“Now tell them we value our employees while laying a quarter of them off via e-mail.”

A meme circulating around based on a scene from The Truman Show is hitting on something we’ve all experienced: the world conspiring against us in a very specific way.

Going to work is starting to feel this way for many folks. As companies reverse course on being people-first, all while doubling or tripling down on AI investment, it feels like a rug pull that’s personal.

I’ve been working on some research, comparing what companies in the S&P 500 said in their 10-Ks in 2021 versus 2025. No shock that the language shifted, with 1 in 5 companies significantly pulling back language that focused on employee experience while amping up AI-focused language.

(BTW, one of the companies with the biggest shifts isn’t Silicon Valley tech at all. It’s Honeywell.)

These shifts aren’t unusual, even as AI feels like a more existential threat to folks we used to call white collar. In 1981 when Jack Welch took over GE, he spent his first four years ruthlessly cutting the organization down. Over 100,000 jobs from one company. All gone.

Industry Week called him “the most acclaimed SOB of the decade.” It was praise, not an insult. It shifted how companies managed people. Widespread downsizing worked its way across industries, even if there was no crisis. It was ruthless and probably a little coke fueled, perfect for the eighties.

Then in the 1990s, modern HR departments were launched. We started caring about the employee experience (before we knew what to call it). We invested more heavily in development, better recruiting practices, and the phrase “People are our greatest asset” spread faster than a cold at a daycare.

Did CEOs suddenly feel regret about downsizing as a workforce management strategy?

Probably not. Instead, the boom of the nineties created record low unemployment, skills gaps, and worker shortages. Demographics and economic success forced company’s hands. They had to be people first. People had choices.

That’s what I wrote about in this week’s Reworked column. While I don’t think we are doing a 90s repeat (which is too bad because I’d really like to see some of those bands again), there is something to learn here about how we get back to a more people-focused workplace.

Unfortunately, it’ll probably be because you’re in a competitive industry, a needed skill set, or we hit a level of demographic collapse that AI and automation can’t keep up with.

There are companies still living by the idea that people are their greatest asset but it’s not very cool anymore. But it will come back eventually. I think.

Take the Sapient Insights Group HR System Survey

Now nearing three decades, this report from Sapient Insights Group is one of my favorites to read and cover. It’s also a researcher’s dream. Decades of longitudinal data about the systems companies use to manage people, from the mainframe days, through SaaS, and now AI-led systems.

If you are familiar with your HR systems, I encourage you to take the survey and tell Stacey, Cliff, and the rest of the Sapient team what you’re currently doing.

Take the Survey

What else is going on this week?

• Road Report 2026: Workday Innovation Summit. Stacia Garr covers five bets Workday is making coming out of Innovation Summit, and the throughline is that the company believes its installed base and data depth are walls. Whether those hold against AI-first HCM challengers is a separate question from whether Workday believes they will.

• The Case for a Four-Day Week: What the Research Shows. David Green hosts Joe O’Connor and Jared Lindzon to argue the question isn’t how to get more from people, it’s how to get more from the time people already spend at work.

• Most Hiring Automation Stops at the Apply Button, Study Finds. 57% of organizations say they use automation agents in hiring; fewer than 1% have connected those tools into workflows that affect who gets hired. Jill Barth has it.

• Human Capital Insider, May 2026. Kyle Forrest builds on the Deloitte HC Trends finding that only 7% of organizations are truly adaptable, which means 93% of them think they are.

• ‘Doomjobbing’ Can Hurt Your Job Search. The term was coined by an 8-year-old watching her laid-off parent scroll LinkedIn for hours. At this point a book would be an improvement.

• Entry-Level Productivity Expectations Have Increased Due to AI, Report Says. 30% of HR professionals now favor hiring fewer junior workers, and 74% of those same organizations have no development programs to replace the on-the-job learning being lost. Efficiency win today, leadership gap in five years.

• What’s Coming for Meta This Week. Francesca Ranieri coins “Sitting Duck Syndrome” for the 78,865 Meta employees who know something is coming, don’t know what, and can’t do much about either. Productivity is not the prediction.

• Rebranding With Purpose. Pete Tiliakos and Amberly Dressler on rebranding in a category moving faster than the messaging, and they’re honest about what doesn’t work.

• Amazon Employees Forced to Hit AI Quotas Immediately Start Using It for Everything Except Work. Amazon introduced AI usage leaderboards and workers responded by gaming their token counts for personal tasks, a practice now called “tokenmaxxing.” Mandating a behavior with a metric is a reliable way to hit the metric.

• A New Labor Market Tracker at The Wrap. Mike Wood built a live unemployment tracker into The Wrap’s site, which is better than navigating the BLS directly.

• Reserved, Coveted and Contested: Decoding the Office Parking Lot. Robin Schooling argues who gets the reserved spot tells you more about the culture than the values wall ever will, and she’s not wrong.

• Kroger Facility’s Parking Lot Policy Violated Federal Labor Law, NLRB Judge Finds. An NLRB judge ruled a Kentucky Kroger distribution center unlawfully barred off-duty employees from the parking lot to solicit union support, and the HR rep made it worse by not telling the employee she could move to the sidewalk.

• The Most Useful AI Use Case So Far. Meg Bear on using Open Brain for meeting prep and synthesis, and why the value only compounds once you’ve used it consistently enough to see the pattern. More practical than most exec posts on the topic.

• Why AI-Obsessed Companies Should Care About the Aging Workforce. Workers 55 and older will account for more than 25% of the G7 workforce by 2031 while nearly every boardroom conversation is about AI and cost control. Dan Pontefract notes demographics do not care about your transformation strategy.

• Why So Many AI Initiatives Fail to Launch. Kristy McCann on why so many AI rollouts never get off the ground, and the explanation is more structural than most organizations want to hear.

• Who’s Your AI Bias Auditor? Bennett Sung frames bias audits as real data science work and walks through three pathways for who actually owns the function. Who you assign matters as much as whether you do it.

• CHRO Confidence Hits Record Highs as Hiring Surges. The Conference Board’s CHRO Confidence Index hit a record 59 in Q1 2026, with 59% of HR leaders planning to increase hiring, and retention listed as the “persistent weak spot” that will most likely undo all of it.

• TA Livestream Takeaways: The Humanity Premium in Hiring. As AI saturates the candidate experience, any moment with a real human has become the premium version, and most TA teams are cutting exactly those moments to fund their AI investments. Roberta Gogos writes it up from Lighthouse Research’s survey of nearly 2,000 candidates and hiring leaders.

• The AI Access Axis: The New Dimension of SaaS Pricing. Kyle James on how AI is adding a new access axis to traditional SaaS pricing models. The pricing layer is where real access decisions get made, long before anyone frames it as an equity conversation.

• The Million Dollar Sourcing Assessment. Balazs Paroczay argues watching a sourcer build a Boolean search in real time tells you more about their quality than reviewing their results. The gap between what teams think they’re doing and what they’re actually doing is expensive.

• Kids Claim Child Labor Law Violations at Roblox. A class action alleges Roblox profited from the unpaid labor of minors, with the lead plaintiff claiming he worked 40-plus hours a week between ages 11 and 13 doing game design for adult-led teams with no wages. Yikes!

Have a great rest of your week!

Back in the summer of 2024, Lattice rolled out digital workers on the org chart, complete with onboarding and (of course) performance management. I wrote on Reworked at the time that it was both bold and misguided. It was quickly shelved and forgotten by all but a handful of folks.

It turns out they were ahead of their time, both for their embrace of AI agents as kinda employees and for turning away from it.

Welcome back to The Turn. This week, we’re covering digital workers and unintended consequences.

New research covered in HBR shows what happens when we humanize the machines at work rather than treat them as they are: brittle, lacking accountability, and ultimately deployed by humans who should be monitoring their digital lackeys more closely.

First, let’s talk about accountability. The research showed that when an AI “employee” made a mistake, people blamed the AI. One participant even said, “The blame isn’t on a person; it’s on the technology.”

But who created, deployed, and monitored the agent? What happens when an agent delivers substandard work? What if it does something against the law? Certainly a person, not a machine, will be held to account.

When an AI “employee” created work, not only does the burden of review shift to a person (which has been well covered); they also reduced the confidence in reviewers. Of note from the article: “Several participants explicitly referenced questioning their own skills, doubting whether they had identified all issues, or feeling the need to re-verify work they would typically accept at face value.”

That also led to them catching fewer errors from worked framed as done by AI “employees” rather than an AI tool. Nearly 20% fewer errors caught on average.

Framing AI tools as employees also made people concerned about their own identities, with concerns about job security and lower trust in how the organization would use AI. As one person said, “If you want people to feel like they will lose their job to AI, or can be easily replaced by AI, then put it on the org chart.”

For organizations, all of that may not matter. We can deride people who reject AI “employees” as horse buggy enthusiasts, self-interested, or simply and stubbornly human. Yet adoption also shows no meaningful improvement when positioning AI as an “employee” vs a tool. Instead, adoption is driven the old fashioned way. Human dynamics, manager encouragement, and clear expectations drove actual adoption.

So all of those downsides and for what? You get to pretend you’re ahead of the curve, actively harming the work and employees with no improvement in outcomes?

It’s worth a read to understand how you can avoid all of this but the biggest learn: Words and positioning matter.

What else is happening this week?

• Life, Agency, and the Pursuit of Ozempic. Missed this one but Max Bayram ties Kyla Scanlon’s Ozempicization essay to Palantir’s manifesto to AI tools and lands on the same mechanism underneath all of them: the appearance of agency is the product, and the actual agency is what gets extracted in exchange for it.

• The Engineer Who Won’t Use AI with Andrew Norcross. Laurie Ruettimann interviews Andrew Norcross, who built the architecture for NASA.gov, the New York Times, and Disney, and now paints fences in Florida while waiting for the AI bubble correction he’s certain is coming. He won’t ship code he doesn’t understand, and “quadruple your AI costs,” he says, “and tell me it’s saving money.”

• Ten Years Later, I Still Believe in Better Technology. Shannon Pritchett is back with a new newsletter, and her reread of a 2016 sourcing article lands on the same indictment: the industry confused automation with innovation and the original assignment, giving recruiters visibility into people they couldn’t see before, still isn’t done.

• AI and Hiring Alignment Report 2026. Tim Sackett shared this, and Metaview’s data on AI in recruiting is worth a read for anyone who wants numbers behind the conference talk.

• Research: Why You Shouldn’t Treat AI Agents Like Employees. BCG and Boston University put it on paper: giving AI agents employee-style roles drops individual accountability, lowers review quality, and increases escalation, all without improving adoption.

• I Built My AI Brain 3 Hours. Jess Von Bank keeps building in public, and her “Judgment Stack” framework explains precisely why most people’s AI output sounds like everyone else’s: they stopped at identity and never got to reasoning.

• Agentic AI in Talent Acquisition. Madeline Laurano’s hosting a webinar on what’s changing in TA. This one is worth getting on the calendar (tomorrow!).

• OK Doomer: AI Isn’t Killing Jobs, Brought to You by AI Investors. Wait, What?. Steve Smith on Galloway and a16z both publishing “relax, AI won’t kill jobs” essays the same week Freshworks laid off 11% and credited AI: “That’s not analysis. That’s asset protection with a Substack account.”

• White House’s Hassett: AI Isn’t Costing Anybody Their Job Right Now. Kevin Hassett says there’s no sign in the data that AI is displacing workers, joining a chorus that keeps getting harder to hear over the layoff announcements.

• Most HR Leaders Think Their Job Is to Support the Business. That’s a Ceiling. Jennifer McClure puts it plainly: the seat at the table conversation is over, and the question now is whether you’re shaping the agenda or still asking for a chair.

• Job Descriptions Are Quietly Becoming the Dividing Line. Jason Lauritsen and Lisa Sterling on the podcast this week: job descriptions were built for factories a hundred years ago, and the best work on your team is probably happening outside the box you put people in.

• The Talent Assessment Market Has a Risk Problem. Charles Handler built an AI to track 400-plus assessment vendors and the finding that should stop you cold: at the highest AI risk levels, only 9% have an I/O psychologist anywhere in the process.

• The Workday-A16z Debate Has Five Smart Takes. Here’s What They’re All Missing. George LaRocque adds the one angle nobody brought: $24.5 billion into Workday-competitive categories since 2017, and the enterprise ceiling has held every single time.

• The AI Bill Is Coming Due, and CHROs Need to Be Ready. George is on a roll. Consumption costs are moving through vendor P&Ls into your renewals, and the average enterprise AI budget went from $1.2 million to $7 million in two years. Your CFO has entered the building.

• Hundred Years, All New People. Tyler Weeks on the time his boss couldn’t fire him so she announced his reassignment to the entire HR department, and what he learned from surviving it: the only real license to lead comes from discovering that the worst case was finite.

• The How #25, May 7, 2026. Kate Achille, newly promoted to CEO of The Devon Group, lost her dog Ruby Sue and went back to work the next day, and uses that to make the case that grief policies shouldn’t require anyone to justify who they loved.

• I SIOPed So Hard This Year. Alexis Fink on the Hot Ones session at SIOP: engagement surveys without follow-through are worse than doing nothing, and most analytics teams maintain dashboards nobody visits. Both true, both will be ignored.

• TechWolf, Deep Tech Meets Work Tech, Context Graphs, Transforming Work. Thomas Otter has money in TechWolf and says so, then makes the case that the context graph layer won’t be owned by the frontier model providers or the ERP incumbents, and explains why that’s the most important open question in work tech right now.

