HR Lessons from Education and AI Study

In this week's episode, Nick sits down with Dr. Jamey Maniscalco, a neuroscientist, former tenured professor, and founder of a brain health company and consulting firm, and host of the Next Door Neuro podcast. They discuss why small daily habits beat big wellness programs, how to make wellness stick by tying it to a deeper purpose, and why employees often just need permission to take a break.Key TakeawaysWellness is most motivating as a means to something you care about, like being a patient parent, rather than a goal in itselfSmall, achievable steps release dopamine, which builds motivation for the next step and makes lasting change more likelyPracticing short stress-management skills daily makes them habitual for the moments you need them mostEmployers tend to overestimate big wellness events and underestimate small daily changes like walks, water breaks, and stepping away from screensJamey predicts the era of biohacking and human optimization is fading, giving way to rest, time outdoors, and in-person connection

In this week's episode, Nick sits down with longtime friend, Richard Lee, founder of fintech startup Domeo and former Goldman Sachs and Capital Group alum who helped scale fintech LSQ to a 2024 exit. They discuss what it takes to build a company in a post-AI world, whether customers will simply vibe code their own software, and how career growth works when teams stay small and flat. Key TakeawaysRichard estimates his seven-person AI-native team is about 10X more productive than the 40-person team that built a similar product a decade agoSoftware will get vibe coded, but in finance and healthcare, where the margin for error is zero, trust is still the biggest barrierIn a world of AI abundance, taste, design, and the human touch are what set companies apartMost CFOs can't name a software product they're happy with (Excel aside), a trust problem that predates AIRichard believes his revenue could 10X without adding headcount, while Nick notes many employees still see managing people as the clearest path to career growth

In this week’s episode, Nick sits down with Barbara Zabawa, founder of the Center for Health and Wellness Law, law professor, and host of the Wellness Law Podcast. A former insurance company attorney who carved out a specialty that barely existed, Barbara brings rare clarity to the legal risks hiding inside employer wellness programs. The conversation covers why tobacco surcharge lawsuits are the new compliance frontier, what AI-generated legal documents actually look like to a practicing attorney, and a new employee benefit concept she believes could address structural barriers to health. Key TakeawaysThe wave of ADA and GINA lawsuits that hit employers like Honeywell and Yale University in the mid-2010s are no longer the lawsuits employee wellness programs are facing todayOver 40 tobacco surcharge lawsuits are currently pending against Fortune 500 companies, alleging that employers are not properly applying the Affordable Care Act’s reasonable alternative standard, specifically by reducing surcharges only going forward when an employee qualifies rather than reimbursing retroactivelyWithout an objective EEOC threshold for what makes an incentive coercive, the best thing employers can do is survey their own workforce: voluntariness is subjective to the employee, not the employer, and a modest reward for a high earner can feel compulsory to someone earning an hourly wageLawyers are encountering AI slop firsthand when clients present them with AI-generated memos expecting a quick sign-off, but a lawyer still has to verify every line and correct every error, which typically takes longer and costs more than if the client had simply asked their question directlyBarbara's academic research focuses on wellness legal partnerships: an employer-sponsored but employer-independent benefit staffed by lawyers, social workers, and health coaches who help employees address the social and structural barriers to health that traditional wellness programs cannot reach

In this week's episode, Nick sits down with Deb Haas, founder of AI Confidential. A former Accenture consultant who built an AI coaching and consulting business from scratch, Deb brings a refreshingly unfiltered perspective on AI fluency, who actually has the advantage in the AI era, and what companies are getting dangerously wrong about adoption. Key TakeawaysA PwC survey of nearly 50,000 workers found more than half feel they are falling behind on AI, but Deb argues most people dramatically overestimate how advanced their peers actually are, and that nobody is truly behind in a technology this newPwC's four AI fluency tiers show the Engine Room (workers who don’t have specialized skills and who are also “not far along” in AI learning) makes up 56% of the workforce, creating a structural problem for any organization trying to become AI-forward while simultaneously cutting training budgets Deb considers AI a language tool, not a tech tool, arguing that if you can write or speak plain English, you have everything you need to use it effectivelyOlder workers and women may have a genuine advantage in the AI era because soft skills (e.g., judgment, empathy, reading the room) are exactly what is needed to evaluate AI output and catch what the machine gets wrongAI slop is a judgment problem, not a technology problem: AI generates the statistical middle of everything it was trained on, and without experienced human review, the result is generic, unreliable output that creates real legal and reputational risk

