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Imagine living in a house with the latest smart home system: lights dim on voice command, your thermostat learns your schedule, your refrigerator orders milk before the carton runs out. Its practical yet delightful. It improves your daily life. Now imagine that same house built on shaky foundations: the electric wiring is aging, and the plumbing is rotting. No matter how advanced your devices are the structure wont support them reliably. Thats the difference between AI and blockchain. AI is the smart tech; blockchain is the well-designed infrastructure that ensures everything works reliably, predictably, and with integrity. Similar to how a home needs both features and structure, our digital economy will demand both AI and blockchain. One amplifies capability, the other ensures resilience. The real promise of blockchain Just as cloud computing once seemed abstract until it powered every app, blockchain quietly is becoming the fabric woven into how money moves, contracts are enforced, and systems operate with one another. For leaders, innovators, and builders, our job is to see past the surface-level headlines and recognize structural shifts. Thats where we are with blockchain. Paying attention now determines whether youll lead the next economyor scramble to adapt after the rules have changed. Two areas in particular highlight how blockchain and AI together can reshape finance: programmable money and transparency in financial records. 1. Programmable money One of the biggest opportunities is programmable money used by AI-driven agents. Programmable money is digital currency with built-in logic. It can move, settle, or trigger actions automatically based on predefined conditions and rules, releasing payment only when goods are delivered, paying employees instantly after clocking out, or compensating an AI agent the moment it completes a task. An AI agent is a bit like a personal assistant. It interprets requests, figures out how best to deliver on them, and improves over time. They can act autonomously to complete tasks and make decisions to meet your requests. These AI agents dont only work during traditional hours, and they dont have an identity according to traditional regulatory frameworks, yet they need reliable ways to be compensated in real time. Blockchain-backed stablecoins are a natural solution. Consider an example of this powerful combination in real estate: An AI agent confirms the accuracy of documents, closes a transaction, and disburses funds. If its a Sunday morning and payment rails like ACH or wires are closed, how does the transfer clear? Stablecoins enable settlement on digital time, not bankers hours. This is already moving from theory to practice. For example, Google just announced an Agents Payments Protocol, which provides a blockchain-based framework for ecommerce transactions conducted by AI agents. Other leading payment companies like PayPal, Mastercard, and American Express also are embracing this technology. This is a clear example of the intersection of AI and blockchain for a pragmatic use case. 2. Financial transparency Underwriting offers another example of the importance of applying blockchain principles to AI. AI is now shaping how risk is assessed, and loans are priced. That creates real concerns about fairness, bias, and explainability. Regulators and consumers alike want to know why a loan was denied and what data drove the decisions, breaking open the black box of financial decision-making. Blockchain offers an answer. It can record AI-driven activity (data inputs, validation steps, approvals, compliance checks), creating a verifiable record across a variety of consumer products like mortgages or credit cards. Instead of a vague application denied, consumers would know the exact reason, like missing income verification or a high debt-to-income ratio. For approvals, the record would highlight the inputs behind the interest ratecredit score, income, loan-to-valueso borrowers know what mattered most. With the same validated data applied across applicants, regulators can verify fairness while consumers gain clarity. Together, AI and blockchain dont just deliver speed. They deliver financial systems that are more transparent to regulators, more understandable to consumers, and ultimately more durable. The long game belongs to blockchain AI is transformative because it amplifies what we already do. Its the smart home app that learns your lighting preferences based on circumstance. Blockchain is different. Its the building department, inspector, and the wiring behind the walls: setting the rules for what can be built, verifying the work, and powering the entire system. For consumers, opaque financial decisionslike sudden credit card fee increases, shifting interest rates, or unexplained loan denialshave long been the norm. The combination of AI and blockchain is poised to change that bringing clarity to the individual and accountability to the system. For leaders and innovators, the blueprint for the future is straightforward: Prepare for an AI future by building a strong blockchain foundation as rapid growth in transaction activity will demand scalable, composable infrastructure Leverage transparent, fair, and accurate design principles so AI decisions can be explained, verified, and defended. Move beyond pilots by embedding blockchain into mission-critical workflows where speed, settlement, and reliability are non-negotiable. The intersection of AI and blockchain is shaping up to be a massive market reset. Those who embrace it now will define what comes next. Michael Tannenbaum is the CEO of Figure.
Category:
E-Commerce
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
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