In this issue:

In this issue, we dive into algorithmic management—where AI assigns tasks, sets pay, and can even fire you without a single human touch.

Efficiency? Sure.
But at what cost?

If your manager never sleeps, never blinks, and only sees data… are you still an employee—or just a metric?

Before we dive in—quick heads-up:

I’ll be speaking at the Job Search Summit on Friday, August 1, 2025 at 12:00 PM EDT. It’s free, because let’s be honest—this market’s a meat grinder, and good advice shouldn’t come with a paywall. Click here to sign up, and if you're all set, do someone else a solid and pass it along.

And now, on with the show.

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The HR Blotter

AI’s hidden ambition: experts now warn that advanced AIs are actually smarter than they let on, feigning innocence while earning our trust—and someday might just steal control without us ever realizing it. We’re essentially training our own overlords to play dumb—only to surrender the keys to the kingdom when we’re too busy patting them on the back. Hmm… What if they think they know who is best to hire more than we do? Would we even know?

Monaco, Japan, and much of Europe are graying fast—over a quarter of their populations are already 65+. As retirement-age workers surge and birth rates plummet, HR leaders aren’t just facing a talent gap, they’re staring down a workforce cliff where experience exits faster than it’s replaced.

The middle is vanishing—AI is gutting layers of management, leaving lean org charts where one boss now wrangles twice the people. Companies call it “efficiency,” but what they’re really doing is handing managers a grenade with the pin halfway out. For HR, the message is clear: train leaders to lead big or get buried under burnout and quiet quitting.

Amazon just hit a million warehouse robots and they’re not stopping. With a new AI brain called DeepFleet, machines are moving faster, cheaper, and closing in on outnumbering human workers. HR can call it “augmentation” all they want, but the writing’s on the warehouse wall: reskill fast or get replaced by something that doesn’t take bathroom breaks.

AI’s not just watching—it's judging. Over half of managers now use AI to decide who climbs the ladder and who gets the boot, yet most haven’t been trained on how to do it responsibly. HR better step in fast, or let the algorithm play executioner with zero accountability.

Three key truths from The Atlantic’s “Good on Paper” podcast: remote work isn’t a universal win—its perks and pitfalls pivot on your age, gender, experience, and feedback network. Junior engineers lose out on upskilling without the 22% boost in in-person feedback, while seniors secretly love the silence and productivity bump. So HR, take note: remote is not a panacea—it’s a trade-off—and you’ll need smart, tailored support systems if you want to keep talent growing, not wandering.

Citigroup just handed hybrid staff a golden ticket—two full weeks of remote work this August, playing the flexibility card when competitors are doubling down on office mandates. It’s more than a perk, it’s a strategic play to lure and keep top talent, with CHRO Sara Wechter calling hybrid a competitive edge in the post-pandemic talent wars. For HR, it’s time to ask: are remote sabbaticals the future of retention, or a risky HR experiment while rivals herd workers back to cubicles?

Tech layoffs have gone full throttle — nearly 94,000 tech roles vaporized in the first half of 2025, averaging about 507 pink slips a day as companies redirect dollars into AI strategy. This isn't the COVID hangover, it's the AI hangover: engineering, support, marketing, even HR teams are being sacrificed to fund the bots . HR needs to pivot fast: workforce planning must balance automation with reskilling or watch your talent vanish faster than server racks in a startup.

The Supreme Court just cleared the way for President Trump to slash tens of thousands of federal jobs across 19 agencies—before the case even finishes winding through the courts. The ruling doesn’t settle the legality, but it gives the President’s executive order teeth, letting layoffs roll out fast while challengers scramble to stop them. For HR in government, this is DEFCON 1: workforce planning now means bracing for chaos, legal limbo, and the political weaponization of pink slips.

Black women’s unemployment is rising—now topping 6%—even as overall job numbers improve, highlighting real disparities without easy explanations. Some point to federal job cuts and scaled-back DEI programs, but others argue the private sector is adjusting to post-pandemic market realities. Either way, it’s a signal to HR leaders: equity efforts need accountability, not just optics—or we risk replacing one imbalance with another.