• Workplace Misconduct Hits Near 7-Year High as Reporting Confidence Rises. 55% of employees experienced or witnessed misconduct in 2025, nearly back to the 2019 peak, reporting confidence is at record levels, and the question of whether AI caused any of this or will fix any of it remains, so far, unanswered.

Have a great rest of your week!

“Hire by Google is the actual game changer recruiting needs.”

That’s what some idiot wrote in 2017 about an ATS launched by Google that would eventually only last about three years and change the game exactly nil.

Unfortunately, that idiot was me.

Welcome back to The Turn. Today, we’re chatting about tech giants that dabble in HR tech.

When you write for decades, you’ll inevitably have some misses. In my defense, it looked perfect for SMBs embedded in the Google ecosystem. I mean, this is what they launched with.

So it seemed really good, but it eventually (and, likely, inevitably) failed. It was a good lesson on when to balance nuance with a good, grabby headline. Maybe more importantly, it was also a lesson about software market dynamics, especially in HR technology.

Now a new tech giant thinks they have something to offer.

Last week, Amazon announced the launch of Connect Talent. It’s an AI-powered agentic hiring platform that conducts voice interviews, scores candidates on competency frameworks and hands recruiters a dashboard with transcripts and rankings. Amazon states it’s built on decades of the company’s own hiring science, and that it’s designed for employers who need to hire a lot of people fast.

My full analysis is on Reworked but after writing my column, the question I keep coming back to is a simple one: Why? Out of all the companies to copy a hiring playbook from, why would anyone in a high-volume hiring industry choose to copy Amazon’s?

From the outside, their high-volume hiring science looks more like something cooked up by Frankenstein. How do hires go from decision to onboarded to paid? At scale, those steps really matter if you want to do anything besides just make a better initial decision. And it really makes more sense when something like Connect Talent is embedded into a broader platform layer. Just ask Paradox. They already have real-life stories that Amazon wants to tell, too.

Also, what happens when Amazon figures out that selling HR tech at the enterprise level is a multi-year endeavor? At least Google was targeting SMBs with Hire, a market that often had less than a handful of decision-makers and incumbent technologies in-house. The change management alone at this scale won’t be fun for anyone.

That doesn’t mean Amazon is completely screwed. As an integration into existing hiring systems, it might make more sense to adapt what Amazon has built instead of building it yourself. And if you’re already developing on AWS infrastructure, it makes even more sense.

But hiring tech isn’t slowing down or waiting for Amazon. Ultimately, it still might go down as more successful than Hire or the Fire Phone. That’s not a high bar to cross though.

AI talent infrastructure is still catching up

How hard is it to find AI talent? Your view likely depends on where in the org chart you sit. New research from my friends at X-Team shows company leadership is super confident in their abilities to bring in AI talent. The people closer to where those people operate? They aren’t as sure.

While focused primarily on AI development talent, we are going to see the same trends as other functions adapt AI. Projecting high confidence from leadership while taking a sort of fake-it-as-you-make-it approach on the ground has a short expiration date, though.

The report also goes into my annoyance: Governance in name only, even for those concerned about governance risks.

If you are hiring AI talent, this is a good one.

What else is happening this week

• The Best HR & People Analytics Articles of April 2026. David Green has the sit-down reading for April, and the frame he opens with earns the whole list: this is less a test of technology than a test of leadership.

• The Definitive Guide to AI Adoption in Talent Acquisition. AI is everywhere in hiring, impact is not. Madeline Laurano finds most organizations have moved quickly across TA but are still running isolated use cases and calling it transformation.

• Atlassian Adds AI Enablement to Its People Strategy. Atlassian created a Chief People and AI Enablement Officer role, and put Avani Solanki Prabhakar in it. Unlike most announcements of this kind, the structural commitment here suggests someone might actually be doing this right.

• HR Tech Confidence Check, Spring 2026. Mercer’s spring check finds the same gap that has been there a while: shifting from simple investment to strategic alignment is still the move most HR teams have not made.

• Voice Is the New Interface. Greenhouse Just Made Its Move, Acquires Ezra AI Labs. George LaRocque talked to Greenhouse CEO Daniel Chait after the acquisition, and the bet is simple: voice is about to become the default hiring interface, and this is Greenhouse staking its claim early.

• Recruiting Brainfood, Issue 499. Hung Lee is one away from 500, and 499 does what you would expect from someone who has kept this going long enough to include a serious essay on organizational sense-making alongside the usual links.

• Somewhere Over the Atlantic, I Figured Out Why AI Adoption Is Broken. Always good to get a Jason Averbook update from 30,000 feet, and this one lands on something that isn’t just a runway: the technology is not broken, the system it was dropped into was never designed to be trusted in the first place.

• Workday and A16z. Thomas Otter offers some comments on the comments about Workday, and there is enough signal here that piling on with more observations would just get in the way. Just read it.

• Is the Workday Debate Really About Workday?. Steve Smith argues that anyone working in enterprise software should take this debate personally, because the real question it keeps circling is about the whole category, not the company.

• Ep 790: Rethinking Work in the Age of AI. Matt Alder and Stacey Harris talking is reason enough to listen, and the topic, what AI is actually doing to work and what the vendor landscape is supposed to do about it, gives the runtime something real to cover.

• Ethical HR Leadership. Laurie Ruettimann has a LinkedIn Learning course on ethical HR leadership, and the fact that she has to preempt the joke about whether that is an oxymoron tells you something about where the field is right now.

• Hurtful, Honest, or Helpful. Craig Hepburn gets closer than most to naming what the AI-and-jobs argument keeps skipping: the actual space between disruption and displacement, and what is happening there.

• A New Way of Entering the Job Market. John Sumser calls it a draft advice note, which is the honest label for a moment when the rules for entering the job market are still being written.

• 2026 RedThread Megatrends. Stacia Sherman Garr maps the five forces reshaping work, and the one that anchors the whole thing is this: they are bigger than either HR or the business, which means both sides need to stop acting like they control the outcome.

• I Just Launched the AI Pledge for Humanity. Here’s Why. AI leaders keep saying UBI is necessary, and Scott Santens puts out the call for them to actually prove it with real commitments, which is a reasonable test of whether the consensus position is a belief or a talking point.

• HR Snacks To Go: Kyle Forrest at Deloitte, Transform. Joel Stupka and Kyle Forrest run through the AI-plus-human equation in ten minutes, and the case they make for multiplication over addition is worth the quick listen.

• The Platform You Bought in 2021 Was Built for a World That No Longer Exists. Mike Wood’s counterpoint is worth the read: the TA software market has already had its Napster moment, not the slow-moving kind where incumbents get years to adapt, and the progress worth tracking is the kind that does not wait for them.

• 5 Lessons for Early-Stage B2B Brands. I got to talk to Bennett Sung about what he was building before he put it in writing, and what he is sharing here, category building, AI-augmented marketing, founder-led GTM, is the kind of playbook people usually keep to themselves.

• I-O Psychology and Predicting the Future. Gabby Burlacu comes out of SIOP with a concern worth talking about: what industrial-organizational psychology knows about where work is going and what organizations actually apply are two very different things, and the gap between them is addressable.

• Informed Does Not Mean Intelligent: Workforce Analytics and the Under-Appreciated Value of Context. Steve Hunt draws a hard line between informed and intelligent in workforce analytics, and the argument is that organizations calling data outputs intelligence are creating blind spots they do not know they have.

• Why Trust Is the Defining Asset of 2026. Three numbers from a Fast Company piece stopped Kristy McCann Flynn mid-read, and the case she builds around them, trust as the asset most companies are not measuring, is one worth thinking about.

Have a great rest of your week!

Welcome back to The Turn. Today, we’re talking about the exciting world of worker buyouts.

Microsoft is offering roughly 7% of its US workforce the option to retire early. The eligibility formula, age plus years of service totaling 70 or more, effectively targets its most tenured (and expensive) employees.

Buyouts aren’t exactly new. If they were a cure-all, every newspaper in the country would be thriving. I am told that is not the case, though.

While it is a novel solution in the tech industry, I’ve always had a problem with a voluntary buyout, even a targeted one.

The biggest issue: Voluntary programs don’t let companies choose who leaves. They let employees choose, and employees optimize for their own interests, which is reasonable but also entirely at odds with what the organization needs.

The employees most likely to accept the package are the ones with the strongest external options. They might have decades of enterprise relationships, networks, and a resume that does a lot of heavy lifting. Some will land at competitors. Some will start something.

Microsoft’s exit package becomes their runway.

The ones who decline are a more complicated group. Some are committed to seeing the transformation through. Some already tested the external market and didn’t like what they found. Some know they have topped out at Microsoft and want a few more years of their top earnings in their retirement accounts.

All of these are good reasons. For the employee.

Microsoft doesn’t get to distinguish between those outcomes. Layoffs aren’t inherently strategic, but they can certainly be that if done well. You make projected cuts based on the skills you need going forward, and you sell the people who you chose on why they are so needed.

Targeting their oldest and most experienced employees is also a gamble. These are the people who remember how the last several platform shifts actually played out inside a large enterprise. Not how they were announced, but how they actually went. They also have customer relationships that predate their current skip-level. They know which problems are structural and which are noise, which escalation paths work, which internal fights are worth having.

That knowledge isn’t in a knowledge base. It isn’t recoverable from a well-constructed prompt. It lives in specific people, built over years of being inside a specific organization.

Microsoft is betting AI absorbs what leaves or that what they actually need is a fresh start with fresh ideas.

That bet could very well pay off at the task level and maybe even a beyond. The judgment layer is a harder problem, and the people who carried it are now taking calls from recruiters.

What else is happening this week

• The AI Labor Debate: Three Views on the Future of Work. Carnegie lines up an optimist, a skeptic, and a muddier middle ground on what AI does to work, and the honest read is that all three might be right at the same time.

• Why Career Sites Are the Future of Recruitment. Matt Charney finds the gems in the thing recruiting teams love to complain about most. The case for owned channels over borrowed ones isn’t new, but the specifics here make it stick.

• Shaker RM Is a Stevie Award Winner. Congrats to Gina Alioto and the whole Shaker team on the Stevie win.

• The Talent Time Machine, Episode 16. Marissa Geist and Jason Scheckner dig into what it actually takes to break IT and HR out of their respective silos. Good conversation on the structural reasons this problem persists and what it costs when it does.

• MrBeast’s Company Sued for Alleged Sexual Harassment, Wrongful Termination. The excuses cycle through the same predictable rotation: startup energy, fast growth, isolated incidents. At some point the pattern is the story.

• The Market Isn’t Pricing Your Numbers, It’s Pricing Your Narrative. Steve Smith breaks down why ServiceNow and SAP command the multiples they do while Medallia doesn’t, even when the underlying numbers tell a messier story.

• Anthropic’s 81K Economics Research. Anthropic drops more research on AI’s economic footprint. How are CEOs so low on the displacement list, given that some of them are already running multiple companies at once?

• SkillCycle CEO & Founder Files Federal Lawsuit Alleging Predatory Takeover, Fraud, and Retaliation. Kristy McCann is fighting to take back the company she founded in an unusual way: in public. We usually only hear about this at networking happy hours at conferences.

• What’s Stalling AI Adoption in HR. Uncertainty about cost is doing more to slow AI adoption in HR than uncertainty about capability, says Stacey Harris.

• How Your Hierarchy Ensures Eventual Failure. Michael Carden taps into the structural reasons well-designed organizations eventually collapse under their own weight. Harder to dismiss than the usual org design takes.

• Intrateam IC Newsletter. If you’re doing digital transformation work inside a large organization and you’re not subscribed to Kurt Kragh Sørensen‘s Intrateam newsletter, fix that ASAP.

• Culture King at 30,000 Feet: Lessons from Shawn Achor at Total Rewards. Tom Short has a solid set of takeaways from Shawn Achor‘s session at WorldatWork in San Antonio. If you weren’t there, this is the next best thing.

• Your AI Prefers Itself. Steve Levy breaks down what happens when AI systems favor AI-generated content in recruiting, where the entire point is evaluating actual humans. Quiet problem, loud consequences.

• Your HR Tech Stack Is Being Rebuilt Around You. Do You Have a Voice in It?. George LaRocque brings the marketscape data into a conversation HR leaders need to be in. The stack is getting rebuilt with or without them, and that’s the problem.

• So Many Vegas Visits, Still So Few for Fun. Rob Pegoraro captures why Vegas is essentially a different city depending on why you’re there. It’s been a few years since a trip that wasn’t for an event, basketball included, and this one resonates.

• The Reinvention of Workday: From System of Record to Platform of Agents. Josh Bersin unpacks what Workday is actually building toward: less the place where HR data lives, more the foundation for AI agents working on top of it.

• More Output, Less Meaning: How AI Is Changing Day-to-Day Work. Interesting research on AI and workplace meaning, brought to you by the company that makes your business cards.

Have a great rest of your week!

Welcome back to The Turn. Today’s story is very, very dumb. But selfishly, I hope you read it anyway.

I wrote about Meta’s attempt to develop a photorealistic AI version of Mark Zuckerberg for Reworked. It’s kind of like the Tupac hologram that “performed” at Coachella a few years ago, but only if you like robotic technocracy instead of counter culture social commentary and sick rhymes.

Unfortunately, Zuck AI is built off of his human mannerisms and tone as well as public statements he’s made (including those made during earnings calls). Sounds captivating.

Of course, the company is investing in this initiative as they plan waves of layoffs starting in May (conveniently leaked out prior to its earnings call next week). Remember when layoffs indicated a company in trouble rather than one that investors flock to? Simpler times.

