Psych Tech @ Work

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Aug 2026

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Science 4-Hire is now Psych Tech @ Work! - a podcast about safe innovation at the intersection of psychological science, technology, and the future of work. Psych Tech @ Work promotes safe technological innovation and human/machine partnerships as an essential force in creating equilibrium and between psychology and commerce. Maintaining this balance in a time of unprecedented change is essential for ensuring that the future of work is ethical, positive, and prosperous. Creating such a future requires an unprecedented level of interdisciplinary collaboration. With the goal of educating, engaging, and inspiring others through thoughtful and practical discussions with guests from a wide variety of backgrounds and specialties, Psych Tech @ Work provides a smorgasbord of food for thought and practical takeaways about the issues that will make or break the future of work! charleshandler.substack.com

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September 24, 202647 min

You're Not Losing Your Job to AI. You're Handing It Over.

“If you automate chaos you get chaos at scale.” — Tomas Chamorro-Premuzic Episode Overview In this episode I’m joined for a third time by Tomas Chamorro-Premuzic — organizational psychologist, author, and now the first Chief Science Officer at Russell Reynolds Associates , one of the world’s top executive leadership advisory firms. We set out to role play this conversation as if it were happening in 2027. We didn’t stick to it. We kept sliding between the future, the present, and whatever is right around the corner — which says more about this moment than the role play ever could. We’ll come back in a year to see how we did. Underneath the predictions is one argument: machines can only take what has already been standardized. Topics Discussed & Key Insights 1. The Future Is Already Here. It’s Just Not Evenly Distributed. Tomas recently returned from Shenzhen — stores staffed by humanoid robots, selling humanoid robots, with shoppers carrying them out in boxes. No human employees. His point, borrowing William Gibson: “the future is already here, just not evenly distributed.” What looks weird and niche today becomes mainstream fast. That’s the honest baseline for any one-year prediction: 95% of work will look the same next year. The question is which 5% won’t. 2. You Can’t Automate What Hasn’t Been Standardized The core flip of the episode. Automation doesn’t take jobs — it takes standardized work. As Tomas puts it: “you cannot automate what hasn’t been standardized already.” Which raises his uncomfortable question: are knowledge workers standardizing themselves? Leaning on AI for everything produces what he calls “work slop and cognitive surrender” — the intellectual version of elevator music. Designed to blend in. Designed to be ignored. The more predictable your output, the easier you are to model, and the easier you are to replace. 3. Hiring for Potential, Not Track Record His first concrete prediction: organizations will hire leaders less for what they have done and more for what they could do. AI enriches the signals needed to model curiosity, humility, coachability, and EQ — the things good assessment science has been chasing for decades. Technical skills, including the AI literacy everyone is worried about, “will be far less important than we think.” Human and humane skills will matter more. Track record measures what’s already been commoditized. Potential is what’s left. 4. Autonomous Hiring Splits the Market I ran a live query on my TA Tech Navigator during the episode. TA Tech Navigator is my online market research portal that contains 540 profiles of candidate evaluation and assessment tech. It has several frameworks that classify AI use and Science and can run trends reports using the aggregate data. The trend it flagged: fully autonomous hiring stacks are moving from fringe to viable. There are now vendors where no human touches the process until the final decision. Employers are splitting into two camps — buying efficiency or buying defensibility. Is the tool validated? Is there something here you can trust? That gap will widen, with regulation as the wildcard. The Navigator’s one-year read: faster, cheaper, more automated — but not fairer, more valid, or more defensible unless somebody demands it. 5. The Zombie Problems Tomas closes with a warning. Obsessing about the future is often an excuse for not dealing with present problems. Two have been with us for decades, and AI doesn’t touch either one: measuring leadership performance objectively, so tools predict actual performance instead of a popularity contest — and our stubborn preference for intuition over validated assessment. The same world that worships data, science, and validity is the world in which the MBTI is the number one personality assessment. New technology doesn’t kill these problems. Only deciding to solve them does. Final Takeaway Machines don’t take work. They take standardized work. The defensible position — for a knowledge worker, a leader, or a hiring process — is to stay hard to predict: keep thinking, keep surprising, and measure the things that actually differentiate people. That’s been the job all along. AI just raised the price of skipping it. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

August 11, 202653 min

AI Hiring Law: What Changed While You Weren't Looking?

