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Cheating has long been an unwelcome but expected risk in the hiring process. While most people are honest and well-intentioned, there are always a handful of candidates who attempt to game the system. Today, however, the problem is evolving at an unprecedented speed. Generative AI has made new, more sophisticated types of cheating possible for any position, from software development to finance to design. In my work with hundreds of employers helping them hire and develop talent, I’ve seen firsthand the myriad ways candidates attempt to game the system. So, why are candidates resorting to these methods? Sometimes, candidates are attempting to secure a position theyre underqualified for, or otherwise gain a leg up in the hiring process. Other times, candidates pursue multiple full-time roles at oncea trend known as overemploymentwhich increases the likelihood that theyll cheat. Here are the four most common approaches candidates use to cheat, and what employers across all industries can do to detect and prevent dishonesty in their hiring processes. THE FOUR CHEATING TYPES 1. Copy-paste plagiarism This is the most widespread and fundamental form of cheating. A candidate is given a taskit could be a coding challenge, a writing sample, or a case studyand they simply copy or heavily borrow from existing resources found online. In some cases, candidates even use answer keys for standardized assessments, which are often sold or shared in online forums. How to detect and prevent it: The ideal mitigation strategy for this type of cheating is to prevent it in the first place by ensuring multiple candidates don’t see the same questions. Think about the SAT, for examplethousands of versions of each question are created and dynamically rotated, but calibrated to be equally difficult. So if a question leaks, it’s unlikely that another candidate sees the same question. Assessment platforms should also crawl the web to see if submitted work matches known public answers, and flag if a candidate spends lots of time in a separate browser window. 2. Hiring a “ringer” With this method, a candidate hires a highly-qualified individuala “ringer” or proxy interviewerto take a skills assessment or even a live interview on their behalf. This is a particularly sneaky form of cheating because the person taking the test is genuinely skilled, but they aren’t the person you’re considering for the job. The problem only becomes apparent later when the person you hired can’t replicate their performance on the job. How to detect and prevent it: The best way to combat this is with identity verification and proctoring. This can be as simple as asking candidates to show a photo ID via webcam at the start of the assessment. Organizations can also use AI-powered proctoring to monitor a candidates behavior, flagging suspicious activity like multiple people in the room or eye movements that suggest they’re getting helpand verify this with human review. 3. Using AI to generate answers This is where AI has truly changed the game. Instead of searching for an answer, a candidate can use a text- or voice-based AI tool to get a complete answer in seconds. These AI models are not only fast, but they generate original content that wouldn’t necessarily be flagged by a simple plagiarism checker. While some organizations may be okay with candidates leveraging AI toolsespecially if theyd be using AI on-the-jobothers are looking to see a candidates skill without the assistance of AI. How to detect and prevent it: One solution is to use AI detection tools that can analyze text for patterns consistent with AI generation. A more robust approach, however, is to design assessments that require human-level reasoning and creativityand even allow candidates to leverage AI to produce their response. With this approach, employers can see how well candidates make use of all the tools theyd have at their disposal on the jobincluding AIto solve realistic challenges or tasks. 4. AI deepfakes This is perhaps the most frightening new form of cheating, and it’s making headlines. A candidate can use AI deepfake technology to create a convincing, real-time avatar of themselves that takes a live interview. This AI-generated persona would not only answer questions but could also mimic facial expressions and body language, which makes it difficult for a human interviewer to distinguish it from the real person. How to detect and prevent it: One way to spot deepfakes is with sophisticated AI-powered analysis. These tools can look for subtle inconsistencies like unnatural eye movements, a lack of blinking, or a disconnect between audio and video streams. Companies can also require a simple, real-time action from the candidate, such as holding up a specific object or moving their head in a certain way, that would be difficult for a deepfake to replicate perfectly. FINAL THOUGHTS The risks and costs of a bad hire are well-documented. An employee who lacks the skills they claimed to have will drag down a team, create poor quality work, and ultimately have a negative impact on the business. The integrity of your hiring process is a direct reflection of the quality of your future team. While the rise of AI has introduced new risks to employers, it has also given us new tools to more accurately identify candidates with the right skills for the job. Used well, AI tools can be a powerful partner in our efforts to build a fair and predictive hiring process. By embracing these advancements, we can move beyond simply detecting cheating and build a future where AI empowers us with new ways to find and hire candidates who will take innovation to the next level. Tigran Sloyan is CEO and cofounder of CodeSignal.