There are two issues here that are both simultaneously valid.

This is what Meta does

Meta sees a problem. Meta tries to solve it with technology first, seemingly unaware of the limits of how far a tech solution can take it. The Metaverse continues its slow collapse in spite of early promise. Workplace by Meta declined much more rapidly.

There’s not necessarily a flaw in the technology itself. Instead, what seems to happen is that Meta has a weird view on how people want to interact with and get from technology and spends ungodly amounts of money on the problem, only to eventually give up.

In that way, this approach isn’t unexpected. Meta has too many people to connect with their CEO in an authentic way. This weird stand in will certainly help, right?

It won’t. I don’t care how robotic and weird this dude is but he is who he is because of his humanity. This isn’t some Black Mirror episode where we can interact with someone’s essence. It won’t even be uncanny.

Let’s just hope they don’t spend another $70B+ on this one.

Connection is a legitimate challenge

And you know, I hate to give them credit, but the problem they’re trying to solve is a real one. Once you get beyond a certain size, connecting with employees as a CEO is just impossible. If Zuck spent every minute of a standard work week connecting with employees, they’d each get 90 seconds.

Think that’s bad? Walmart’s 2.1 million employees could bank on 3.5 seconds each. Hope you make a strong impression.

Which is why companies don’t try to do this at any sort of scale. They use sampling or focus groups and hopefully they don’t insulate a CEO too much from the real world problems. It’s why shows like Undercover Boss worked.

Most companies of that size have also figured out that the CEO isn’t even a great person to be connected to anyway. If you’re at Walmart, you’d probably rather be connected to your store manager or regional leader where decisions about your working life are more closely made. If you’re at Meta, you’d probably rather be connected to the senior leader of your department or your project team.

Please don’t do this

I don’t know why the folks at Meta think this is a good idea but it isn’t. I could make jokes for days about it, but at a time when thousands of people are being laid off by cold emails at 6 am, we need real human connection, not a synthesized version of it.

Trying to scale connection to the CEO will not work. It’s a dumb investment. I don’t want to ask an avatar for answers about my job or about the direction of the company I work for. I’ll talk to a human, any human, with some insight. And if it is just a video put together by humans whose job it is to communicate such matters, then that’s fine as well.

Did I say don’t do this already? Don’t do this.

Claude Design is live and it’s better and worse than I expected

Claude Design launched and I played with it (and completely exhausted my usage for an entire week).

The TL;DR version is like all things AI: Highly capable in plausible execution, less so with specifics and production-level quality. It takes some work to get it in good shape and there will be some very well executed slop.

Apologies to all my designer friends who will be feeling the annoyance that folks in the coding and writing trades have been dealing with.

What else is happening this week?

• Letting AI Do Your Work Erodes Your Confidence in Your Own Thinking. Passive acceptance is the key variable: the 1,923 adults who took AI answers without pushback reported weaker confidence and less ownership over their own ideas, while those who edited and pushed back came out fine.

• The Hidden Cost of Frictionless AI. reframes the cognitive dependency problem as “capability preservation,” which is more useful for organizations whose AI rollouts are producing faster but shallower work.

• 58% of Employees Plan to Search for New Jobs in the Next Year. Intent is cheap; the question nobody’s answering is what jobs these people are planning to land in, given how quickly the entry-level roles are being absorbed.

• If AI Were a Woman. builds a more interesting piece than the premise promises, including a first-person detour into Claude’s Design app that does real field reporting alongside the philosophy.

• How to Get Started with Claude Design. No mature design system, no clean results: that’s Brandon Giella‘s honest summary of a tool that works as a multiplier but not as a replacement for foundational design operations.

• Early Talent in Peril: Can Work-Based Learning Solve the Entry-Level Career Crisis? Six percent fewer early talent jobs since 2022, twice as many applicants per opening, and AI absorbing the work that used to be the training ground: Alexandra Levit makes the data hit harder than the talking points from Unleash.

• Writing from AI Defaults to the Mediocre Median. Jaclyn Schiff compares AI-generated LinkedIn content to Spotify’s recommendation bubble, and it’s a good one: algorithms optimizing for broad appeal produce content nobody asked for specifically.

• The Religious Right Is Being Recruited for the AI Crusade. The argument that Christians have a moral obligation to embrace AI, pushed by Peter Thiel, Katherine Boyle, and Trae Stephens, is a more substantive strategic play than the headline makes it look.

• HR Tech’s Moviephone Moment. Mike Wood launches a new site and opens with a Seinfeld reference, which is a strong editorial debut if you’re picking one cultural touchstone for where HR tech is headed.

• Purple Acorn Network Partners with PodStar to Scale Founder Voices Across the Future of Work. Congrats to Evan White: 30,000 monthly listeners and a partnership that gives real global distribution to a platform that has already outgrown its original lane.

• The Road to ERE: Part One. One of the better recruiting conferences on the calendar gets a preview, and Brian Fink‘s conversation with Ed Delgado on what sourcers actually need to do with AI is worth reading before Atlanta in May.

• The AI-and-Jobs Convo Gets Real: Talking Layoffs, Brain Fry and the SaaSpocalypse. Steve Smith argues the real displacement story isn’t just jobs. It’s also the software those workers depended on. What happens when the middleware layer collapses and the management layer built to oversee it goes with it?

• We Need to Do the Reps to Get Better as Talent Advisors. Working at something is still how you get better at it, and John Vlastelica‘s pushup analogy holds because talent advisor skills are built in actual conversations with actual hiring managers, not in training modules.

• The Payroll Problems That Never Change. More than a dozen years since I’ve done payroll and Stacey Harris and Sapient Insights are surfacing the same core failures: integration gaps, customization limits, and systems that don’t talk to each other, just with newer logos on the vendors.

• The Truth About California’s Fast Food $20 Per Hour Wage Law. Steve Boese reports that prices barely moved and job losses were modest, which dispenses with the main employer objections but leaves the more useful question open: when does this become a federal conversation?

• How Webinar Transcripts Drive Search Visibility by Anchoring to 3 SEO Fundamentals. Bennett Sung makes the case that one webinar can become five focused search pages, and the core argument that raw transcripts are an underused asset holds even if you skip the SEO scaffolding.

• Why AI Improves Employee Engagement for Some Organizations and How to Make It Work for Yours. Almost half of surveyed leaders report AI improved engagement while fewer than one in ten say it hurt. Those numbers look very different sitting next to the burnout and cognitive offloading studies published recently.

Have a great rest of your week!

We’ve developed a bad habit of treating individual talent moves like market research.

Someone notable leaves one software company for another, and within hours, there’s a hot take about what it means for the incumbent’s competitive position, its AI credibility, and its long-term viability.

The more senior the person, the more definitive the conclusion for some reason.

It’s gotten to the point where every hire announcement from Anthropic or OpenAI comes pre-loaded with a narrative about who’s winning and whose goose is getting cooked.

This is not a useful way to read the industry.

People move for a lot of reasons. It can be a role that turned out to be the wrong fit, a challenge they wanted to explore, or a working environment that suited how they actually want to work. There can be personal reasons or preferences, even for big-time executives.

None of those reasons are completely invisible in a job announcement, but none of them are completely visible either. What you see is a name, a title, and a press release written by a comms team.

I wrote on Reworked this week about Peter Bailis leaving Workday for Anthropic, partly because the coverage got some things meaningfully wrong, and partly because it’s an example of this broader reflex. From misreading what the title actually means to stretching the competitive angle, too much got skipped.

None of that is surprising. Nuance doesn’t travel well in a news cycle. But the cumulative effect of treating every notable hire as a verdict is that we end up with a lot of confident conclusions built on thin evidence.

The talent patterns that actually tell you something are aggregate, not individual.

• Which kinds of people are choosing to build at the model layer versus the application layer?

• Is the flow directional and sustained or scattered?

• What do dozens of moves reveal about where the hard problems are perceived to be?

One person’s career decision, for any number of reasons you’ll never fully know, doesn’t get you there.

We’re going to keep seeing these hires, and they’re going to keep getting this treatment. Don’t read too much into it on an individual level. Look for larger trends. I know it’s less fun, though.

Use AI to research in a way that’s smart

I’ve been annoyed by bad research since before Wikipedia and “Source: Google” citations.

The most annoying part is that people today have much more powerful tools at their disposal, yet they use them to synthesize the most boring conclusions. “AI is disrupting recruiting,” or “Entry-level jobs are changing.”

Wow. I definitely needed a summary on that.

I recently shared how I structure my general research process using AI. It is NOT a pretty process, and it is expensive as far as usage is concerned. But instead of a Reader’s Digest version of the news, it gives you an organized reading list to get you an in-depth read on what has and is happening.

What else is happening this week?

• Meta AI Lets Zuckerberg’s Staff Talk to the Boss — Meta built an AI clone of Zuckerberg so all 79,000 employees can feel connected to a CEO who apparently couldn’t manage it himself. Nothing says “we value our people” like a chatbot wearing the boss’s face.

• Gallup: Rising AI Adoption Spurs Workforce Changes — Half of workers use AI at least sometimes, but only one in ten says it’s actually changed how work gets done. Companies that adopted it are somehow hiring more and cutting more simultaneously, in case you were wondering who’s pocketing the productivity gains.

• Is Joy at Work Possible? — talked to Yolanda Fraction, who argues joy is more achievable than happiness at work because it doesn't require things to be going well. A genuinely useful distinction most workplace wellness gurus never bother to make.

• Zoomers Are Sabotaging AI at Work — Nearly half of Gen Z workers are deliberately breaking their company’s AI rollouts by leaking data, using unapproved tools, and submitting garbage output. The researchers recommend better change management rather than stopping the automation victory laps while threatening people’s livelihoods.

• Minimum Wage Hikes Are Quietly Raising Injury Rates — When the minimum wage goes up 10%, companies respond by working people harder rather than trimming headcount, producing two to three additional injuries per 1,000 workers annually. You got a raise and a repetitive stress injury.

• Workplace Management Tech Is Everywhere, But Integration Challenges Limit Its Value — Workplace management tech is widely deployed and barely anyone can get it to talk to their other systems. This sentence has been true of enterprise software since 1998 and probably won’t change with AI.

• Bosses Are Getting Worse. AI Is Part of Why. — Managers are outsourcing their judgment to ChatGPT, and their employees are cleaning up after it. Groundbreaking findings from the year 2026.

• The New Architecture of Work — AI is collapsing the cost of expertise to near-zero and most enterprise software was built for organizational structures that no longer make sense. proposes replacing static job titles with dynamic task categories, which is the right idea and one nobody will actually implement.

• Should AI Agents Be Treated Like Colleagues? — A journalist staffed a startup entirely with AI agents and was stunned when they argued, disobeyed, and questioned his authority. The answer to the headline is no, and 82% of deploying companies are going to learn that firsthand.

• The Remote Work Fantasy Breaks in Real Life — A designer who moved through nine countries concludes that stability produces better work than novelty. The files always come with you.

• Could Microsoft Win the War for Enterprise AI? — While everyone obsesses over OpenAI and Anthropic, argues Microsoft is quietly positioned to capture the enterprise AI market through Office, Azure, and existing customer relationships. The companies that control distribution usually win. Or at least that’s what the guy who poured me my IPA last night said.

• The Future of Work Is Here, But Hiring Hasn’t Caught Up — AI and automation have reshaped what companies actually need from workers, but recruiting is still running the 2019 playbook. The gap between how work has changed and how companies hire for it is where a lot of current talent pain lives.

• Zoom CEO Says 3-Day Workweek Could Be the Future — Eric Yuan, whose company nearly collapsed when offices reopened, is very excited about AI making the five-day week obsolete. A real study backs him up on the time savings, but he’s a CEO, so the question of who actually keeps those hours went unaddressed.

• The Agent Illusion — Most things vendors are selling as AI agents are just workflows with a better interface. ’s test for a real agent: Does it adapt to new conditions or just follow a predetermined path, and does it remember anything? This also might work on me.

• The Missing Layer in AI Transformation — CHROs are deploying AI without first getting clear on what work they’re trying to change. This is also what happened with every ERP, HCM, and workforce planning platform before this one.

Have a great rest of your week!

Welcome back to The Turn. Today, we’re talking about everyone’s favorite work technology company, Microsoft.

I say that semi-jokingly and semi-lovingly. My provincialism for the PNW extends to the folks up in Redmond. Seattle’s journey as a major tech hub looks very different without them. Creating 12,000 (or more) millionaires concentrated in one place will do that.

There have been many phases of Microsoft but they may be entering their most interesting one. As I wrote in Reworked last week, CEO Satya Nadella inherited a company infamous for internal politicking and frequent re-orgs under different fiefdoms more than a decade ago.

Now, under new-ish CPO Amy Coleman, the tech behemoth’s HR function is going through a consequential re-org of its own. No more chief diversity officer. People analytics moves directly under the HR org. And a newly named Workforce Acceleration team tries to capture the momentum of a stampede of AI usage across its sprawling global workforce.

They are also losing multi-decade HR leaders in talent acquisition, total rewards, HR business partnerships, and diversity. A century and a half of combined leadership will be gone.

One of those changes is big enough on its own. All together? It’s a lot. Maybe too much. But as Coleman wrote in her internal memo:

Stability has been the name of the game for Nadella’s turn at CEO. That has resulted in generally warm reviews from the investor class. But since last summer, the stock has been in a relative free fall (though the stock is still 9x what it was since Nadella took over in 2014).