“The way the law is working — the Workday case is exhibit A here — is that vendors are going to be on the hook if their systems are producing biased outcomes. Period.” — Marc Weinstein Episode Overview In this episode I’m joined by Marc Weinstein, an attorney with 27+ years of practice focused on organizations that use tests to make high-stakes decisions about people — licensure, certification, admissions, and employment selection. His work centers on the legal rights of people impacted by AI systems and the organizations that use them. The legal ground under AI hiring is moving fast. In the past few months a state AI law got sued into a rewrite, a court handed down a ruling that shields bias audit data from view, and a new lawsuit opened a line of attack nobody was watching. If you buy, sell, or use AI hiring tools, this episode helps you stay on top of the shifting sands. Topics Discussed & Key Insights 1. First, how to roll: governance before headlines. Marc’s advice starts above the regulations. Your own governance is the key to successful risk mitigation. Get this part right and the rest gets much easier. * Before you track any law, ask why you want to use AI in the first place. If you have good answers, then ask how * Set principles for your organization — not for the whole world, for your organization. Let them drive your use cases, your vendor selection, and your evaluation criteria * Operate by them for real. A policy that lives on paper is worth nothing * Do this and you’re already in compliance with most of the laws that could touch you. Everything below is what’s shifting underneath that foundation 2. Vendor liability is shifting fast — get ready now. For a long time, vendors selling hiring tools got a pass. How the customer used the tool was the customer’s problem. That arrangement is breaking down. * In Mobley v. Workday, the court is letting claims proceed on the theory that the platform acts as an agent of its employer customers — which puts the same federal anti-discrimination laws that apply to employers on the platform * Plaintiffs go where the deep pockets are. Mega vendors like Workday and 8fold are the deep pockets * Employers are still independently on the hook. Vendor liability adds to yours, it doesn’t replace it * Newer legislation points the same direction, and California’s new regulations make bias testing itself evidence in court — more on that below * Since we recorded: the court refused to dismiss most of the remaining claims against Workday. The case keeps moving, and the core theory keeps surviving 3. Bias audit data can be withheld — and that says a lot. A discovery ruling in the Workday case protected the company’s bias testing data from the plaintiffs. Worth sitting with what that means. * The court held the data was privileged because lawyers curated it and the testing was done to provide legal advice — not for business use * Publicly advertising that you conduct bias testing did not waive the privilege * So the people alleging harm from the system may never see the data that would show whether the system harmed them * The bigger question: if the most important evidence about how these tools perform can be shielded, what does accountability actually look like? Proof may have to come from other directions 4. There are new ways to hold these tools accountable. The Eightfold case shows creative lawyering finding routes into AI hiring that didn’t exist a year ago. * The claim is built on the Fair Credit Reporting Act and privacy law — the argument is that AI-generated candidate scores are consumer reports, compiled without the required disclosures and consent * These theories don’t require proving discrimination as a legal element, which makes them easier cases to bring * The target hasn’t changed. It’s still about how these tools treat people. There are just more ways to get there now 5. Colorado is the cautionary tale: get aggressive, get crushed. Colorado passed the most rigorous state AI law in the country — mandatory risk management, impact assessments, a real duty of care. Then it got taken apart. * xAI sued to block the law. The DOJ intervened on xAI’s side — the first time the federal government moved to invalidate a state AI law * The legislature rewrote the law under pressure. The replacement is a notice-and-transparency framework, delayed to January 2027 * The lesson for everyone else: states that regulate AI aggressively are going to get sued by the federal government and the private sector. Plan around that reality 6. Meanwhile, California quietly became the strictest. While everyone watched Colorado, California put real requirements on the books — and nobody has sued to stop them. * Under the FEHA regulations, conducting a documented bias test supports an employer’s defense in discrimination litigation. Not conducting one supports the plaintiff. Records must be kept four years * The privacy agency’s automated decision-making rules take effect January 2027: pre-use notice to applicants and employees, opt-out rights, and the right to demand how a system reached a decision about you * That last one matters. Can anyone actually explain how a frontier AI model reached its decision? For most of these tools, the honest answer is no Final Takeaway The laws will keep shifting. Colorado got rewritten, California’s deadlines are coming, and the courts are redrawing vendor liability case by case. You can’t chase all of it. Build the governance foundation, make vendors show you their evidence — your data, not just their study — and treat every recommendation a machine makes as the decision it really is. Good governance gets you more than 90% of the way there, as long as it’s not just lip service. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

June 29, 202645 min

You Bought the AI. So Why Aren't You Using It?

