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What Tech Companies Need To Know About Quiet Quitting

What Tech Companies Need To Know About Quiet Quitting

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September 23, 2022
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3 min read
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Picture this: your employee is on vacation. Before taking their time off, they submitted all the tasks assigned. But, the manager reviewed their work after they left and drops them messages after messages. Two cases would happen:
  • Case 1: Employee checks the messages and edits their work while on vacation.
  • Case 2: Employee turns off their notifications, enjoys their vacation, and checks the messages after returning.
When employees choose the latter approach, they quiet quit their work.However, numerous misconceptions are floating about the term.People trying to explain quiet quittingLet’s understand what exactly quiet quitting is and what you as a tech company need to know about it.

What is quiet quitting?

Quiet quitting is not laziness

Employers' and recruiters' perspectives on quiet quitting: employees quitting their job or being lazy. It’s neither.When employees quiet quit their job, they do exactly what their job description says. They don't choose to overwork, cling on to after-work calls, and shut their emails once they head home.In other words, quiet quitting is all about maintaining healthy boundaries and creating a work-life balance.

What does quiet quitting look like in practice?

Because quiet quitting focuses on creating a healthy work-life balance, employees practicing it usually:
  • Say no to projects that do not come under your job description
  • Leave work on time
  • Don't check their emails and slack messages after work hours

Quiet quitting in tech: Why does it happen?

Who are quiet quitters
  • Higher expectations: Imagine a manager overworking, staying late (even on weekends), and working while employees enjoy their weekends off. Because they are driven toward their job, they expect their team members to have a similar approach.
  • But here's the thing: not every employee has similar career aspirations and not all of them want to work day in and out—especially Gen Z.
  • Unable to take full advantage of health and wellness benefits: Half of the employees fail to utilize the health and wellness benefits companies provide. They may go on once-a-year vacation but it (still) leaves room for burnout.
  • Poor management: According to Gallup, quiet quitting is a consequence of poor management. Managers fail to show leadership skills their team needs where empathy and compassion count first. That's why, when employees overwork, managers fail to recognize their efforts—leading to disengaged employees.

Also, read: 7 Ways To Reduce Burnout In Your Tech Teams

How can tech companies respond to quiet quitting?

Quiet quitting in tech: Why does it happen?

Tech companies have been changing their approach to creating a healthy work environment for their employees ever since the companies transitioned to on-premises. Why?

Working remotely helped employees create healthy boundaries that have been disturbed after their return to the office.

For those interested, here's an insightful thread on what quiet quitting means for people working in different sectors. So what can you do to make sure your employees are engaged and minimize quiet quitting?

#1 Transition to a 4-day work week

Companies have already started transitioning to a 4-days work week model. Employees in these organizations work for four days and have the remaining days off. This gives them ample time to rejuvenate. Does this mean they‘ll need to work extra hours on the 4 working days? Not at all.A great example: MyCheckins, a Bangalore-based SaaS company functions Monday to Thursday for 32 hours.

#2 Switching teams internally

Are the employees disinterested in working with your team? Instead of pushing them to give quality output, get them to work with another tech team in your department for a few days. If they feel engaged working with the other team, help them make the switch to the other team.

#3 Run employee pulse surveys

Running regular employee pulse surveys is a great way to keep a tab on what's happening on the ground level. By reviewing the survey, you can find the loopholes and work on improving the reasons for disengagement.

#4 Recognize them for their efforts

Many times, employees work hard only to feel unappreciated. When they put in extra effort, they expect two things from their managers—to be appreciated or rewarded with a pay rise.When neither happens, they pull themselves off from the overwork they had been doing. To tackle these situations, managers must learn to appreciate their team members more and reward them whenever relevant.For example, when a team member does incredible work on a project, send them a thank you note or celebrate their efforts in front of fellow team members.
Also, read: 7 Employee Engagement Strategies For WFH Tech Teams

Can quiet quitting ever be positive? Let’s see…

In the context of technology companies, where the phenomenon of quiet quitting has been prevalent, specific positive effects can be observed. Quiet quitting often results in less immediate disruption to ongoing projects and workflows. Teams can continue their work without the abrupt departure of key personnel. Employees who quietly quit may stay on for an extended period during their transition, allowing for the transfer of critical knowledge and skills to team members, ensuring continuity in project execution.For employees, quiet quitting provides time to reevaluate their career paths within the tech industry. They can explore new opportunities while still fulfilling their current roles, leading to a more informed career shift.

What are some of the negative effects of quiet quitting in tech companies?

While there are positives, quiet quitting often leads to a reduction in employee productivity, as workers only perform the bare minimum required by their job roles. In tech companies, this can slow down project timelines and innovation cycles.When some employees engage in quiet quitting, it can negatively affect the morale of their colleagues who may feel overburdened or demotivated by the lack of shared effort and enthusiasm. In the tech industry, where precision and innovation are key, quiet quitting may lead to a decline in the quality of work. This could manifest in more bugs in software or less creative solutions to technical problems.What’s even more important to understand is that quiet quitting is often a precursor to actual quitting. Tech companies might face higher turnover rates, leading to the loss of skilled employees and increased costs in hiring and training new staff. One of the biggest assets in tech is innovative thinking. Quiet quitting can lead to a stagnation in creative ideas and initiatives, which are crucial for a tech company’s growth and adaptation to market changes.The ripple effect of reduced productivity and quality can potentially reach customers, leading to dissatisfaction with the products or services, which is particularly detrimental in the competitive tech industry. Managers may find themselves spending more time micromanaging or addressing the consequences of quiet quitting, instead of focusing on strategic planning and fostering a positive work environment.

Become a healthy employee-first company

As a company, HackerEarth has a healthy employee-first perspective on things. If employees are indulging in producing lesser output than what their job says (instead of creating healthy boundaries!), relook into the few ways we shared above to create a meaningful and healthy work environment.

On our latest episode of This Is Recruiting, we spoke at great length with Crystal Lay, CEO of GBS Worldwide about what talent teams can learn from the Quiet Quitting trend to improve employer branding and workplace culture. Watch the full episode here!

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Author
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September 23, 2022
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3 min read
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Vibe Coding: Shaping the Future of Software

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code.

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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