And with how reactive the market is and how quickly AI is impacting SaaS software, it really is a “what have you done for me lately?” sort of environment. Thanks for those long term gains and the dividends. Now, show us more growth, more innovation, and, yes, probably more AI.

Will it all work out? Microsoft can afford to take some swings and misses in the service of getting closer to the bleeding edge. The risk of looking complacent (regardless of reality) is a probably the bigger risk.

But I don’t think this playbook is for everyone. I explain why in my column.

HR tech providers: Are you doing agentic AI for real?

I am writing a report on it and would love to get stories of real life use cases and outcomes rather than AI washing. If you have 30 minutes this month to walk me through what you’re doing, fill out the short form here:

Brief me

What I’m reading

I haven’t done this for awhile but I am LONG overdue to recommend some folks to follow across LinkedIn and Substack because I do an absolutely awful job of crediting for keeping me informed and inspired. To write a lot, you have to read a lot. Who am I missing on my reading list?

• Laurie Ruettimann is an OG who has been telling HR the truth about itself since before the industry was ready to hear it. You can read Punk Rock HR on LinkedIn and her newsletter on Substack.

• Steve Smith monitors the workplace technology market so you can stay current without having to read every vendor press release and funding announcement yourself, which is either a service or a calling. You can read Work Tech Weekly on LinkedIn.

• John Vlastelica named his newsletter after the thing he’s most tired of hearing from leaders, which tells you everything about his relationship with corporate pep talks and nothing about why we keep giving them. You can read Try Harder Is Not A Strategy on LinkedIn.

• Jess Von Bank writes at the intersection of work, technology, and humanity, which is the intersection where most organizations install a stop sign and call it strategy. You can read her newsletter on Substack.

• Katrina Kibben has spent years trying to get companies to write job descriptions that don’t immediately disqualify the people who could actually do the job, which is both noble and the world’s most patient Sisyphean project depending on your employer. You can read Rewrite Recruiting on LinkedIn.

• Dan Schawbel has been tracking workplace trends so consistently for so long, I’m convinced that future historians will cite him when trying to understand what people work was like right now. You can read Workplace Intelligence Weekly on LinkedIn.

• Madison Butler is documenting the corporate experience with the honesty that most companies’ engagement surveys are specifically designed to suppress. You can read The Corporate Crash Out on LinkedIn.

• Brian Fink has appointed himself the chief advocate for an industry that doesn’t always advocate for itself, which either makes him noble or means he has a very specific kind of energy. You can read The Talent Architects on LinkedIn.

• David Manaster has been covering talent acquisition longer than most of today’s recruiting platforms have been in business, giving him the rare perspective of knowing what this industry looked like before it got a rebrand. You can read ERE Weekly on LinkedIn.

• Hung Lee manually curates recruiting content every week from sources most people aren’t reading, which in an era of AI-generated everything is a principled stance (and maybe some pure stubbornness). You can read Recruiting Brainfood on Substack.

• Mervyn Dinnen is still making the case that HR is actually a business function, which is apparently a necessary argument in any organization where the people team is still fighting for a seat at the table. You can read HR Means Business on LinkedIn.

• Mike Wood reads all the HR tech news every week so you don’t have to, and in an industry that produces this volume of announcements and rebrandings, that is genuinely a public service. You can read The Wrap on LinkedIn.

• Jennifer McClure is still using “disrupt” without apology, which is a statement of conviction and proof that she really is the founder of DisruptHR. You can read Designed to Disrupt on LinkedIn.

• Donald Thompson releases his leadership insights monthly, which is a sensible pace for ideas that take longer than a quarter to actually implement. You can read Leadership Edge on LinkedIn.

• Joel Stupka gives us snackable content that, even if you aren’t 100% sure you like it, only lasts a few minutes (but that has always worked out for me). You can read HR Snacks on LinkedIn.

• Kyle Forrest is Deloitte’s Human Capital CMO moonlighting as a newsletter editor, which means you get the enterprise view on what HR should be thinking about, curated by someone whose firm has thought about it for longer in private. You can read Human Capital Insider on LinkedIn.

• Thomas Otter has been tracking HR technology long enough to have watched multiple generations of “revolutionary” platforms become legacy systems, which gives his reflections on work tech a certain earned quality. You can read Work in Progress on Substack.

• Jason Averbook has spent decades at the intersection of HR and technology, which means he has had a front row seat to every overpromised implementation and every genuinely transformative shift (and can apparently still tell the difference). You can read Now to Next on Substack.

• Anita Lettink has made it her mission to decode the EU Pay Transparency Directive before it decodes your organization’s entire compensation structure first. You can read Equal Pay on LinkedIn.

• Steven Hunt started as a mathematician and became an organizational psychologist, which is the most logical career arc for someone who eventually concluded that people are the hardest variable. You can read Talent Tectonics on Substack.

• John Sumser has been the HR tech industry’s resident skeptic long enough to have earned the right to say “I told you so” about several things that are now considered conventional wisdom. Heather Bussing continues to play the role of counselor (in all ways) for the industry. You can read HRExaminer on Substack.

• Josh Bersin covers the entire HR technology and talent landscape with a research operation that makes you wonder if he sleeps, or if the real Josh Bersin is actually a very well-trained AI model by now. You can read Josh Bersin Unleashed on Substack.

• Kevin Wheeler has been writing about the future of talent since the future was still largely theoretical, with both incredible foresight and a field that is just catching up to things he was saying years ago. You can read Future of Talent.

• Meg Bear and Amy Wilson decided two newsletter authors are better than one newsletter author (or one podcast host), which is either a sign of shared passion or a mutual inability to commit to a solo project. I like both of those. You can read Insight Loop on LinkedIn.

I am sure I missed some folks (or maybe you don’t post enough!) but I’ll share more inspiration on the reg.

That’s it for this week!

He ended up at a Temple of the Dog rehearsal instead, more or less by accident. Somewhere in that session, Chris Cornell needed him on “Hunger Strike,” a song that became one of the defining tracks of a decade of Pacific Northwest grunge.

The rest is history.

Serendipity probably isn’t a good way to justify the mechanics behind a killer song or a user interface for SaaS. But it’s what kept going through my head when I saw the announcement from Workday about Sana’s “Superintelligence” offering, which is part connector, part agent orchestrator, and part deep AI chat capabilities for the enterprise.

The last part follows a trend which I’m not sure I like.

Visual interfaces have always been full of accidental on-ramps. You hover over something you didn’t mean to click and discover a feature you didn’t know existed. You scroll past a setting, you flip through a menu, you stumble, and you might find something interesting.

A visual interface has texture. It has information without asking. It has those moments of friction and serendipity that people have come to expect.

Chat has none of that. If you don’t know that you don’t know, well, then you don’t know. Even the stuff that it’s really good at, like pulling information from all over the place and making some semblance of sense, requires some level of knowledge to even ask.

It’s easy to make fun of a dashboard that looks like it was created when the Seattle Supersonics still existed. But those usually existed because someone was making a decision based on it.

I will say that for users with motor impairments or low vision, chat with voice enabled can be a genuine step forward. Have you ever had an accessibility feature read you a data chart? Don’t (unless you need it). A chat interface that can be descriptive instead of visual is a godsend.

For regular users of ChatGPT, there’s probably a level of comfort (especially at the beginner level). But for power users, for those who really need the full ugly functionality of these enterprise tools, it’s going to be a sidestep at best.

Natural language chat interfaces are built for ambiguity. That’s why they are so good for beginners. But strip all that away and the supposedly frictionless interface requires you interact with a different level of precision. You train yourself to issue commands so that you get expected results. So your brain and fingers become a different type of UI.

Obviously Workday isn’t shedding its whole UI but other startups in HR have sort of embraced this chat interface as the default or, in some cases, only option. The move to position Sana as chat first might embolden them to push further.

I don’t think that’s wise.

Vedder wasn’t supposed to be in that studio. But the scene worked the way it did because people were in the same room, stumbling into each other’s orbits, and discovering things sideways.

The best interface might not be one that simply goes from taps or clicks to chats because it feels easier for everyone. Sometimes it’s the one that puts something you didn’t explicitly ask for in your path.

What else is happening

• Workers Are Using AI to Sneak Out for Spin Classes and Skip Lunch Meetings. A Zoom-sponsored study conveniently finds that AI saves workers 30 minutes a day, which sounds pretty sweet. Bosses paying for AI productivity tools may be surprised to learn they’re also funding spin class memberships.

• 3 Charts on What CHROs Think About AI and the Future of Their Departments. Nice story from HR Dive showing that every CHRO says AI is their top priority, yet half haven’t figured out how to measure whether it’s actually doing anything. Apparently “AI strategy” for most organizations means “we’re aware AI exists and are very worried about it.”

• HR’s Imperative: Invest in the Skills AI Can’t Touch. HR thought leaders are urging companies to finally invest in “human skills” like emotional intelligence and critical thinking, the same skills we’ve been systematically devaluing in favor of quantifiable metrics for the past two decades. Better late than never?

• JPMorgan’s Jamie Dimon Says Remote Work Breeds ‘Rope-a-Dope Politics’ and Stunts Young Workers’ Growth A billionaire CEO insists young workers can only learn by watching him make mistakes in person, despite research showing remote workers are more productive and more engaged. The 1,200 JPMorgan employees who petitioned against the five-day mandate still seem unconvinced.

• Report Reveals Widening AI Guidance Gap. Thomson Reuters finds that companies are enthusiastically pushing employees to adopt AI while having no coherent strategy for what that’s supposed to achieve. That’s fine, because only 22% of companies have any AI strategy at all. The bar for “ahead of the curve” remains comfortably low.

• Major League Baseball’s Robot Umpires Are Here—and They Tell Us Something Uncomfortable About AI and Work. MLB introduced an AI challenge system for ball/strike calls, framed as a thoughtful human-AI collaboration. Given that nearly two-thirds of player ejections were over ball/strike disputes last year, human umpires weren’t exactly making a compelling case for their own indispensability.

• Welcome to the Weird World of AI Agent Teams. Researchers discover that when you put a bunch of AI agents together without organizational structure, they spiral into chaos, a finding that will not surprise anyone who has never attended a corporate meeting. Coordination problems, it turns out, are not a uniquely human flaw.

• Who Owns AI Agent Access? At Most Companies, Nobody Knows. 85% of companies have AI agents running in production, but nobody has agreed on who’s responsible for securing them. That seems like detail worth sorting out before something goes catastrophically wrong. With agents routinely over-privileged and borrowing human credentials, the next major breach has essentially been pre-scheduled.

• Why AI Is a Massive Job-Creation Technology, Despite What You Think Josh Bersin argues AI creates more jobs than it destroys, pointing to rising software engineering salaries as proof. The “don’t worry, new jobs will appear… eventually” argument has a long and storied history of being technically correct and creatign short-term pain.

I just launched Beacon Turn

A little promotional news: I just officially launched Beacon Turn. If you’re reading this on my Substack, you know this.

You can read the full announcement on my LinkedIn and if you have any research, media, or advisory work, I’d love to chat about it.

Thanks and have a great rest of your week!

One of my favorite places in my town is the Vancouver Community Library. It’s located about 10 blocks south of my house and I walk past old homes, apartments, and businesses along a tree-lined street to get there.

The funny part about most of my library trips is very few of them involve me reading a physical book. Instead, I’m often doing work, writing, or research there when I am tired of my office and house.

I see all kinds of folks there too. I might see a parent and child exploring the children’s book section. I see a senior volunteering to help people prepare taxes. I see folks without housing getting a break from the elements. People of all types, really.

Sometimes I work better with murmured chatter. Other times, I want to be interrupted. While I love working out of my home office, it’s nice to have places you can opt into some level of social gather.

Plus, the view from the top deck where you can get some sunshine and views of the river, Portland, and the West Hills is nice.

(Oh, and hi old ZoomInfo HQ.)

I bring this up because I enjoy the sense of community I get from the library the same way I get from my favorite coffee shop, gym, or brewery. I see some of the same people consistently but also many different people. I’ve helped people find specific books or where to grab a decent sandwich (Tap Union if you’re okay with a little walk, btw).

But unlike a coffee shop, brewery, or gym, there’s no purchase pressure or access issues at the library. Besides the used book sale, there’s nothing really to purchase. It is open to all, where a person with a bike trailer full of their possessions has the same ability to access it as the high-paid lawyer who works across the street.

It’s a true third place. Not home or work but a third place where community can be built. In The Great Good Place, Ray Oldenburg says that third places are not just good for us but good for the world.

And I wonder if we need that for HR.

Creating a place that’s inviting by design

I tried to count the number of HR conferences or events that I’ve been to and I lost track. Hundreds, maybe? And while I like these spaces, they are limited by design. They charge money. They are in far flung locations. They are often built around an agenda with specific things that the organizers think you will like. The networking events are contrived and often revolve around alcohol or loud spaces (usually both).

And this is fine btw. Conferences in particular are big deals for people. They might be spending some of their own money or their employer’s money. They get to go to a nice place, learn something, and maybe even have a little fun. When I get cynical about all these events, I have conversations with folks who might only do this once a year and remind myself that they are important.

These events also have many of the same subjects, speakers, and sponsors. Some pay to get your eyeballs and earholes on them. That means there is stratification. Someone gets a VIP ticket to a vendor party with Third Eye Blind playing while someone else is scarfing down In-N-Out with a view of the High Roller (most of the time, I’d prefer the latter).

But if a conference isn’t a good third space, then what is?

I think social media used to be that place. But algorithms, advertising, and the eventual closed loop nature of them gets us the same problem we deal with in other access restricted areas.