“You buy the hiring platform, and then you don’t have these AI workflows enabled... here you are three, four years later. You’re still not able to do that.” — Nicole Mundy Episode Overview In this episode I’m joined by Nicole Mundy , Senior Research Analyst at Talentech Labs — a research and advisory firm that helps its enterprise clients evaluate and procure hiring technology systems. Nicole brings a valuable perspective to the table because she sees the dynamic between vendor and buyer up close and personal. When combined with my experience in this same realm from the science side- our discussion shines light on the reality of what is happening in AI tech adoption for TA. Topics Discussed & Key Insights 1. Companies are buying AI hiring tools at scale — and then leaving them switched off. Among the world’s top enterprise organizations, Nicole estimates a surprisingly small amount are actually using AI to automatically assess active applicants. Why? Approvals never come - Companies buy the platform intending to enable the AI, “once the right approvals are in place- but years often pass without any change. Pilots underperform - Big companies test these tools and often conclude they can’t really use them, or they just didn’t work. Lack of solid ROI evidence - Despite the best intentions- most companies do not do the follow up work needed to demonstrate the impact of these tools on the bottom line. Legal ambiguity freezes decisions - With regulations constantly in flux, risk management often takes priority over business needs. 2. Validation is misunderstood and absent. What vendors without I/O science guidance call validation isn’t what legal compliance actually requires. * Vendors are quick to speak about the validity of their solution and talk endlessly about validating their AI models — running statistical checks that the model predicts consistently and de-biasing its outputs across groups. This is purely empirical work. * But that’s IT-style validation. It confirms the system runs as built; it says nothing about whether the tool is fair or job-related * Validation for legal compliance, and sound science, demands a blend of rational and empirical work to document the job-relatedness of any tool used to make employment decisions 3. “Skills” are everywhere, and nowhere. Skills-based hiring is the headline everyone wants. The problem is what counts as a skill. * Most platforms apply the “skill” label with no objective framework to define it. A skill ends up being little more than a tag like “Excel” or “communication” * The definitions behind these labels are usually poorly organized and loosely constructed. * There is no connection between the skills a platform claims to measure and any outcome on the job. Without that link, there is nothing for a buyer to trust or defend. * The companies doing skills-based hiring well are not buying one vendor and flipping a switch. They run multi-year programs: define the skills objectively, inventory what the organization has against what it needs, curate tools carefully on the front end, and collect assessment data at multiple points. 4. Cheating is a zero-sum game, so let’s change the rules AI-assisted candidate fraud, from AI completing assessments to coaching candidates through interviews, is driving enterprises toward more dynamic evaluation that is harder to game. But chasing detection is largely a losing battle. * Trying to catch and block AI use is whack-a-mole. It’s a zero-sum game, and it’s frustrating, because the tools keep getting better and the detection never really gets ahead * There’s a more useful way to think about it. People are going to use AI on the job, so why not let them use it in the application process in a controlled way? The question stops being “did they use AI” and becomes “how well do they use it” * Most large organizations are still just trying to get visibility on how much cheating is happening and what it looks like, which says how early everyone is on this. * Across the approaches Nicole sees, one thing tends to hold up whether a process is locked down or fully AI-assisted: competency-based follow-up questions that make candidates explain their own reasoning in their own words. Final Takeaway Enterprise isn’t slow on AI in hiring because it doesn’t understand the technology. It’s slow because the tools are bought on vendor claims that were never reviewed against real science, and the danger only becomes clear once the tool is in play. The companies getting it right aren’t chasing tools. They’re building programs, science first. Held to that bar, many of the tools on the market today wouldn’t survive the review, and the ones that would wouldn’t be sitting switched off. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

June 4, 202654 min

“The answer isn’t more AI — it’s better signal.”

In this episode I’m joined by Robert Newry , Founder & CEO of the assessment company Arctic Shores and long time champion of doing assessment right! Robert and I (and my AI co-host Mayda Tokens!) dig into one of the most urgent problems in hiring right now: the complete breakdown of traditional hiring signals. We ponder the question- “How do we find the truth in an age where AI has flooded the top of the funnel, made credentials and resumes unreliable, and put enormous pressure on organizations to find new ways to identify talent?” And we come up with some pretty good answers! 1. The Top of the Funnel Is in Chaos The numbers are staggering. Accenture’s global resourcing lead told Robert they’re on pace for 12 million applications this year for roughly 100,000 hires — up from 4 million just three years ago. Same size team. Two and a half times the volume. The culprit isn’t a surge in qualified candidates; it’s AI-powered application tools that let candidates apply to jobs while they sleep. The moral contract between candidates and employers has been broken: candidates assume companies are using AI to screen, so they’re using AI to apply. “It’s chaos out there. Candidates are using AI to fight AI — and we’re in a no-win scenario.” 2. Traditional Assessment Is Increasingly Gameable Arctic Shores’ research from 18 months ago showed what most people didn’t want to admit: AI can ace virtually any traditional assessment format — personality tests, cognitive reasoning, multiple choice — with ease. And it’s not just about having a second screen open. Candidates can now point a phone at their screen, have the AI read the item, and get the answer instantly. Proctoring doesn’t solve this. The old protection mechanisms are obsolete. 3. The Answer Is Better Signal, Not More AI The solution isn’t to ban AI from the process — it’s to design assessments that AI can’t easily game because they’re rooted in authentic behavior. Robert’s framework: if AI is being used to evaluate signals, those signals have to be grounded in high-fidelity behavioral data — not scraped from job descriptions, not inferred from keyword matching, not built on garbage in. Job descriptions themselves are often the first failure point, and no amount of downstream AI sophistication fixes a weak foundation. 4. Stop Counting Leaves — Look at the Roots Robert’s tree analogy is one of the sharpest frameworks in this episode. For decades, hiring has been obsessed with leaves — the skills on a resume, the credentials on a LinkedIn profile. But with the average shelf life of a skill now estimated at two and a half years, leaves are increasingly unreliable. What matters is the root system: the durable human capabilities that allow someone to grow new skills, adapt to changing roles, and thrive in uncertainty. 5. Skills-Based Hiring Needs a Clearer Definition of “Skill” Both Robert and I agree: the skills-based hiring movement is directionally right, but conceptually messy. Calling “innovation” or “persistence” a skill conflates what can be learned with what is innate. Durable traits — personality, cognitive style, learning orientation — don’t expire the way technical skills do. Measurement strategy has to account for these differences, or skills-based hiring just becomes the next echo chamber. Final Takeaway The hiring signal crisis is real — and it’s accelerating. AI has made it trivially easy to fake credentials, game traditional assessments, and flood the funnel with noise. The organizations that receive the best signal won’t be the ones that deploy the most AI. They’ll be the ones that invest in the right signal: behavior-based, validated, and rooted in the durable human traits that no machine can fake. *Claude.ai assisted with the creation of these show notes This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