In this week's episode, Nick sits down with Chris Williams, former VP of HR at Microsoft and leadership advisor to senior executives worldwide. Chris joined Microsoft through the acquisition of FoxPro and eventually oversaw HR for 32,000 employees under Steve Ballmer before branching off into leadership coaching and development. The conversation covers why cutting middle management creates problems companies won't feel for years, how AI can genuinely augment managers without replacing them, why most companies handle layoffs badly, and his prediction that we are currently in an AI bubble. Key Takeaways Flattening organizations by eliminating middle management creates hidden career traps, including employees having little room to develop management skills incrementally, which can result in some leaving for organizations that offer a path to growChris predicts a retention crisis in three to five years for companies making extreme cuts to create flat orgs todayAI cannot replace the interpersonal nuances of management (e.g., writing performance reviews, handling personnel conflicts, recognizing when someone shuts down after a certain type of feedback) but can help managers track communication patterns and manage more people effectively, making 8 to 12 direct reports realistic instead of the traditional 4 to 6Companies are terrible at communicating layoffs: they focus entirely on the 1% being let go while ignoring the 99% who remain and are left anxious, uncertain, and quietly polishing their resumes without any clear communication about what the layoff means for themMuch of what is being attributed to AI-driven layoffs is post-COVID hiring correction: companies that massively over-hired in 2020 through 2022 are now right-sizing, and the AI narrative is a convenient frame for what is also a structural adjustmentChris's prediction: we are in an AI bubble that will correct, similar to the dotcom era—AI is a genuinely powerful tool, but companies making reckless decisions about management layers and headcount will walk many of those back within three to five years

In this week's episode, Nick sits down with Alex Seiler, Chief People Officer and HR expert with 20 years of experience across companies from Blue Shield of California to WeWork. The conversation focuses on two of the most under addressed topics in HR: bereavement leave and caregiving. Alex shares why the standard three-day policy for bereavement leave is a relic of decades-old union contracts, why employees almost never raise caregiving needs with HR, how to build a business case for better policies, and two bold predictions about where workplace grief policies and human-centered culture are heading.  Key TakeawaysThe standard three-day bereavement policy traces back to union contracts from the 1950s (one day for travel, one day for the funeral, one day to drive back) with no input from grief experts or consideration of the actual mourning processThe average US company today offers approximately 5.6 days of bereavement leave, while grief experts now recommend a minimum of 20 days for the loss of someone close—a gap that most employers have not meaningfully addressed in decadesOnly 8% of employees feel comfortable raising caregiving needs with HR, creating a self-sealing cycle: employees don't disclose, data shows no demand, policies don't change, and that outcome confirms to employees that did speak up that it was pointless to do soAlex's prediction: three-day bereavement policies will come to sound as dated as no maternity leave within five to ten years, driven by accumulating state mandates that will force multi-state employers to standardize well above the current floorIn an AI world where technology equalizes output, the remaining differentiator between employers will be how they behave when something goes wrong in someone's life—what you do when someone's mother actually passes away is the test of whether human-centered culture is real or just a recruitment talking point

In this week's episode, Nick sits down with the Oracle of Remote Work, Darren Murph. A former journalist with a Guinness World Record in publishing, Darren pioneered the Head of Remote role at GitLab and now coaches organizations globally on distributed work and organizational design. The conversation covers why the remote versus in-office debate is a false binary, how AI is making remote-first knowledge management unavoidable, and two bold predictions about the future of careers.Key TakeawaysEvery company of scale is a distributed company (multiple offices, floors, and time zones) with the same coordination challenges as remote work, making the remote versus in-office debate a false binary that distracts from the real question: how intentional are you about your work design?The return-to-office wave is best explained by complexity theory: humans revert to their last known state of coherence when exhausted by change, which is why legacy companies with deep in-office culture will likely stay there, and that's okayRemote work failure is almost always a hiring problem: people who lack autonomy and self-direction will underperform in distributed environments regardless of the tools, and building that into your hiring criteria matters more than any policyMeeting hygiene is one of the highest-leverage things distributed teams can invest in; Darren stresses that connection before content, five-minute buffers, and actively seeking disconfirming evidence at the end of every meeting all compound over timeCompanies that ignored knowledge management during COVID are now paying for it as AI that relies on undocumented or conflicting institutional knowledge with no reliable baseline often hallucinates and ultimately and is forcing the knowledge management conversation that should have happened years ago