Game social interaction or build early enough and you can stay ahead. But the average person probably isn’t doing this and they don’t have time for it anyway. There are few HR practitioners that really have the social following that matters. You can’t work your day job and produce the level and bulk of content you need to stand out now. Even with help from marketing and ghostwriting like some people use, it can be tough.

Places that are less algorithmic? Sure, but why would I look at those when I could read a LinkedIn update from two weeks ago?

What a third place solves for

The reason why I continue to wonder about this is that I think we get stuck in an echo chamber. A small group of people determine the experience, content, and more in HR.

As a former event organizer, I always tried to get perspectives from people outside of my world but let’s be honest, it isn’t easy. Plus, the format of events in general is hierarchical by design (and by necessity, probably).

With the online environment, I wonder if anything can really cut through in a way that connects people. There are probably pockets of it, especially in niche areas.

There are likely some local organizations as well but those come with membership fees or event fees because, well, space costs money. Someone has to foot the bill at some point.

When I look at all the handwringing and confusion in HR today about the future of the profession or how it will change, the leaders I know have to be intentional about creating their own little packs of colleagues and advisors. But, those things take time and time feels like the thing that escapes us.

But we also have to have a place where we can come as ourselves instead of peacocking around and pretending we have all the answers. Authenticity is thrown around a lot but in reality, it’s about creating a space where we feel comfortable with reality. Nobody has all the answers.

And while hierarchies feel comfortable, I can tell you that in private conversations, the biggest names that get in front HR audiences are questioning what’s going on just as much. They just have the ability to come up with better sounding answers.

Do we need this?

I don’t know. Maybe people feel like they have a sense of community already. But I think a space that operates as a community with fewer boundaries is a good thing.

I don’t have answers beyond that, though. I am interested in your honest take. Do we need something like this or do you get your fill elsewhere?

What else is happening

• U.S. Department of Labor Launches ‘Make America AI-Ready’ Initiative. The government’s latest workforce strategy is a seven-day AI course delivered entirely by text message. It’s designed to be accessible, quick and simple, which is another way of saying your AI education now competes with spam texts and verification codes. The future of work, apparently, fits in your notifications.

• HR Leaders Say They’re Optimistic Despite Anticipating More Change in 2026. Nearly three-quarters of HR leaders say they feel optimistic, but two-thirds also expect even more disruption this year. Culture, engagement and constant change are still major problems, and employees don’t actually share leadership’s confidence. So it’s optimism… with a heavy dose of denial.

• Report: CHROs Now Among Highest-Paid Execs at Public Companies.

  While workers watch their pay shrink, chief human resources officers are suddenly joining CEOs and CFOs in the boardroom penthouse. A Conference Board study shows CHROs appearing as named executive officers jumped 55% between 2021 and 2025, with AI hype and talent wars turning “HR” into code for high compensation.

• Half of Companies Are Cutting Compensation To Fund AI Investments. In the race to feed the algorithm, 54% of surveyed companies admit they’re raiding employee pay to bankroll AI projects. Bonuses, equity and even base salaries are all on the chopping block, and many bosses plan to smear tiny “peanut butter raises” evenly across staff. Cutting compensation to pay for robots. What could go wrong?

• U.S. Companies Plan to Accelerate Global Hiring Despite Major Workforce Headaches. Companies are expanding globally and hiring more—but also admitting they can’t find or keep talent. Nearly half say attracting international workers is “very or extremely challenging,” while burnout, turnover and “quiet quitting” are still wrecking engagement. So yes, hiring is up, but so is the chaos that comes with it.

• Why AI Job Cuts Are Turning Brutal. The era of cushy exits is over. Now people just lose Slack access and find out they’re fired. HR leaders say layoffs have become “sterile and surgical,” with less human interaction and more speed as AI reshapes companies. Turns out when efficiency is the goal, dignity is optional.

Have a great rest of your week!

Welcome back to another issue of The Turn.

I was able to get back to an Unleash conference, the first for me in more than a decade. I came in the day before and was able to plow through meetings, sessions, and some talking with vendors.

But, what got me was the keynote. Not on AI (as one would expect) but on failure.

Failure, by the way, isn’t a popular conference topic. Shocking, I know. Vendors don’t build booths around it. Nobody wants to tell stories about failures.

Amy Edmondson is different, though. She’s spent 30 years studying failure, and her argument cuts against the dominant mood of every AI conversation happening in organizations right now. “Our intuition about failure fails us,” she said. We learned failure was bad before we could reason about it.

Her framework separates failures into three types. Basic failures are preventable. They are often single cause in familiar territory and better systems can drive them toward zero. Complex failures are the perfect storms where multiple factors converge into a disaster that hinged on no single point.

Intelligent failures, on the other hand, are what you get when you run a real experiment in new territory: the outcome wasn’t what you wanted, but the information was exactly what you needed.

Intelligent failure has specific requirements. A credible hypothesis, a goal the organization actually cares about, and a scope sized to generate knowledge rather than to generate a report. Those exist because it’s supposed to teach you something. “An intelligent failure in your organization happening a second time,” she said, “is no longer intelligent.”

Edmondson described a conversation with senior executives at a financial services company who told her that failure might be acceptable in good times, but right now, facing uncertainty, “it’s important that everything go well.” Her response? “When you implicitly or explicitly declare failure off limits, you should expect to be kept in the dark.”

An AI story without the AI mention

That’s the AI implementation story playing out right now across most large organizations.

The pilots are staffed with enthusiastic early adopters with the conditions controlled. The goal, even when nobody says it out loud, is to produce a result that justifies the next stage of investment. Yet, the next stage inevitably fails to deliver value (like most AI initiatives to date). As she says, “the pilot didn’t prevent the fiasco, because the pilot was a success.”

A real pilot looks different. It runs under representative operating conditions. Its explicit goal is to learn, not to demonstrate.

What makes that hard isn’t process design, though. It’s what Edmondson describes as the anxiety zone: the environment where someone on the ground thinks “I’m afraid I need help. I can’t ask for it. I will look foolish.”

Organizations willing to say publicly where AI is working and where it isn’t, to treat the honest account as more valuable than the polished one, are going to learn faster than everyone performing confidence. The ones that can’t will find out what they missed when it’s expensive.

Education, early career professionals, and HR’s role

I was also invited to a discussion about how education is struggling to keep up with the changing needs of industries. It was a diverse group across workforce leaders, researchers, consultants, and folks like me who really wanted to hear what people saw as an opportunity.

Alexandra Levit led the discussion and it was enlightening. She’s the author of many books, including Make School Work, a focused look at work-based learning.

Reducing stigma about trades, better AI training earlier, and how we can create a national policy when so much of what we’re doing is run on a state-by-state or even down to an individual school level.

I came away with two takeaways.

Many trades have already figured out the high school-to-career pipeline. It’s one of the meaningful ways that districts have kept graduation rates steady while providing students with the opportunity to jump start their careers. White collar work could (and should) spend time learning from them.

The second takeaway is a little more troubling. As we are diving in head first into AI in education, I continue to wonder how we build up skills like judgement. One of the things I heard from Alexandra is that she can’t use anything out of Claude as is. How does she know it’s not ready for prime time? Years of experience doing work by hand. Seeing how words and numbers come together not only give you judgement, they give you taste.

Building taste and judgement has always just been a repetition game. Do things enough times and you figure out what good should look like and what it takes to get there. But if AI is doing the heavy lifting, how can you judge something as good or complete?

AI boosts developer performance (with a catch)

I’ve strung together a number of posts recently critical of AI, so much so that I got asked if I was anti AI at Unleash. My latest on Reworked talks about AI actually coming through on its promises: Freeing people up to do work. And, while AI has been lauded as an accelerant for mid- to senior-level developers, it’s actually doing numbers with junior developers.

Of course, that comes with a catch.

AI has killed a lot of the collaboration between development teams. Instead of asking a coworker how to solve a problem, they are solving it with AI.

Which, is good and bad. Good in that those senior devs can focus instead of fielding questions. But knowledge transfer? Judgement? The why behind why you do certain things? Those things are missing.

That’s what I worry about. We don’t understand the long term consequences of this, either. Maybe things will turn out fine. But instead of wishing and hoping, I wonder what we do to intentionally build skills necessary to be a good partner with AI if and when we use it. How do I know what to look for? What are my own tendencies as a human being with this very agreeable technology?

In the waning days of World War II, a Manhattan Project scientist named Vannevar Bush wrote an essay in The Atlantic that described a problem that feels as fresh today as it did more than 80 years ago.

The world was producing knowledge faster than anyone could absorb it. Every field was generating groundbreaking discoveries. Every discipline was advancing at breakneck speed.

The tools available to make sense of it all were filing cabinets, index cards, and more books and papers than a person could ever hope to read.

Bush called it a problem of connection.

We had learned to grow information of incredible volume. We hadn’t learned to connect it.

His proposed solution, the memex, was a desk-sized machine that could store libraries of material on microfilm and, more importantly, let users link items to each other. Rather than organize by category like a filing cabinet or keywords like an index at the end of a book, it would work the way a curious mind creates sense from noise. The way the electrical impulses that drive a human heartbeat can feel like the beat of a Green Day song.

The technology to build the memex didn’t exist, but the core principle of this idea would shape eight decades of grappling with a now evergreen challenge: Too much information, too little wisdom.

Technology catches up with the vision

Twenty-three years later, Douglas Engelbart demonstrated the mouse, the graphical interface, and live hyperlinks to a room full of people who had never seen any of it. It became known as the Mother of All Demos. Alan Kay took those ideas further at Xerox PARC, building systems that treated the computer as a dynamic medium for thinking, not just data processing.

Then Bill Atkinson, a programming genius working on the original Macintosh team, had a psychedelic-driven vision on a park bench in Los Gatos. Human knowledge was siloed. Physicists knew things. Poets knew things. Musicians knew things. None of them were talking to each other, though.

His answer was HyperCard, released in 1987. It was my first experience with what would become the building blocks of the modern internet.

It was a virtual stack of cards that lived on your computer that could be linked to one another dynamically. Ordinary people with no formal computer training could build their own associative webs of knowledge. Teachers built learning tools, businesses built interactive knowledge bases, and I used it to build a choose-your-own-adventure style game that was about as sophisticated as you could imagine for an elementary school kid.

I believe poop was involved somewhere in the storyline.

Two brothers also used it to build a strange, contemplative game called Myst that became the best-selling PC game for nearly a decade.

HyperCard was, as Alan Kay later described it, symmetric. The person consuming information and the person creating it were on the same continuum. You could open any button and see how it worked. Like a jazz musician, you could use that knowledge play the same notes or you could iterate and expand.

Then Tim Berners-Lee, working at CERN on a NeXT workstation, built the first web browser. Inspired by HyperCard, the web as we know it today arrived. HyperCard faded as the web of information expanded beyond what we could store in a single computer.

But something important got lost in the transition.

We are building more file cabinets again

The web that actually emerged, the one we live in, is still mostly organized by taxonomy. Search by keyword, find your answer, and go on your way. The associative layer, the one where connections between unlike things produce genuine insight, mostly didn’t get built. Websites became self-referencing silos. Social media favored algorithms that give you more of what you already have.

We got abundance. We still didn’t get wisdom.

Information is cheap and plentiful. Wisdom is what you develop after you’ve seen enough information in enough contexts to recognize a pattern that isn’t obvious. It requires connection, not just retrieval. It requires someone or something that can look across the silos and say: These things are related, and here’s why it matters.

AI is arriving in organizations right now with a promise that sounds familiar. More information, faster. Cheap wisdom and abilities without all that thinking work. The implementations being built and deployed are largely retrieval and regurgitation tools.

Bigger, faster, and, sometimes, hallucinating filing cabinets.

AI is genuinely useful and it’s hard to describe how powerful it can be. We can’t build wisdom without knowledge. When I describe to my daughter how we researched before the internet, I might as well be talking about the Library of Alexandria.

But the way we use these tools is not solving the fundamental question that Bush was grappling with in 1945.

The tools that actually move people from information to wisdom are the ones that surface connections people wouldn’t have made on their own. They tell you not just what is true but what relates to what else, and why the relationship matters in your specific context.

When I see the information bloat, lack of vision or purpose, and thousands of copy and pasted AI posts that have the same beats, the same messages, and the same conclusion, I know we haven’t answered the fundamental question because we don’t even know what the question really is.

Connection, not collection

Every generation since (at least) 1945 has continued to make access to vast stores of information easier and declared victory. Certainly this will be the invention that solves our problems. Yet, information multiplies and we still feel like we don’t understand our world.

The hard problem is connection, not collection.

The tools we use to solve this problem reflect the assumptions of their builders. HyperCard was built by someone who wanted ordinary people to be authors to share their sense with the world, not just readers. AI is being shaped right now by the assumptions of the people deploying it.

Those assumptions are worth considering.

For example, when Anthropic publishes a study that reduces an occupation to collections of tasks and skills rather the connection of them, it feels like the record is skipping again. The theoretical AI coverage proposed is the type of fever dream that we’ve seen before.

There is genuine novelty with AI innovation (and, sure, a little bit of terror). That’s why it is so frustrating to see these abilities reduced to “answer engines” or debating which (contextually devoid, individual) tasks that AI can or cannot do.

Can AI help you connect with the world better? Can it create understanding where discernment is difficult? Can it help you make better decisions than you would’ve made on your own? Can it make you a better person or give you abilities beyond multiplying my own foolishness at unbelievable scale? Can it make something better that people actually want?

I think so because I’m a silly optimist, but it’s not how we’re using it.