April 27, 202648 min

Going All In: Using AI to Build Better Assessments

“By the time you dot the final I’s and cross the final T’s, the assessment is already out of date.” — Taylor Sullivan Episode Overview In this episode I’m joined by rising I/O rockstar Taylor Sullivan , IO psychologist and the architect of Workera’s assessment strategy. With Taylor’s guidance Workera, a verified skills intelligence platform, is doing something most of the industry is still afraid to do: going all in on using AI to build, deliver, and validate AI-based assessments. Taylor and I (and my AI co-host Mayda Tokens) dig into how this actually works, why it’s scientifically defensible, and why the industry needs to stop waiting and start moving. Topics Discussed & Key Insights 1. Traditional Assessment Development Is Already Broken By the time a traditional assessment clears all the I-dotting and T-crossing, it’s often already out of date. AI changes that — enabling dynamic content generation, richer construct understanding, and real-time iteration that keeps pace with how work actually evolves. 2. Codifying Measurement Science Into a Multi-Agent System Workera didn’t just bolt AI onto existing processes. They embedded IO psychology’s core principles — evidence-centered design, validity frameworks, job analysis — directly into a multi-agent authoring system. Experts define the standards. Agents execute to those standards. The science drives the machine, not the other way around. Here’s a brief sketch of how it works in practice * Define the purpose — Tell the agent what you’re measuring and why. This grounds everything that follows. * Extract the construct — The agent probes the skill space using critical incident techniques, identifying what great performance actually looks like. * Design the assessment — The agent selects question formats (multiple choice, drag and drop, voice interaction, sequencing) based on what will best elicit evidence of the skill. * Automated quality review — Before anything goes live, the system checks for bias, language issues, and content alignment to the original skill definition. * Monitor and improve — Once deployed, the agent tracks response patterns, flags problems, and learns from score appeals adjudicated by humans. The skill domain is flexible — it works for cheeseburgers or cybersecurity. The methodology behind it is the same either way. 3. The “Harness” — Why This Is Safe The key to responsible agentic AI isn’t less autonomy — it’s a well-designed harness (the constrained ecosystem where the agents do their thing). Human experts define what good looks like, set quality thresholds, and build in escalation points. The agents work within those constraints and loop back when they hit uncertainty. As Taylor puts it: “It’s not running completely autonomously unchecked.” 4. This Is About Development, Not Just Hiring Workera’s primary focus is post-hire — workforce development, upskilling, and learning. Once an assessment identifies verified gaps in a person’s skills, the platform connects those gaps directly to personalized learning plans, curating from an organization’s existing content library. Two people can get the same score on an assessment and walk away with completely different development paths based on their specific pattern of strengths and gaps. 5. Verified Skills Intelligence — What It Actually Means In a world where AI can write a perfect resume and LinkedIn profile for anyone, credentials are noise. Verified skills intelligence cuts through that — using assessment to generate actual evidence of what someone can do, fit for the stakes of the decision being made. Final Takeaway The tools to move beyond multiple choice, beyond static assessments, and beyond slow validation cycles exist today. The bottleneck isn’t technology — it’s the will to trust well-designed systems. When the science is built into the machine from the start, speed and rigor aren’t in conflict. They’re the same thing. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