In this week's episode, Nick and Geoff cover two timely benefits stories. First, EY announces a $100 million bonus pool for employees who demonstrate human skills, a notable signal from one of the world's largest consulting firms about what it still values in an AI-saturated environment. Then, a Business Group on Health survey reveals that 14% of US employers have dropped or plan to drop GLP-1 coverage from their 2027 benefits. Nick and Geoff debate where employer adoption goes from here.Key TakeawaysEY has allocated $100 million in bonuses for employees who demonstrate human or people skills, signaling that in a consulting business built on client relationships, AI cannot replace the value of human judgment and connectionGeoff, a former EY employee, notes that consulting is a business where the partner selling the engagement, putting a narrative around data, and building long-term client trust is doing work that AI simply cannot replicate, making the $100 million commitment less surprising than it might appearHealthcare costs are rising well above general inflation, with health insurance costs up approximately 8.5% last year and projected to rise 9.2% for 2027, creating real budget pressure on employer benefits decisionsA Business Group on Health survey found that 14% of US employers have or plan to drop GLP-1 coverage from their 2027 benefits, a meaningful difference considering 36% cover the drug for diabetes and weight loss (60% cover it for diabetes only)The key difference between enthusiastic GLP-1 adopters like Bank of America and the 14% walking away is likely not disagreement about the drug's efficacy but workforce profile—high-turnover employers bear the medication cost without capturing the long-term health benefits that justify it

In this week's episode, Nick and Geoff discuss a recent study that finds AI dramatically improves students' homework scores and speed but meaningfully hurts exam performance, creating strong lessons for how employers think about talent development. They also examine new research which links the four-day work week directly to lower obesity rates and discuss what employers who cannot yet make that shift can do to capture similar benefits.   Key TakeawaysA study of tens of thousands of students found that AI users saw homework scores improve by 18% but exam scores decline by 20%, signaling that AI is helping students complete work without actually learning the underlying materialFindings from the study translates directly to talent development risks in the workplaceZynga founder, Mark Pincus, recently spoke on the issue's core risk: AI can get you to a B+, but it will not get you to an A—and if you have never done the foundational work, you cannot even recognize what an A looks likeAccording to the Financial Times, an investment bank made fewer return offers to its 2025 intern class after senior financiers found that interns' initially impressive work seemed shallow once probed furtherResearch across 33 OECD countries found a 1% reduction in annual working hours correlates with a 0.16% decrease in obesity ratesA four-day work week represents roughly a 20% reduction in hours, suggesting meaningful population-level health effectsEmployers who cannot implement a four-day work week can pursue similar benefits by stacking smaller interventions: stress management programs, healthy food access, structured breaks, and flexible scheduling all address the same underlying mechanisms without changing the work arrangement itself

In this week's episode, Nick and Geoff dig into two stories. A National Bureau of Economic Research study identifies "LinkedIn time traveling," where candidates retroactively edit old job entries to stuff AI keywords and exaggerate skills. Then the episode turns to new data showing AI is significantly worsening an already acute workplace loneliness crisis, with 44% of employees now choosing AI over collaborating with a colleague. They dig into practical strategies for what employers can do about it.  Key TakeawaysOne in five LinkedIn users engage in "time traveling", the term for a newly uncovered practice of retroactively editing past job entries to add AI keywords, remove DEI references, and inflate skills The behavior is being driven in part by candidates attempting to game AI screening tools, stuffing profiles with keywords designed to pass algorithmic filters, and to make them appear more fit for certain rolesThe US Surgeon General has equated the health impact of chronic loneliness to smoking 15 cigarettes a day, yet most employers are not treating it with anything close to the urgency they would apply to a physical health risk of equivalent scale44% of employees choose AI over collaborating with a colleague, 75% seek advice from AI rather than a coworker, and 37% use AI for brainstorming out of fear of judgment, a pattern that is accelerating workplace isolation The most effective employer response is intentional collaboration: stack projects with multiple people, use AI in shared team settings rather than in isolation, and reframe AI as a team member rather than a private tool