I don’t want a faster, scaled up version of my thinking or abilities when it can’t crib the connections I’m making in my head. I don’t want a machine that can take the collection of tasks that makes me good at what I do without the ability to ask, “Wait, why are we even doing this?”

Someone still has to ask the right question. Someone still has to decide what the pattern means for this organization, this moment, these people, and find the right medium to tell that story so that someone who gives a damn will understand.

If all we are doing is just reinventing a faster file cabinet (again), then what the hell are we doing? Creating a brainless, context-less collection of information, tasks, and skills that can scale stupid work 10x?

I believe Vannevar Bush’s vision is still out of reach, at least in practice. It’s worth asking if we as people want to use the very capable technologies we already have to build wisdom or connection or if we are always going to try to settle for shortcuts and speed?

I think there is still a market for people who care about making sense out of noise rather than noise out of sense. Let’s hope so, at least.

A long, long time ago when I was still in HR, I watched two managers face the same crisis in different ways. Business was down, we had to shift people, and employees in the organization were frustrated and scared.

One gathered his team in a conference room the morning the news was going to be announced. He didn’t have all the answers, but he told them what he knew, admitted what he didn’t, and promised to keep them updated.

The other manager waited for the official talking points. When they came, he sent them via e-mail. By the time they got the sterile news, the team had already heard from their colleagues that change was coming.

A month later, both teams had the same leaders and a similar group of employees as they had before but very different levels of trust and performance.

Back then, all I had was vibes for what I saw. Clearly, the first manager was operationally better and a more empathetic leader. You could see signs of it in his employees too. But why did it seem like his team also buckled down more and were more willing to put in the unpleasant work to turn our fortunes around?

If the story is familiar, it’s because this pattern plays out everywhere. And now, SparkEffect (formerly Waldron and Torchiana)’s 2025 Trust in Turbulence report puts numbers to what many of us have seen up close: Trust is an operational variable that can predict whether teams hold together or fall apart when things get messy.

The numbers tell a simple story

When employees trust their leaders, everything works better. The key findings from the report shows that in high-trust organizations, 97% of people say their organization has strong financial performance. In low-trust environments, it’s less than half. Retention jumps from 52% to 93%. Even basic clarity about the company’s direction doubles.

Trust comes from how leaders show up every day. It manifests in how they explain decisions, handle uncertainty, and follow through when it matters. During disruption, those daily actions decide whether a team holds together or falls apart.

Where trust actually lives

The data also answers a harder question: who holds that trust?

It might surprise you that it isn’t the CEO or the executive team. Having worked with executives, I know big decisions that rock companies feel like a pressure cooker for them personally. They feel like everything they do is under a microscope.

But trust is really owned by the managers who talk to employees every day. People place far more confidence in their direct leaders than in “the organization.” But that confidence is brittle. When disruption hits, trust in their direct managers is what drops sharply. Their trust in the broad organization? It barely moves.

That should make every executive pause and consider where the pressure truly resides. The moment turbulence hits, the leaders closest to the work absorb the shock first. They become translators and stabilizers, all while trying to keep the work moving as if nothing was happening. If those leaders don’t have clear direction or support, the trust gap starts there and spreads fast.

The research makes a point that’s easy to overlook: leadership across the entire organization is strategic infrastructure. It’s as essential as having an office roof that doesn’t leak or a warehouse that has been upgraded to modern safety standards. You can’t build trust through slogans or one-off initiatives. You build it from the ground up by equipping managers to lead with clarity, fairness, and empathy. That’s especially true when they don’t have all the answers.

And when disruption happens, scaling your leadership capability is the only way to keep the value of trust intact.

When the waves hit

Every leader hits a moment when the room goes quiet. A tough call, a bad quarter, a change that lands wrong...

Values set the next move. They shape the words you choose, the tradeoffs you accept, and the people you protect. They decide who hears first, who gets context, and who gets your time.

Comfort tempts shortcuts. Values hold the line. They ask for plain talk when news stings. They demand fair process when speed feels easier. They keep promises in view when pressure shows up.

People watch the choices that their direct managers make. They see who they stand with, how they explain it, and whether they stay present when the room gets heavy. Those signals become the story employees tell about working there.

The teams that came out stronger shared four habits, and none of them are complicated:

• They communicate early and plainly, even when the news is rough

• They explain the “why,” not just the “what”

• They treat people with fairness and empathy in the moment

• They invite questions, even when they can’t answer all of them

Those actions sound basic, but they’re show up rarely under pressure. The report found that when disruptions were handled well, employee trust actually rose above baseline. When they were handled poorly, trust dropped by nearly 30%.

The elasticity of trust

SparkEffect uses the term “elasticity” to describe how well trust stretches under pressure and snaps back afterward. I love it. The idea is simple: some organizations absorb the hit, learn from it, and emerge steadier. Others crack.

In high-elasticity environments, employees say their companies retained key people, sustained reputation, and had a clear plan for the next challenge. In low-elasticity ones, those same scores collapse. Strategy clarity, arguably the foundation of leadership,bottoms out almost completely.

That’s what happens when trust runs out.

Most crisis plans focus on risk management: minimize damage, restore order, move on. The better leaders treat disruption as an accountability moment. They use it to show who they are when things aren’t easy. When handled with transparency and care, those moments turn into proof points of integrity.

Building trust on purpose

Trust can’t be built with values statements or an executive leadership retreat on purpose. It’s much more pragmatic than that. The research suggests four practical moves every organization can make to meaningfully support trust:

1. Support your frontline managers. They hold the most trust and the most risk when things go wrong. Equip them. Have them build a bank of trust when those hard moments hit.

2. Rehearse crisis leadership before you need it. Practice clarity and fairness in small moments so they’re automatic in big ones. These moment count and become evidence for or against your leaders.

3. Design fairness into decisions. Ask early: Is this clear? Is this fair? If I described what I did to an outsider, would they get it? If there’s hesitation, there’s work to be done.

4. Measure trust like you measure performance. What gets measured changes behavior.

Trust shapes the outcome of every disruption, from how people talk about leadership to whether they stay to help fix what broke.

What endures

Trust grows in the moments that test conviction. It takes shape through clarity, fairness, and steadiness when conditions shift. Each disruption becomes a record of what leaders chose to stand for.

Leaders who ground every decision in those values leave something lasting: a pattern people can rely on when change comes again. That consistency is the foundation of endurance, the quiet strength that carries a company forward.

That’s it for this week!

I’ve lost count of the number of HR Tech Conferences I’ve attended since my first one in 2009. In the dingy basement at the back corner of McCormick Place, the show was much smaller. You could walk every aisle in the expo hall in a brisk 5-10 minute walk. You could fit all the attendees in one of the breakout rooms used in 2025.

Maybe that’s an exaggeration, but you probably weren’t there.

The spectacle that exists today makes it impossible to get a feel for the room the same way you could 16 years ago. That makes me more convinced than ever that HR Tech is a reflection of our own experiences as much as it is a bellwether for what’s going on in the industry.

Feeling cynical about the crest of the AI hype cycle? You’ll find plenty of reasons to embrace the cynicism. A booth proudly proclaiming to be “like ChatGPT but for hiring” is both unintentionally hilarious and an indicator of how much AI has latched onto every last function of HR, whether buyers actually want it or not.

Feeling like innovation is starting to ramp up? Even beyond the ruckus of AI and agents, there are some interesting things happening that work leaders have needed for far too many years.

Feeling hopeful for a soft economic landing and continued investment in the industry? The show size (as big as ever), increased buying behaviors, and a Fed interest rate cut made those proverbial wallets feel a little looser.

Feeling like we’ll go back to reinvesting in employee experience like we did in 2022? Well, even a Rorschach test can do only so much projection.

So what am I seeing?

Show me, don’t tell me

AI was ubiquitous. There were a scant few booths that didn’t have a mention of AI and disappointingly, no strong counter to the AI-fication of everything. HR Tech can be a miserable place in the throes of a hype cycle. The eyes glaze over like looking at the menu at Border Grill for the seventh time.

What would’ve been nice to see is a strong, clear AI story. Instead, there was a lot of bark and little bite.

Vendors had issues answering questions like:

• How does your AI work in plain language?

• Who is using your AI solution?

• How is it improving outcomes?

• How is it improving the experience, especially for employees?

Of course, I wasn’t talking to the product leaders (I didn’t think I’d even be coming to the event until a few weeks ago). But very few people generally do. If your whole team can’t answer these questions credibly, it’s bad.

Ultimately, buyers aren’t looking for AI in and of itself. They are looking for answers to problems their organization is facing. A lot of them are distrustful of big promises because HR technology providers often struggle with basic automation and janky integrations already. Anyone who is in the CHRO seat has had a major technology failure in the last five years. Even if intrigue is high, they are probably going to focus first on getting the basics right.

Skills no longer pay the bills

In the paraphrased words of Supertramp, goodbye skills, it’s been nice. Hope you find your paradise.

What was once the hot trend for a few years was much less prominent this year. I’m not surprised, though. Skills are hard to quantify and capture in the same way that credentials or years of experience are. A credential is binary. You either have it or you don’t. Skills have a whole range of possibilities. And even people at the same skill level will vary.

Not only that, there’s a whole philosophy around taxonomy and organization that carries some very strong feelings. That makes the match, even for true believers, about as easy as finding the owner a glass slipper after that late night party in Eye Candy.

Pragmatically, we need some sort of answer for skills. Buyers seem to be struggling here but they also seem happy to ride it out with existing tools.

Does anyone care about the employee experience anymore?

I get that the pendulum has swung power back to employers but wow. No wonder employees are slow walking AI adoption. Even the hint that AI could do some work that humans are doing has led people to talk about EX as an afterthought. Or worse, that AI will make the employee experience better (which just isn’t showing up in any data).

This is real short-term thinking. There aren’t too many companies out there with sustainable success that also don’t at least try to create a great employee experience. And I believe EX is innate in an organization, through good times and bad (or whether the power dynamic is in their favor or not).

The thing is, being a good workplace also stands out more when you have the option to act like a jerk and still get people to work for you. Maybe vendors feel like EX is implied but buyers do not.

Cheers and jeers

What was good and what was bad?

Cheers: New team

It was great getting together with some of my new (Mary Ellen Slayter and Alec Cherrin) and not-so-new (Steve Smith and Anthony Spangler, APR) colleagues at Rep Cap and having some great meetings at HR Tech. Everyone was excited!

Jeers: Vegas

I got sick, my feet still kinda hurt, and the dry air never goes away. I get why HR Tech is in Vegas, probably for the long haul, but I don’t have to like it. I want to go back to a real city, maybe one with some humidity and things besides Vegas-priced and styled bars and restaurants.

Cheers: Old and new connections

It was great seeing so many people (too many to count, I’m sorry, I can’t tag you all). As a guy who’s always happy to stay in my cave of an office away from socialization, the sprint of talking with this many people all at once is a lot. My heart is happy and I was glad to see y’all but I’m also happy for some quiet again.

Jeers: Badge scanning

I don’t know what happened but a bunch of companies decided to get really aggressive with the badge scanning this year. I had multiple people scan my badge without even asking and it seemed like everyone was looking for those sweet leads. My month-old email inbox is suffering already.

News from the show

• SAP confirms completion of the SmartRecruiters acquisition - link

• Visier Inc. launches Manager Agent - link

• Darwinbox launches Super Agent - link

• Employ (Jobvite/Lever/JazzHR) showcases AI Screening Companion - link

• Perceptyx debuts AI Agent Suite and reimagined platform - link

• Cornerstone OnDemand presents next evolution of the Cornerstone Galaxy platform - link

• RADICL launches AI-native platform to reimagine how people and teams organize around work - link

• Beekeeper introduces Manager Hub and My Portal for frontline EX - link

• beqom introduces Pay Equity & Transparency solution - link

• PageUp launches AI Skills Matching and “Paige” virtual assistant - link

• Remote expands global HR & Payroll with Remote Surveys and enhanced HRIS - link

• Salary.com introduces CompAnalyst AI Suite - link

• Skillsoft unveils next‑gen Percipio learning platform - link

• Proctorio announces Secure Interviews (anti‑fraud for interviews) - link

• RChilli Inc. introduces AI agent capabilities for autonomous recruitment data ops - link

• Nayya unveils year‑round, AI‑driven benefits actions and guidance - link

• Chime Workplace integrates financial wellness with Workday - link

• Guardian Life expands Guardian Access APIs for real‑time benefits data exchange - link

• EMPTrust Employee Onboarding Modules debuts EVA Section 2 Employment Verification Agent for I‑9 - link

• TraineryHCM launches AI in ReviewCloud + new JobArchitect module - link

• iCIMS releases the 2025 State of Frontline Hiring report - link

• ADP announces Lyric HCM named a Top HR Product of 2025 - link

• Workday announces Workday Wellness named a Top HR Product of the Year - link

• Paradox, A Workday Company announces Immersive Job Preview named a Top HR Product of the Year - link

• Eightfold AI announces recognition as one of HR Executive’s 2025 Products of the Year - link

• RedThread Research announces Ingentis has joined its Tech Consortium and previews member showcases at HR Tech - link

• The Josh Bersin Company announces 30 AI HR Tech Trailblazer vendors to be unveiled in the HR Tech 2025 keynote - link

• HR Executive & HR Tech announce the 2025 Top HR Products of the Year - link

A note on this newsletter going forward

I am still working on getting up to speed in my new role, so things might be touch-and-go for a bit as I get into a new rhythm. Stay tuned for updates on the evolution, and maybe eventually some new brand and name. If you’re subscribed, you won’t miss it. If you’re not? Well, I pray to the LinkedIn algorithm that you’ll find me sooner than two weeks later.