March 30, 202658 min

Quality Research Shows the Real Impact of AI @ Work

Quote: “If you know what you’re doing, AI makes you faster. If you don’t, it just makes you wrong faster.” –Louis Hickman In this episode I’m joined by esteemed Psych Tech @ Work , Alumnus and AI research machine, Louis Hickman. Our incredible conversation taps into Louis’ myriad research studies to unpack AI’s direct impact on work, domain expertise, and talent assessment. And of course, this episode also marks the return of the now new and improved AI podcast co-host Mayda Tokens (2.0). Besides telling dumb jokes- Mayda’s job is to remind us that AI isn’t just a tool — it’s becoming an active participant in how we think, question, and explore ideas . In the course of our conversation Mayda and I coax some PROFOUND take aways from our friend Louis as he shares the practical outcomes of his research: 1. AI is not removing the need for expertise — it’s making it more visible. Scaling intelligence is easy.Scaling judgment is not. The organizations that succeed won’t be the ones that adopt AI the fastest. They’ll be the ones that: * Understand what they’re measuring * Use AI to enhance — not replace — that understanding’ * Maintain control over how decisions are made 2. AI allows us to scale both good science and bad measurement Louis pushes back on the idea that recent advances represent a fundamental shift in how we measure people. Instead, what we’re seeing is: * Better models * Faster processing * More scalable systems But none of that replaces the need for valid, reliable, and job-relevant measurement . 3. AI doesn’t level the playing field — it often rewards those who already understand the game. One of the most interesting ideas in this episode is how AI interacts with individual differences in expertise . At a high level: * For simple tasks , AI helps novices perform closer to experts * For complex tasks , AI actually widens the gap- allowing experts to perform better Why? Because experts know how to ask better questions, recognize when AI is wrong, and refine its outputs—while novices often lack the ability to judge quality, diagnose errors, or course-correct when things go off track. 4. Replicability in LLMs Is Possible — if you know how to set it up right A major “wow” moment in Louis’ research: By running the model locally on the same class of hardware, fixing the model and prompt, and turning off sampling/randomness in the settings, you can make the system produce the same output for the same input every time. 5. AI should be used to scale decisions, but those decisions still need to be grounded in clearly defined constructs At this point, AI adoption isn’t optional—it’s expected. Organizations are being pushed to move faster and scale, while vendors are rapidly building and deploying solutions, often without deep validation. The resolution isn’t to slow down adoption—it’s to ensure we add and maintaining rigor . 6. AI makes it easy to scale assessment, but if the underlying design is weak, we’re just scaling bad measurement faster. The resolution is to ensure what gets scaled is built on clear constructs, strong design, and validated measurement , so speed amplifies quality—not noise. 7. Working with AI is no longer just about what you can do—it’s about how effectively you can partner to make what you do better! The tension is clear: AI can accelerate work, but over-reliance without critical evaluation leads to lower quality, missed errors, and reduced trust. This shows up in real ways—unchecked outputs, declining attention to detail, and growing skepticism in collaborative work. The resolution is that AI doesn’t replace accountability—users still need to apply judgment, review outputs, and take ownership of the final result. Tune in to get the full story on these profound revelations and hear Mayda’s stand up comedy routine. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

February 23, 202648 min

The Truth About AI Based Talent Assessment

“The rules haven’t changed. The technology has — but the rules haven’t.” — Nathan Mondragon Episode Overview In this episode, I’m joined by my old friend (and now co-worker!) Nathan Mondragon , an IO psychologist and long-time leader in creating the future at the intersection of assessment science, hiring technology, and applied AI. Nathan and I have lived through multiple waves of “this will change everything” technology — from early online testing to video interviewing, machine learning, and now generative AI. And the beat goes on! Nathan and I have recently joined forces at ProboTalent where we are creating defensible AI based assessment tools. We talk about where AI has genuinely moved the field forward, where it hasn’t, and why so many of the debates we’re having today are versions of conversations we’ve been having for decades. Along the way, we unpack Nathan’s paradigm busting work at HireVue’, and why the fundamentals of good measurement haven’t changed — even as the tools have. Topics Discussed & Key Insights 1. The Rules of Good Assessment Haven’t Changed — We Just Keep Forgetting Them Nathan makes a point that anchors the entire episode: while technology has advanced dramatically, the core rules of good assessment — validity, relevance, interpretability, and fairness — are exactly the same. AI doesn’t get a pass on methodology. If anything, it raises the bar for rigor, because mistakes scale faster. 2. Early Hiring Tech Was Built to Solve Operational Problems, Not Measurement Problems We talk about the early days of online hiring and assessment, where the primary goal was digitization , not insight. Systems were designed to move paper processes online, not to improve how well we understand people. That legacy still shapes today’s platforms — and explains why so many tools feel efficient but shallow. 3. HireVue Was a Real Paradigm Shift — and It Required Scientific Courage Nathan reflects on the early days of HireVue and why it was genuinely revolutionary at the time. The breakthrough wasn’t just video — it was the larger shift toward digitizing and scaling structured assessment experiences in a way the field hadn’t seen before. What made this moment interesting from an IO psychology standpoint is that it required a different mindset as a scientist: being willing to engage with a new modality, even when the measurement implications weren’t fully understood yet. Innovation in assessment has always involved tension — between rigor and experimentation, between what’s proven and what’s possible. Nathan shares what it was like to help lead through that transition, and why thoughtful scientists have to be able to sit with uncertainty long enough to shape new approaches responsibly, rather than rejecting them outright. 4. AI Didn’t Create Bad Measurement — It Made It Easier to Scale A recurring theme: AI doesn’t magically improve weak constructs. If you feed it noisy proxies, you just get faster, more confident noise. We discuss why generative AI and machine learning don’t eliminate the need for careful construct definition — and why “it correlates” is not the same thing as “it measures something useful.” 5. Interactivity Matters More Than Modality One of the most important takeaways: the future of assessment isn’t about whether something is text, video, or simulation-based — it’s about how interactive and information-rich the experience is . Nathan explains why dynamic interaction reveals far more about decision-making, reasoning, and capability than static prompts ever will. 6. Native AI vs. Embedded AI Is a False Debate We unpack the difference between “AI-native” products and traditional tools with AI layered on top — and why this distinction often misses the point. What matters isn’t where AI lives in the stack, but whether it’s being used to improve interpretation , not just automate scoring or classification. 7. Skills and Knowledge Are Still Hard to Measure — and AI Has to Be Used Carefully We close by confronting a reality the market often underestimates: skills and knowledge testing have always been difficult to do well , and scaling them without losing rigor is even harder. We connect this directly to the work we’re doing at Probo Talent , where the focus is on a more responsible alternative: using AI to scale the parts of assessment that have historically been hardest to scale, while staying within safe, established modalities and an explainable, scientifically grounded wrapper. The goal is not novelty for its own sake, but a practical example of how AI can be used carefully to solve long-standing problems in skills-based hiring without sacrificing defensibility or trust Final Takeaway AI changes how we can build hiring and assessment systems — but it doesn’t change what makes them good . If we ignore decades of psychological science in favor of speed, novelty, or convenience, AI will simply help us make the same mistakes faster. But if we use it to deepen interaction, improve interpretation, and stay disciplined about what we measure, it has the potential to finally move the field forward in meaningful ways. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