Category:
E-Commerce
Leonardo da Vinci is often credited with writing the first resume in 1482, meaning the resume has been with us for more than five centuries. And though its layout has evolved over the years, the premise hasnt: a piece of paper that tells someone where youve worked, what you studied, and maybe a bullet or two about what youve accomplished. Thats the problem. The resume is designed to tell us where someone has beennot what they can actually do. It shows what the last person who hired you needed done in their company that they thought you could handle. It looks backward when the world of work we live in today demands that we look forward. It inflates titles, overvalues brand-name employers, and reduces people to keywords designed to sneak past applicant tracking systems. Too often, the best resume isnt from the most qualified candidateits from the one who figured out how to game the system. And yet resumes persist. Why? Because theyre easy. Theyre free to create. Most people (sort of) know how to make one. Employers ask for them. Entire hiring systems are built around them. Theyre the definition of mehits good enough. But good enough is no longer enough. WHAT BETTER LOOKS LIKE Were in a labor market that is more dynamicand more inequitablethan ever. The resume does nothing to address that. It privileges polish over abilities. It amplifies bias through names, schools, and companies that often serve as proxies for race, gender, age, and class. It fails miserably at consistencyone candidates resume looks like a design portfolio, anothers like a plain-text listand leaves parsing software to guess what counts. No wonder amazing candidates fall through the cracks. So what would better look like? A new standard has to do what resumes cant: Capture actual skills. Not just where youve been, but what you can doand what extras make you uniquely you and uniquely qualified. It has to be structured, so it can travel across technologies. Standardized, so hiring managers can make real comparisons. Accessible, so you dont need expensive software or a design degree to present yourself fairly. And anonymizable, so the three pound caloric monsters in evaluators skulls cant fall back on the same biased shortcuts resumes encourage. Of course, the hard part isnt dreaming up the new standard. Its building the bridge. We cant assume every applicant will suddenly have the time or access to build a digital skills profile. And we certainly cant expect that employers will throw out their applicant tracking systems overnight (even if they might want to). We need technology that can translate existing resumes into skills-based profiles, while slotting neatly into the workflows companies already use. Thats how you shift the system: not by burning it down, but by giving people a clear path to make the switch. I know some will say the resume is too entrenched, too universal to ever disappear. Theyre probably right in the short term. But history is full of standards that seemed permanentlandlines, CDs, even fax machinesuntil they werent. The resume has lasted 543 years not because it was brilliant, but because it was familiar. Familiarity isnt a good enough reason to keep failing candidates and employers alike. If we want hiring to be fairer, faster, and more predictive, we need to blow up the resume and build something better. The future of work isnt about paper credentialsits about the skills that predict how a person will perform in a role. And its past time our hiring systems caught up. Natasha Nuytten is CEO of CLARA.
