explain what exactly was optimized, how they identified bottlenecks, or what the downstream effects were.
The Musk-style question works because it doesn’t test memorization—it tests ownership. A real engineer can explain the “why” behind their decisions, not just the “what.” And if they can’t, that’s a red flag that their skills may be inflated or second-hand.
8) Non-technical cues and ownership
- Camera on, presence real: “In remote interviews, the camera must be on. You can tell when someone is reading. There are strange silences or oddly paced answers,” says Pablo.
- Consistency across history: Do their stories match what’s verifiable?
- Communication under pressure: Pablo often asks: “What would you do if a delivery is delayed and the issue came from another team?” These situational questions show collaboration, ownership, and adaptability—beyond just the qaulity of the code.
9) Double validation: HR + senior engineering
At Inclusion Cloud, we never leave hiring decisions to chance—or to a single perspective. Our process always includes two layers of evaluation. First, HR validations that cover the basics: background, résumé consistency, and cultural fit. Then, a second round led by technical leaders who replicate the kind of technical challenges developers will face on real projects.
This dual approach is where “fake polish” collapses. Someone who relies on generic answers or scripted performance struggles when confronted with system migrations, legacy constraints, or integration trade-offs. But genuine engineers shine in these scenarios, showing how they think, adapt, and connect technical choices to business outcomes.
How We Built inMOVE™ by Inclusion Cloud
We didn’t just update our interview scripts. We built an entire framework—inMOVE™ by Inclusion Cloud—to adapt recruiting to the AI era.
In its first stage, inMOVE™ uses advanced AI tools to filter more than a million candidates in our database, cross-checking resumes against public data. The goal is precision: surfacing profiles that not only look good on paper but also align with real project history.
In its second stage, we go beyond the résumé to profile how candidates actually use AI. Are they simply leaning on it as a crutch, or do they integrate it responsibly into their workflow? We look at how they refine prompts, validate outputs, and fit AI use into secure, scalable development practices.
Then comes the human filter: double validation by HR and senior engineers, with live coding challenges and scenario-based interviews. That’s why our clients consistently receive senior, certified, and field-tested engineers—not AI-inflated résumés.
Conclusion: Adapting to the Chain Reaction
In science, small shifts in one element can trigger ripple effects across an entire system. Hiring works the same way. The rise of accessible AI tools, combined with the pressure candidates feel in today’s market, has introduced new incentives for gaming the process.
Some will rethink their internal hiring approach: retraining recruiters, redesigning interviews, and investing in better signals to separate real skill from scripted polish. It’s a heavy lift, but it can work if you have the resources to build those capabilities in-house.
Others will decide that precision requires scale and expertise, and will turn to a partner who already knows how to navigate these risks. A partner who can bring both AI-powered screening and human-led validation to the table, making sure candidates aren’t just good at looking good, but can actually deliver once they’re hired.
What’s clear is that doing nothing is no longer an option.
Old processes won’t catch deepfakes, proxy interviews, or AI-polished résumés. And trial hires that “test” candidates only postpone the cost of a bad match.
If you’re looking for a partner to improve the precision of your hiring pipeline, we’d be glad to schedule a discovery call. Together, we can explore how our AI-powered recruiting engine can help you avoid the noise—and accelerate your roadmap with elite talent.
Q&A: What Leaders Ask Us About Hiring in the AI Era
Q: Can remote hiring still be safe?
A: Absolutely. Remote hiring isn’t broken—outdated processes are. With the right signals, double validation, and AI-aware methods, companies can continue to hire remotely while keeping quality high and risk low. That’s exactly what our inMOVE™ framework was designed to do.
Q: What’s the real cost of a bad hire?
A: It’s never just salary. A wrong hire slows projects, increases onboarding costs, and drains your best engineers who end up covering gaps. In some cases, it can even create compliance or security risks. By the time you realize the mismatch, you’ve already lost time and momentum. That’s why prevention is far cheaper than correction.
Q: How do you separate genuine developers from AI-inflated ones?
A: We look at context, not just output. Anyone can generate clean code with AI, but only skilled engineers can explain why it was built that way, the trade-offs they made, and how it fits into the bigger system. That’s why we focus on real-world scenarios, consistency across resumes and public work, and live challenges that reveal problem-solving under pressure.
Q: Should we ban AI in interviews?
A: That depends on your philosophy. Some organizations want to test raw problem-solving with no tools. Others prefer to mirror real-world conditions, where AI will be part of the workflow anyway. We’ve adapted to both approaches—what matters is setting clear rules and then focusing on how candidates use AI responsibly.
Q: Why work with a partner instead of building our own process?
A: Because adapting alone takes time, training, and resources. We’ve already built and refined a recruiting engine for the AI era—filtering over a million profiles, cross-checking public data, and running senior-level technical validations. By working with us, you don’t just get candidates—you get proven, certified engineers who’ve been screened for both skills and adaptability in this new landscape.