January 19, 202647 min

Why Recruiting Tech is (Still) Not Helping Candidates and How to Fix It

“There’s this massive imbalance between the employer side of the recruiting equation where they’ve got all the tech, they’ve got all the weapons… Candidates don’t have anything.” –Doug Berg In this episode, I’m joined by Doug Berg , head matcher and big kahuna at Match2 , a longtime builder and operator in the talent technology/recruitment space and the only guy I know that wears flip flops to HR Tech.. Doug has lived and hacked nearly every iteration of online hiring — from fax machines and early internet job fairs to today’s AI-powered recruiting chaos. Doug and I have lived parallel lives in some sense. We have both been on the scene as recruitment went on-line and have continued to wage war against the barriers that are blocking successful hiring. But Doug’s unique experience building recruiting focused tech helps us take a very well rounded perspective. Doug and I trace the psychology of hiring systems , why most recruiting technology still fails both candidates and employers, and how efficiency-driven design has quietly stripped humanity out of the process. We talk about what broke, why AI is making some problems worse before it makes them better, and what a candidate-centered future could actually look like if we stop designing hiring like a transactional funnel and start designing it like a relationship. Topics Discussed & Key Insights 1. Hiring Has Always Been Psychological — Ignoring That Is Why It Breaks Doug shares early recruiting stories that reveal a core truth: people don’t make job decisions based solely on skills or titles. They’re driven by values, aspirations, lifestyle preferences, and identity. Yet most hiring systems still treat people as static records instead of dynamic humans. Music to the ears of a psychologist like me! 2. Applicant Tracking Systems Were Built for Control, Not for Candidates We unpack how applicant tracking systems were designed for compliance and efficiency — not engagement. The result: * One-way transactions * Forced applications * Zero room for curiosity, context, or conversation Doug explains why this original design choice still haunts modern hiring. 3. AI Isn’t Breaking Hiring — It is Amplifying the Broken Parts AI didn’t invent hiring dysfunction — it amplified it. Candidates now apply to dozens of jobs at once using bots. Employers respond with more screening, more filters, more automation. The outcome? More noise. Less signal. Worse experiences on both sides. 4. Real Hiring Happens Through Interaction, Not “Efficiency” Doug tells stories about simple interventions — like proactive chat on career sites — that led to real hires for impossible-to-fill roles. The lesson is clear: when candidates are allowed to participate instead of comply, hiring actually works. 5. Hiring Will Stay Broken Until Candidates Control Their Side of the System One of the central ideas in the episode: candidates have never been given real agency. Doug explains the structural imbalance: * Companies control the systems * Candidates adapt or disappear We explore what changes when candidates control their own data, preferences, and relationships — and why that shift matters. 6. The Resume Is a Dead Artifact — Identity Needs to Be Portable Resumes are outdated snapshots. Doug makes the case for living profiles, portable personalization, and persistent relationships that move with the candidate across employers. AI finally makes this possible — not by enforcing rigid taxonomies, but by interpreting relevance across skills, experience, and context. 7. The Future of Hiring Should Feel Like Reconnection, Not Rejection We close by zooming out. Doug shares a simple but radical vision: if someone gets laid off on Friday, they shouldn’t start from zero. They should already know: * Who wants them * What they’re worth * Where they fit Hiring shouldn’t feel like rejection roulette. It should feel like an intelligent market reconnecting human supply and demand. Final Takeaway Hiring doesn’t fail because people are hard to assess. It fails because we designed systems that ignore how people actually choose, trust, and engage. AI won’t fix that on its own.But used thoughtfully — with psychology, agency, and dignity baked in — it might finally help us build hiring systems that work for humans again. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