That’s it for this week!

AI runs on 1s and 0s. Binary is clean. It’s precise. Yet, how that binary is interpreted through layers of code that run the latest AI and automation technology is definitely not precise.

So when I hear HR or marketing leaders ask, “Should we use AI for this?”, I know we’re stuck in the wrong kind of binary thinking. That’s not anyone’s fault, though.

The way most teams have been taught to think about AI is incredibly reductive: automate or don’t. Human or machine. Yes or no. That might’ve been fine when automation meant a glorified macro. But now we’re building systems that talk back, generate ideas, even “reason.” The stakes are higher and the question needs to grow up.

We need to start asking different questions. Stop asking whether AI belongs. Start asking what kind of partnership you’re designing between AI and your people.

Let me explain.

The Question That Gets You Nowhere

Almost every AI conversation I have starts with some version of:

“We’re thinking of using AI to do [X]. Have you used this before? Should we?”

That’s like asking, “Should we use electricity?” It’s too broad, too vague, and completely divorced from the actual work. Some tasks are ripe for full automation. Others? You’d be out of your mind to take the human out of the loop.

There’s a Better Question and a Smarter Framework

Stanford researchers recently published a framework called the Human Agency Scale (HAS), and although it’s dense as academic research tends to be, it’s also surprisingly pragmatic.

HAS maps AI-human collaboration across five levels, from full AI control (H1) to fully human-led tasks (H5). It’s not flashy, but it’s honest. And honestly, that’s what most teams need right now. We should talk about how AI should show up in the work, not just whether it should.

Here’s the shorthand from the research:

• H1: AI runs the show. Humans aren’t needed.

• H2: AI leads, humans keep an eye on it.

• H3: It’s a true partnership where both contribute.

• H4: Human leads, AI supports in the background.

• H5: AI, stays out of it. Human-only zone.

The intent is simple: Start by intentionally using the right level of AI for the task, not just simply saying yes or no to it.

It’s so intuitive in its nature that you might not think it’s a revolutionary line of thinking. But again, I still get the binary question over and over. Clearly, we need to reinforce this line of thinking even if it feels obvious.

Design for Trust

Another finding from the research is that AI that erodes human agency tends to backfire. AI that respects and amplifies human skill? That’s what ends up working best for everyone. The HAS isn’t just for leaders making decisions, but also a framework for employees to think and talk about where they want help.

The best use cases aren’t typically the flashiest that you might hear about (or that science fiction writers have dreamed up). Instead, they’re the ones that remove the grind without stripping the soul out of the job. Inbox triage, not creative direction. Summarizing claims notes, not breaking bad news to a customer.

Next time you’re evaluating AI for a workflow, don’t ask whether or not it should be automated. Ask:

• What’s the ideal mix of human and machine here?

• Who needs to stay in control?

• Will this make the work more meaningful—or more miserable?

Want to dig into this more? I wrote way more words about it for Reworked in one of my monthly columns.

What else did I see this week?

• Your Prize for Saving Time at Work With AI: More Work via WSJ

• $21 for an InMail? You’ve got to be kidding me via Shannon Pritchett

• When It Comes to AI Talent, $100 Million Can’t Buy What Matters via Reworked

• AI is changing the world faster than most realize via Axios

That’s it for this week!

This is a different sort of newsletter. When friends write books, we read them. When they are as good as what Robin Schooling has put together in Real HR: What It Is, What It Can Be, and How to Get There, we talk about them on Work Leader Weekly.

It clocks in under 100 pages according to Kindle’s somewhat-accurate estimate and it took me less than 90 minutes to read through it.

Here’s what I liked: Schooling doesn’t sugar‑coat the profession. She opens with field stories that feel too familiar: leaders defer tough calls, HR receives the fallout, budgets evaporate when people work turns expensive. The book’s through‑line is simple: stop defending a system that keeps you powerless.

She positions HR as the place where risk collects and credit evaporates. Practitioners juggle compliance, coaching, and cleanup while authority sits elsewhere. Her diagnosis lands because she details the mechanics, not the drama. When finance trims the head‑count plan or operations bypasses policy to hit a deadline, HR starts behind the curve and pays interest on the decision for months.

“You’re more than an HR policy manual with legs.”

That sentence arrives early and sets the tone for the book. Schooling urges every HR professional to write a personal manifesto. The exercise isn’t therapy or self-aggrandizing. It forces clarity about personal purpose and limits. Without that line in the sand, HR absorbs every overdue task and owns failures it never controlled.

The middle chapters dissect culture theatre. Cupcake days, branded hoodies, emoji‑heavy Slack channels — none of it matters if managers reward toxic behavior or dodge feedback. Engagement lives in daily choices, and Schooling calls out leadership teams that praise values at town halls while ignoring the obvious mismatch on the floor.

“Best practices are a conduit to conformity.”

Schooling’s swipe at copy‑paste playbooks is well-timed, especially in an era of AI moving everyone to the mean. HR loves a template, yet she shows how imported frameworks become straightjackets. Context wins over fashion. When a process creates friction, strip it back. When a metric distracts from results, drop it. The book treats simplification as a strategic act, not a weekend tidy‑up.

Technology gets the sharpest critique. Vendors promise that AI will predict turnover, write job ads, and coach managers in real time. Automation can clear repetitive tasks, but it cannot rebuild trust or teach courage. Schooling reminds readers that data reveals trends, but people still choose whether to act. If leaders ignore red flags today, a prettier dashboard tomorrow changes nothing.

“You don’t fix broken trust with branded hoodies.”

That line doubles as a verdict on the entire quick‑fix mindset. Cosmetic perks, chatbots, and endless pulse surveys distract from the hard work of clear expectations, consistent feedback, and fair decisions. Fix the system before you ask employees to rate it.

Schooling keeps circling back to the only point that matters:

“HR’s purpose is to connect the capabilities of individuals to organizational success.”

With that anchor, every tool, policy, and conversation turns practical. If a process speeds alignment, keep it. If a rule blocks performance, rewrite it. If a technology buries judgment beneath scores and categories, walk away.

By the final chapter, Schooling offers no redemption arc, only responsibility. HR will gain influence the same way every function gains influence: by demonstrating value that leaders can’t ignore. That means framing problems in business terms, forecasting consequences, and refusing to carry risk that belongs elsewhere.

The takeaway for practitioners is brutal but freeing. Stop waiting for validation. Claim authority by drawing boundaries, applying evidence, and speaking in the language of outcomes. When AI hype rolls in, test it, but measure it against purpose. When leadership asks for a quick morale boost, show them the cost of ignoring root causes. When someone forwards a “best practice,” ask whether it solves the problem in front of you.

Real HR is a short book, but the ideas stretch beyond the pages. They demand daily vigilance: how much of your schedule is reaction, how much is influence? Which metrics inform decisions, which merely decorate slide decks? Whose priorities shape your workload tomorrow?

Schooling never promises ease because that magical world doesn’t exist. If you believe work can be profitable, fair, and sustainable, the text gives you language to press the point. It strips away comforts, clichés, and hero narratives until one challenge remains: do the work or keep polishing the mask.

That challenge is the book’s worth. HR doesn’t need another inspirational slogan. In an age where AI is coming for HR, clarity and challenge are what we need more than ever. Grab the book from Amazon today.

We’re in an era of deep economic instability. Tariffs are back (and gone, and back, and gone, and…). Trade tensions are escalating. The dollar’s dropping. The stock market swings between panic and euphoria on an hourly basis. And somehow, in the middle of all this chaos, employees are expected to keep calm, carry on, and “do more with less.”

The truth? People aren’t stupid, they’re disillusioned.

I recently wrote a piece for Reworked about Big Tech layoffs and how performance isn’t the real reason so many people are being let go.

Despite the narrative of cutting the fat from their payrolls, these decisions aren’t really about underperformance. Many of the people impacted were top contributors. The real drivers? A mix of economic posturing, shareholder appeasement, and internal politics.

It happens, but as I concluded in my column, truth matters:

“Companies need to acknowledge when their own decisions, rather than employee shortcomings, are driving layoffs. The longer they continue to rely on performance-based messaging to explain away poor planning, the more difficult it will be to regain trust and establish honest accountability.”

The disconnect between effort and reward is what’s breaking people’s trust. And if doing great work in an age where everything feels uncertain doesn’t shield you, what does?

When people can’t trust the economy, and they can’t trust leadership to protect performance, they start looking for something that feels steady and safe. And they won’t find it in companies that behave like fairweather allies.

It’s painfully clear that some companies are happy to trade long-term reputation for short-term stock bumps. They lay off workers in record numbers while raking in profits. They spin narratives instead of owning hard truths. They lose credibility and think they can buy it back later (like they do with their stocks).

But trust doesn’t work that way. You can patch it up once it’s broken, but it won’t be the same.

So if you’re in a leadership role right now, ask yourself: Are we building a place where people can trust the system they’re in? Or are we just hoping the perks will distract them from the instability?

You can be honest and still lead with strength. You can tell people the truth and still retain their respect. You can make tough calls and still protect your long-term credibility.

The world outside is unpredictable. That’s why the systems inside your organization must be grounded in clarity, fairness, and truth. I continue to be unmoved by the accusation that EX is soft. It’s not fluff. It’s the backbone of trust.

And trust is what keeps people showing up when everything else feels like it’s falling apart.

Don’t be the kind of company that cracks under pressure. Be the one people remember for doing the right thing, especially when it would’ve been easier not to.

QUICK HITS FROM AROUND THE WEB

• Alleged Mole Admits To Spying in Rare Drama for HR Tech via Morning Brew

• Spotify’s Head of HR Outlines Transformative Strategy for Talent Acquisition, Retention via the HRD

• Why HR Posters Matter via Punk Rock HR

• Taking AI Talent Out of FAANG (and Microsoft) via ERE

• Ford: If HR Tech ‘Isn’t Solving a Problem, It’s an Irritation’ via Unleash

• Thousands of North Korean IT Workers Have Infiltrated the Fortune 500—and They Keep Getting Hired for More Jobs via Fortune

• The People-First Workplace Approach Is Slipping Away, Researchers Say via HR Dive

• Quiet Cracking: A Hidden Workplace Crisis via TalentLMS

• AI Is Changing Work—The Time Is Now for Strategic Upskilling via the World Economic Forum

• Layoffs Reached a Near Record High in March, Most From DOGE’s Hatchet Job on the Federal Workforce via HR Brew

• Top Five Labor Law Developments for March 2025 via The National Law Review

Predictive Analytics Goes Off The Rails

I know my crystal ball is still a little hazy after the last few years but in the UK, maybe that isn’t as big of a problem. They have now developed an algorithm that can predict who will be murderers:

Researchers are alleged to be using algorithms to analyse the information of thousands of people, including victims of crime, as they try to identify those at greatest risk of committing serious violent offences.

This all feels very familiar.

That’s it for this week!

As my plane landed in Phoenix for isolved’s inaugural analyst event on Monday, my phone buzzed with messages and texts. Why? Deel and Rippling are locked in a battle straight out of an episode of Silicon Valley, complete with allegations of corporate espionage and Slack messages as star witnesses.

You might think watching two unicorns slug it out would be pretty entertaining. You’d be right. I almost got popcorn on my way to the hotel. Is all of this drama relevant to work leaders? Maybe not directly, but I’ll admit: I’m keeping an eye on it (And seriously, if the allegations are true — maybe don’t do that? Just an idea.)

But not every tech company story has to be ripped straight from the tabloids. My 1.5 days in Arizona with my friends at isolved were notably drama-free.

Honestly, a little more action wouldn’t have hurt, especially since customers weren’t around to dish out those great, informal insights between sessions. Usually, those casual conversations provide context that’s just as valuable as the formal presentations.

I did get a beer from a burro, though. Also, I got to see Taliesin West as an adult who could fully appreciate Frank Lloyd Wright’s masterful work. Not bad? Not bad!

As usual, I had some clear takeaways from the huge amount of things we covered.

1. Small is Big (No, Really)

A 500-employee company might sound modest unless you’ve actually tried running one. Without the right tech or approach, managing 500 people can feel more like wrangling 5,000. Trust me, I’ve been there.

Go to the typical vendor event and you might be shocked to hear this stat: fewer than 25,000 of the nearly 19 million businesses in the U.S. have over 1,000 employees. The vast majority of companies are far smaller, yet so many vendors often dismiss these businesses as low-margin, high-hassle clients. And to be fair, if you haven’t built your business around that, that can be exactly what it is.

isolved, however, sees genuine opportunity here, leveraging a hybrid sales approach of direct customer relationships and reseller and partner networks. Their flexible tech approach and shared sales and service model with payroll bureaus, ASOs, and PEOs can combine high-touch service with tech-forward solutions, all depending on what best serves their client’s business.

Could some of their product naming be clearer? Absolutely. I didn’t exactly get why they branded some of their products (like Perfect Payroll™) while others were left with just descriptive names. Their demos weren’t exactly mind-blowing, but they were practical, robust, and showed genuine awareness of SMB needs. AI agents were in the product roadmap as you might expect, but if anything, it was focused on the pragmatic.

Bottom line: Small businesses deserve solid software and support. I hope isolved keeps them at the heart of what they do as they continue their growth.

2. Quiet Confidence (Maybe Too Quiet?)

Payroll-first companies like Paychex, Paylocity, and Paycom quietly rake in billions annually. Paycor is nearly there too, soon to join forces with Paychex. isolved doesn’t publicly disclose revenue, but anyone who knows how to do some quick, back-of-the-envelope math based on employee headcount could tell you they’re comfortably holding their own.