Category:
E-Commerce
AIs explosive growth depends on a backbone of vast energy-hungry, water intensive data centers, costing hundreds of billions of dollars in resources. The challengeand opportunityof the moment is ensuring this infrastructure scales without hollowing out long-term value. Across the U.S., states are racing to attract data center facilities with lucrative incentives. The promise is economic growth and prestige. The reality is more complicated: hidden costs borne by communities, power grids, and ecosystems. As a venture capitalist focused on hardtech and sustainability, I see this tension as both risk and opportunity. The future of AI will belong to those who reconcile scale with sustainability, building infrastructure that powers innovation without draining the very resources societies depend on. THE ECONOMIC CASEAND THE HIDDEN BURDEN Data centers are capital-intensive projects that can inject billions into local economies. Virginias 300 facilities, for example, contribute more than $9 billion annually. Illinois, with over 180 centers, has positioned itself as a hub thanks to land, fiber, and access to the Great Lakes. At the national level, the market was valued at $302 billion in 2023 and is projected to double by 2030. Its easy to see why elected officials welcome them: construction jobs, tax revenue, and the prestige of being a digital gateway. But beneath the headlines, the numbers tell a different story. Subsidies can exceed $800,000 per job. One Alabama project sought $167 million in tax breaks for just 200 positions. Counties quickly become dependent on the revenue, creating pressure to approve more projects regardless of long-term costs. Meanwhile, resource demands are staggering. A single hyperscale facility can consume a gigawatt of power, enough to supply 750,000 homes. Collectively, U.S. data centers used 17 gigawatts in 2022 and are projected to reach 130 GW by 2030, or about 12% of U.S. electricity demand. That growth is already pushing utility prices higher. In Illinois, residential customers saw rates jump 45% in 2025, while businesses faced nearly 30% year-over-year increases. And with federal rollbacks of clean-energy tax credits slowing renewable development, much of that demand will be met by keeping fossil plants online, which undercuts climate goals. Water use adds another layer. Cooling a single large data center can require 5 million gallons per day, equivalent to the needs of a town of 50,000. Over five years, U.S. facilities are projected to consume 150 billion gallons, straining freshwater systems that are already under stress. These burdens are not abstract. They reshape household budgets, increase business costs, and raise profound questions about equity. In Newton County, GA, residents face water shortages, rate hikes, and a possible water deficit by 2030 linked to data centers in the area. In Virginia, an African American cemetery dating to 1863 is now surrounded on three sides by data centers. The social and environmental costs are real and growing. THE INVESTORS LENS: OPPORTUNITY IN THE CRISIS None of this is an argument against data centers. They are indispensable to AI, and AI is indispensable to the future of industry, science, and society. But the current trajectory is unsustainable. Thats why investors, entrepreneurs, and policymakers should treat this challenge as a growth opportunity. Four areas stand out: Cooling and heat sinks: With cooling consuming more than 40% of electricity, technologies like immersion cooling, direct-to-chip systems, and advanced heat sinks can dramatically reduce energy and water use. Carbon capture and utilization: Data centers can serve as testbeds for capturing emissions at the source and converting them into usable inputs. Long-duration storage: AIs variable load requires resilience. Hydrogen systems, advanced batteries, and other long-duration storage solutions can stabilize grids. Advanced materials and compute efficiency: The frontier is not just bigger GPUs but better architectures, AI-optimized chips, and neuromorphic computing that deliver more performance per watt. These are not speculative bets. They represent areas where margins are high, corporate demand is urgent, and public capital is increasingly aligned with private investment. They are the technologies that will allow AIs infrastructure to scale responsibly. A SMARTER FRAMEWORK FOR GROWTH Reconciling scale with sustainability requires a new playbook. Policymakers, utilities, and investors can align around four principles: Transparency: Require audited disclosure of energy and water use. Conditional incentives: Tie tax breaks to sustainability metrics, not just square footage. Geographic balance: Encourage siting where renewable energy and resilient water supplies are abundant. Community benefits: Ensure subsidies translate into tangible improvements for households, schools, and infrastructure. This isnt about slowing AIs growthits about ensuring it creates lasting value. Done right, data centers can become engines for innovation rather than liabilities. ALIGN SCALE AND SUSTAINABILITY The paradox is clear: AI demands unprecedented computtional scale, but unchecked growth risks raising bills, draining water, and delaying climate progress. The alternative is equally clear: Align incentives, invest in breakthrough technologies, and embed accountability into every deal. The winners of the AI era wont just be those who scale fastest. Theyll be those who scale responsibly and build the infrastructure of a digital future that is as sustainable as it is transformative. Haven Allen is CEO and cofounder of mHUB and managing partner of mHUB Ventures.
Category:
E-Commerce
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