December 19, 202544 min

AI Education, Personalized Learning, and the Future of Work

TL;DR AI literacy is becoming a baseline skill. This episode explores how organizations and individuals are actually building AI capability at work, with a focus on: * Self-directed learning and AI education at scale * Personalized learning journeys versus one-size-fits-all training * The shift from basic AI use to agentic workflows * The role of human strengths—creativity, judgment, and adaptability—in an AI-driven workplace In this episode, I’m joined by Erica Salm Rench , an AI educator and leader at Sidecar AI . Sidecar is an AI education platform and learning management system (LMS) designed to help organizations educate their employees on AI through self-directed learning. It combines structured courses, role-based learning paths, and hands-on use cases so individuals can build AI capability at their own pace while organizations raise overall AI fluency. Our conversation explores what AI education actually looks like beyond hype—how people are learning it, how organizations are rolling it out, and why understanding AI is quickly becoming a career differentiator rather than a technical specialty. AI Education Has Shifted from “What Is It?” to “How Do I Use It?” Erica explains that the conversation around AI in associations has changed dramatically over the last several years. Early on, organizations were hesitant to even talk about AI. Today, the question is no longer what is AI? but how can we use it to advance our mission, improve operations, and better serve our members? That shift brings a new challenge: helping people move from curiosity to competence in a way that feels approachable rather than overwhelming. Meeting People Where They Are One of the strongest themes in our discussion is the importance of meeting learners at their current level of comfort and knowledge . AI education isn’t one-size-fits-all. This means combining: * Foundational AI concepts * Role-specific applications (marketing, events, operations) * A growing library of real-world use cases * Ongoing updates as tools evolve The goal isn’t to turn everyone into a AI engineer—it’s to help people understand what’s possible and apply AI meaningfully in their day-to-day work. From Prompting to Agentic Work We spend time talking about the evolution from simple AI use cases—like writing emails or summarizing content—to agentic AI , where systems take action on a user’s behalf. This shift matters because it fundamentally changes how work gets done. Instead of just assisting with tasks, AI begins to: * Automate multi-step workflows * Scale work that previously required human labor * Act as a force multiplier rather than a one-off toolWe agree that while much of this is still clunky today, the direction is clear: agents are becoming a core part of how work will be organized. Personalized Learning Is the Future of Education A major insight from the episode is that personalized learning journeys will define the next phase of education—especially in fast-moving domains like AI. Erica describes how Sidecar uses AI within its learning environment to: * Act as a learning assistant * Answer questions in real time * Reinforce concepts * Help learners connect theory to application This mirrors a broader trend: education becoming less about static courses and more about continuous, adaptive support . The Psychology of Learning AI at Work We talk openly about fear—fear of job loss, fear of falling behind, fear of not being “technical enough.” Erica makes the case that leaders have a responsibility to educate their teams, not just for organizational performance, but for people’s long-term career resilience. From a psychological perspective, AI education: * Reduces anxiety by replacing uncertainty with understanding * Increases confidence and autonomy * Helps people see AI as a collaborator, not a threat Spending even 20–30 minutes a day learning AI can quickly change how people see their own future at work. Human Strengths Still Matter More Than Ever One of my favorite parts of the conversation is where we zoom out to the human side of all this. As AI removes technical barriers, the differentiator becomes human qualities —creativity, resilience, judgment, adaptability, and the ability to ask good questions. AI doesn’t replace these traits. It amplifies them. Used well, AI allows people to overcome past limitations, work around weaknesses, and bring their ideas to life faster than ever before. What Listeners Should Take Away AI literacy is becoming a baseline skill. The people who thrive won’t be the most technical, but the most curious, adaptable, and intentional about learning how to work alongside intelligent systems. Education—done thoughtfully and continuously—is the bridge between fear and opportunity. Where to Find Erica Erica is highly active on LinkedIn and can be found through Sidecar AI , where she and her team are building education-first pathways into AI for associations, nonprofits, and mission-driven organizations. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

November 21, 202556 min

Jobs, Security, and Survival: Is Universal Basic Income in our Future?