Despite the company’s bold, neon-pink branding, the event itself felt intentionally low-key. No flashy walk-up music, minimal hype around upcoming announcements, and a product roadmap that felt reasonable and aligned with what I hear from companies outside the enterprise.

isolved’s strategic bets, particularly in hospitality, professional services, and staffing, are also backed by solid research rather than buzzwords and gut feelings. It’s generally a smart, methodical approach and refreshingly driven by ethnographic research (my colleague Aaron Delgaty would’ve loved to dive deep into the conversations they were able to have with users and buyers).

Bottom line: This is another reason why I like having customers there: Energy. Some of these features are going to be super helpful and customers should love the direction here. A little humblebrag now and then doesn’t hurt.

3. What’s Next for isolved?

Could isolved eventually mirror the Ultimate-to-UKG evolution? Possibly. They’re recruiting talent from large-scale HR tech companies and cautiously exploring the enterprise market.

But they haven’t lost sight of their roots. They continue to solidify their position in foundational markets that they’ve done so well in and steadily grow their direct customer business.

However, don’t hold your breath for immediate global expansion. isolved remains unapologetically focused on the U.S. market, leveraging partnerships (like Remote.com) to meet international customer needs. They acknowledge their North American focus, comfortable playing to their clear strengths.

Regarding future plans or potential exit paths (like the aforementioned Paycor acquisition by Paychex), isolved was open but not overly forthcoming. Their private equity backers, Accel-KKR, appear perfectly content to support their unwavering growth. Cheap money still isn’t cheap and an IPO isn’t a sure thing, even for companies with a solid track record and healthy profit margins.

Bottom line: While isolved’s event lacked the TMZ-esque drama of Deel and Rippling and other tech-forward events, maybe that’s precisely the charm of the whole thing: straightforward, reliable, and practical. Keep it real and keep growing, without the weird spy vs. spy shenanigans.

It was great to reconnect with peers in the analyst community like Steve Boese, Karen Steed, Stacey Harris, Tami Nutt, Joey Price, Nicholas W.J. Biron, Jessica A Cheung, Matthew Brown, George Rogers, Steve Goldberg, Ivan Oz, Jan Erik Aase, and probably a few more I’m missing. A few have already posted some recaps of the event and I’m sure there will be some fantastic takeaways.

Finally, it was great to get hosted at the event by the folks at isolved and it was a nice excuse to get out of the dreary PNW. Big thanks to Amberly Dressler and her team for all her hard work in making it a success as well as the folks at isolved who took the stage like Celia Fleischaker, Pragya (Malhotra) Gupta, Kelli Rico, Amy Mosher, Opal Wagnac, Megha Shah, Alice McDonald, Megan Coen, Meredith Reilly, and Todd La Fever.

Now, it’s time to be back in my cozy confines.

QUICK HITS FROM AROUND THE WEB

• AI Is Coming for the Laptop Class via Vox

• How AI-Enabled ‘Bossware’ Is Being Used to Track and Evaluate Your Work via ComputerWorld

• Job Redesign Around AI: Work Intelligence Tools Arrive via Josh Bersin

• Employers Are on an Unsustainable ‘Hiring Spree’ for AI Talent, Report Says via HR Dive

• Breaking the Stigma: Menopause Benefits Gain Momentum in the Workplace via ERE

• Federal Agencies Plan for Mass Layoffs as Trump’s Workforce Cuts Continue via NPR

• The “No Robo Bosses” Act (SB 7): How California’s New Bill Targeted at AI Could Impact the Workplace via California Labor & Employment Law Blog

• How Rippling’s Legal Action Against Deel May Affect the HR Tech Market via HR Executive

• Assessing the ‘Mood Music’ - Are Employee Surveys the Right Tool? via HR Grapevine

Flimsy NDAs and a Little Streisand

Meta’s effort to silence a whistleblower is possibly turning into a labor law fiasco. KQED reports that Meta’s attempt to enforce an NDA against a former employee’s tell-all book has now raised significant questions about labor law, inadvertently boosting the very story it sought to bury.

Two key points immediately jump out:

1. Labor law isn’t typically center stage in tech disputes, but Meta may have handed critics and federal labor officials a compelling case. Labor protections cover more than union organizing. They shield employees’ rights to speak openly about workplace issues, even controversial ones. Meta’s aggressive tactics could soon put them under unwanted scrutiny but part of the question will be how Trump labor officials read and enforce the law.

2. This saga is a classic example of the Streisand Effect. Many people would never have paid attention to the whistleblower’s book had Meta not taken such aggressive steps to suppress it. By overusing NDAs, Meta amplified attention and curiosity — exactly the opposite of its goal.

Meta’s situation underscores a valuable lesson: the harder companies fight to suppress information, the louder and more damaging the conversation becomes.

That’s it for this week!

We teach the next generation how to work, whether we mean to or not.

Cat’s in the cradle and the silver spoon… If you know the Harry Chapin song, you know how this goes: Kids watch their parents, learn from them, and often repeat the same patterns. Sometimes for better, other times for worse.

Work is no different.

For decades, younger employees have looked at their overworked, overwhelmed bosses and thought, “That doesn’t seem like a great deal.” And now Gen Z is saying it out loud.

As Fortune covered last week:

More than half (52%) of Gen Z employees report to recruitment company Robert Walters that they’d rather not be middle managers—according to a survey of 2,000 white-collar workers, 800 of which were Gen Zers. Taking note of burned out and unprepared managers, as well as the current state of white-collar work, Gen Zers are setting their sights on bigger dreams than becoming bosses.

Sound familiar? It should.

My generation of Millennials had the same hesitations about middle management, and for good reason. We watched Gen X and Boomers work late, take calls on vacation, and wear burnout like a badge of honor (then be first out the org when bad times came). Then we entered the workforce and were expected to do the same.

When we pushed back, we were called lazy. Now Gen Z is pushing back, and the cycle continues.

But here’s the thing: Every generation should want the next to be smarter about work than they were. If Gen Z rejects stress without support, if they question whether middle management is worth it, that’s not entitlement — it’s progress.

So instead of dismissing them, maybe we should listen. Maybe we should ask: What needs to change so that leadership isn’t just more stress for a slightly bigger paycheck?

The goal shouldn’t be to convince Gen Z to accept burnout. It should be to build something better — so that when it’s their turn to lead, they actually want to.

Meets Expectations? Not This Rating System

Five-point rating systems have some advantages, but they can also be problematic and discriminatory, at least in some cases.

A new study in Nature shows how even seemingly neutral rating systems — like customer reviews or performance scores — can reinforce bias. When people rate others, their judgments are shaped by existing stereotypes, whether they realize it or not. Over time, those small biases compound in ratings, creating systemic disadvantages like pay discrepancies and lost opportunities.

A better way, especially for shorter interactions like a customer survey, is to use a simple thumbs-up or thumbs-down rating. For longer interactions like in employment, there are some potential trade offs like a lack of granularity that comes over a period of time.

It’s easy to focus on obvious discrimination — blatant bias and bad actors are clear-cut cases. But the real challenge is recognizing how built-in systems, like the way we evaluate performance, quietly tilt the playing field.

It’s difficult to coach and train bias out of people. If fairness matters, we can’t just look at individual decisions. We have to rethink the structures that shape them. Good food for thought for all the performance management junkies.

QUICK HITS FROM AROUND THE WEB

• This Former HR Tech Exec Thinks Companies Are Losing Sight of Hiring Top Talent. She Wants to Help. via HR Brew

• Majority of Employee Referrals Made During Work Hours via SHRM

• How A Smart Watch May Lead To Unintended Concerns For Employers via California Labor & Employment Law Blog

• U.S. Workers Are More Worried Than Hopeful About Future AI Use in the Workplace via Pew Research Center

• ChatGPT vs Deepseek – Which Is a Better Sourcing Tool? via ERE

• Democracy Dies, First, in the Workplace via In These Times

• How Generative AI Is Pushing the Boundaries of User Interfaces via Reworked

• Apple to Push for Free Speech Tests in Labor Case at Fifth Cir. via Bloomberg Law

• Is the Workplace Less Friendly? Employees Say They Care Less About Having ‘Work Besties.’ via HR Dive

• How Musk’s Efforts To Reduce the Federal Workforce Compare to Efforts in the 1990s via NPR

Cybersecurity Still Matters

Getting hacked is bad. Getting hacked and losing your job? That’s brutal.

A Disney employee just learned this the hard way after a hacker stole their passwords and work data, uncovered potential violations, and ultimately led to them being fired. The former employee is suing Disney, claiming he never accessed racy material on his work device.

Companies love to tell employees to “be careful” with security, but let’s be honest — individual vigilance only goes so far. If one mistake can cost someone their livelihood and a whole lot of sensitive data, maybe the real issue isn’t just user error, but how we design security in the first place.

Also, if your password is still password123 and you’re still downloading random stuff on the internet, consider this your sign to change your ways.

That’s it for this week!

Dear Elida,

Today is the first day of a new decade. A decade where you will go from bicycling age to driving age. You will grow and learn so much, and I can’t wait to be a part of it. By 2030, you won't be done growing, either. You’ll just be beginning, if my experience is anything like yours. Like Elsa in Frozen 2, we are taking our own journey into the unknown.

One of my favorite things to do with you is to go on adventures. We have done all kinds of fun trips, from climbing Beacon Rock to hiking at Latourell Falls. Remember seeing all the dogs on the trail? I think you took a picture of every single one.

We went to the Ape Caves in October of last year. Maybe you’ll remember but it was dark and kind of spooky. We had lights but we couldn’t see everything. After getting used to the dark and the limits of our lanterns and flashlights, we were able to get along just fine. We spent an hour and a half underground and explored 1.5 miles of cave. 

You told me you were a little scared but that you wanted to keep going. That’s definitely your approach to life at five and three-quarters years of age. 

There are a lot of similarities between our experience spelunking and looking ahead a full decade. It’s easy to be overwhelmed or to be unsure. Like I told you that day, it’s okay to be scared. 

What we can see ahead of us is very limited. I know we’ll have some laughs and some tears. I know we’ll have some hard times. I know some days will feel easy. But, I can’t predict what will come around the next bend. Sometimes, that would be a pretty nice skill to have. 

What I can tell you is that if you give the next ten years what you gave the last five, you’ll be better than good. You’ve grown into an amazing little girl. You give me courage to step into the unknown like you do: Forging ahead, full of energy, even if I don’t always audibly share my self-doubts and fears like you so transparently do. 

2020 is going to be a great year and you’re a big reason why. Let’s take on this year like we did the last. 

Love,

Dad

As we twisted our way around Mt. Tamalpais on the way to the beach in our Subaru, something felt different. A sense of melancholy washed over me as I looked in the back seat. My daughter, now five, looked out her window at the redwoods getting denser and then thinning as we wound our way to the coast.

After a year of firsts — of exploring as many places as we could within a few hours of the Bay — we were reexploring some possible lasts. Maybe not last lasts, but almost certainly lasts as residents of the Bay Area. When a lazy Sunday morning lingered into an afternoon, as it often did, we found ourselves surrounded by the beauty of the Northern California coast. Those days don’t happen on vacation, they happen at home.

But California never truly felt the part of home.

I’ve read a lot of stories about people who have left the Bay Area. They talk about legitimate gripes about the area that I could identify. High cost of living? Homelessness? Transit? Toxic tech culture? Inequality? I get it.

In the end, all of the challenges that California faces are ones that other areas of the country face (or will eventually face, to some degree).

I had (and still have) a great job, one that supported us. It didn’t allow for a lot of saving or doing but we also weren’t ever wanting.

I also never felt ostracized. Which, as a straight, white, married male in his mid-late 30s who worked in a tech-adjacent industry, I stayed mostly in people’s comfort zone here. If you’re new to an area, it can be helpful to blend in but it can also feel a bit invisible.

I also really loved the area we lived in. The area of Marin we were in was a perfect combination of walkable community, suburban comfort, and city accessibility.

The sun dances across my daughter’s face as we continue to drive toward the Sonoma coast. I wondered what Elida would remember about California. I hope she remembers the small co-op preschool that her mom was able to be a part of. I hope she remembers a few of our adventures. I hope she remembers just the amazing beauty that was all incredibly accessible.

For me, moving far away from family and friends put my priorities in sharp relief. I love being able to wander and explore — it’s what ultimately drove me to move, and if we’re being honest, what made me happy to go along with other moves. What I missed was coming back to a place I could truly call home.

California, while glorious and nearly perfect if you have the means to make it work, didn’t have the key things that my wife and I both identified as important parts of our own childhood.

It’s not just the backdrop to the scene that mattered but the people who were in it.

We’re lucky to have people in California we call friends now, but our core is in the Pacific Northwest. That’s home. Not for now, but maybe as forever as we can anticipate.

“When can we come back?”

When you have a five year old, that’s always the question for any place that’s fun. This time, I didn’t know when we’d visit our favorite beach again. So, I told her we’d have to see.

She sat quietly as we listened to music. Our feet were caked with sand and the car smelled like sunscreen and sea air. After a few minutes, she broke her silence.

“I like that beach,” she said.

“So do I, baby,” I replied back.

“Do they have beaches where we are going to live?” she asked.

“They’re not as close but they definitely do,” I told her.

“I bet I’ll like it, too,” she replied.

I looked in the rear view mirror and smiled at her. She smiled back.

I think she’ll remember the right parts about California. I think I will, too.