Conrad Shaw “So much of the labor market is driven by desperation. UBI shifts that. People can actually hold out for what they’re worth or for work that aligns with who they are.” — Conrad Shaw Conrad is perhaps the most unique guest I have had in the 5 year history of this show and he is on to talk about Universal Basic Income (UBI) , a very unique topic that is growing in exposure. For almost a decade Conrad has dedicated his life and career to furthering the cause of Universal Basic Income (UBI). In 2016 he and his wife started a documentary called Bootstraps which focuses on following families who lived through the experience of a basic income. Since then, he has: * Fundraised for and operated a nationwide basic income pilot * Filmed a multi-year docuseries currently in post-production * Co-founded Commingle , a mutual-aid platform enabling communities to self-fund their own grassroots basic income systems * Worked extensively on messaging, outreach, and public education around income, stability, and societal transformation I learned a lot from Conrad and our conversation debunked my own myths about UBI. So a really important part of this episode is the truth about what Universal Basic Income (UBI) actually is — and what it is not . What Universal Basic Income (UBI) Is — And What It Isn’t UBI is the idea that every person receives a recurring, unconditional, baseline income — a financial floor that ensures no one starts the month at zero. It is not meant to replace work or equalize everybody’s income. Instead, it shifts the starting point so people can make decisions from stability rather than desperation. What UBI is : * A stable, universal base-level income for all * A platform for economic mobility and personal freedom * A modernized, simplified social safety net * A tool for reducing the survival-based pressure in the labor market What UBI is not : * It does not eliminate jobs * It does not cap how much people can earn * It does not remove incentives to work * It is not a socialist equal-wealth system UBI reframes the labor market so people compete for work based on interest, alignment, and ability , not raw financial need. Practical Ways UBI Could Work Conrad’s work goes beyond speculation. He has spent nearly a decade building practical UBI experiments, including the national pilot documented in Bootstraps (2016) and his current role with the Income To Support All Foundation and Commingle , a new community-driven model. He explains that UBI can be implemented through several pathways—government programs, private pilots, or community-level mutual aid—but none are simple. A government-led UBI requires political will and rethinking how we allocate resources. Philanthropic pilots can demonstrate impact, but they’re temporary. Community models like Commingle allow people to pool and redistribute resources now, without waiting for legislation, but scaling them is challenging. What’s clear is that executing UBI at any level is difficult , requiring trust, infrastructure, and cultural acceptance. Yet the difficulty doesn’t diminish the need. Instead, it underscores why experimentation and new models matter. Individual Differences: Why UBI Supports People Doing What They’re Meant to Do One of the deepest connections between Conrad’s work and mine is the concept of individual differences —the idea that every person brings a unique constellation of strengths, traits, interests, and abilities that make them naturally better suited to certain kinds of work. When people are trapped in survival mode, those natural gifts often go unused. They pick jobs they can get, not jobs that reflect who they are. Freedom from this paradigm reshapes careers in ways that benefit both individuals and employers, allowing people to walk away from toxic or exploitative conditions and take jobs they genuinely care about, leading to better performance and engagement. With a secure foundation, people have the psychological and financial freedom to make career decisions based on fit , not fear. This supports: * Better alignment between person and role * Higher engagement and intrinsic motivation * Better workforce outcomes because people choose work that matches their abilities * Greater societal value , as more people apply their genuine talents instead of defaulting to whatever job pays immediately From Conrad’s perspective, this alignment is one of the most compelling aspects of UBI. When people are free to choose work that resonates with their abilities, the labor market becomes more efficient and more human. Employers gain workers who actually want to be there. Individuals gain a sense of purpose rooted in their authentic strengths. In a world where AI, automation, and job volatility make career paths uncertain, helping people express their natural abilities becomes more important—not less. How AI Fits Into the UBI Conversation AI enters this conversation as both a catalyst and a complicating force. As Conrad points out, technological change is accelerating so quickly that we can no longer predict which jobs will exist, which skills will matter, or how stable any given career path will be. This uncertainty puts enormous pressure on individuals—especially those who don’t have the luxury to retrain, take risks, or weather employment gaps. UBI provides a stabilizing infrastructure in that landscape, giving people the freedom to adapt as work evolves rather than being overwhelmed by it. AI serves the UBI concept well because it highlights the importance of individual differences : as routine tasks get automated, the value of uniquely human abilities—creativity, empathy, problem-solving, and deep domain expertise—rises. UBI supports people in discovering and developing those strengths, while also offering society a buffer as AI reshapes industries faster than institutions can respond. In this way, AI doesn’t replace the need for UBI—it makes the case for it even stronger. Why Making UBI Work Matters in an Uncertain Future We must acknowledge the reality: we are entering a period defined by instability—rapid technological change, unpredictable job markets, and widening gaps between opportunity and access. In such an environment, the old assumptions about steady careers, stable industries, and predictable pathways no longer hold. UBI becomes a tool for resilience. It doesn’t solve every problem, but it gives people the space to adapt, learn, and navigate a chaotic future without falling into crisis. It creates room for people to pursue what they’re best suited for, rather than what pays the most simply out of need. The conversation frames UBI not as a political ideology but as a human-centered adaptation strategy —a way to strengthen psychological well-being, improve labor market alignment, and provide society with a more stable foundation as the world accelerates around us. The truth is that UBI isn’t easy; it’s a fight against gravity in a system not built for change, but we are entering into an unprecedented level of uncertainty in all aspects of our lives- so we need to have creative and idealistic solutions This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com