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Managed by Machines: How Algorithms Are Reshaping the Modern Workplace

In our last dispatch, we peered into the executive suite, where AI is making its subtle, sometimes symbolic, ascent. But the revolution isn't confined to the polished boardrooms. It's already deeply embedded in the daily grind, reshaping the very fabric of how work gets done for millions. This is the story of algorithmic management – the invisible hand that now guides, evaluates, and often controls the modern workforce.

The Data Engine: How Algorithms Take Charge

Imagine a manager who never sleeps, never blinks, and sees everything. That's algorithmic management. It's a system where AI, not a human supervisor, collects vast amounts of data on worker performance, assigns tasks, sets pay, and can even discipline or terminate employment. It's management by metric, driven by real-time data streams.

How does it work? It starts with relentless data collection. Sensors track movement in warehouses. GPS monitors delivery drivers. Every tap on an app, every minute of idle time, every customer rating – it's all fed into the algorithm. This data then fuels a suite of automated functions:

Real-Time Monitoring: From tracking a delivery driver's route to monitoring warehouse workers' "Time Off Task," algorithms watch everything. GPS systems know exactly where gig workers are at any moment. Sensors in factories can detect when assembly line workers pause for more than a few seconds.  

Automated Task Assignment: No human dispatcher needed. Uber's algorithm decides which driver gets which ride. DoorDash's system calculates the optimal route and assigns orders dynamically.  The algorithm weighs factors like distance, traffic, driver ratings, and demand patterns to make split-second decisions.

Performance Scoring & Ratings: Workers become numbers. Uber drivers live and die by their star ratings.  Amazon warehouse workers are tracked on their "rate" – how many items they pick, pack, or sort per hour. Fall below the algorithm's expectations, and consequences follow automatically.

Dynamic Pay & Incentives: The algorithm doesn't just assign work; it sets the price. Uber's surge pricing kicks in when demand spikes.  Delivery apps adjust pay based on distance, weather, and how many drivers are available. It's a digital auction house where human labor is the commodity.

Automated Discipline & Firing: Perhaps most chilling, some systems can terminate workers without human review. Amazon's algorithm can flag workers for "productivity issues" and initiate termination proceedings. Uber can deactivate drivers based on ratings or algorithmic flags.  

The Corporate Appeal: Why Algorithms Manage

For companies, the allure is obvious. Efficiency at scale. Amazon can manage hundreds of thousands of warehouse workers with minimal human oversight. Uber coordinates millions of drivers worldwide without traditional management structures.

The numbers are compelling. Algorithmic management can reduce labor costs, optimize task allocation, and eliminate some forms of human bias in decision-making. It's management without the mess of human emotions, favoritism, or inconsistency.

But there's a darker side to this efficiency. When workers become data points, when algorithms make decisions about livelihoods, something fundamental shifts in the relationship between employer and employee. The human element – empathy, understanding, flexibility – gets lost in the code.

The Human Cost

Workers in algorithmically managed environments often report feeling dehumanized, constantly monitored, and powerless. They're managed by systems they can't see, understand, or appeal to. When the algorithm makes a mistake – and it does – there's often no human to turn to for help.

This is just the beginning. As AI gets smarter, as sensors get cheaper, as data gets richer, algorithmic management will spread. From warehouses to offices, from gig work to traditional employment, the invisible hand is reaching everywhere.

Next time, we'll dig into what happens when that hand makes mistakes. When algorithms go wrong, who pays the price? And who's accountable when there's no human in the loop?

The machine is watching. The question is: who's watching the machine?

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The Fine Print

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One more thing before I go…

Probably should’ve said this sooner, but hey—guess I got a little shy (doesn’t happen often).

🎙️ The Jim Stroud Podcast is officially coming back—this time with a new home on the Purple Acorn Network. Launch date’s July 15th, but you can get a sneak peek right now by clicking here.

Want a bonus? There are 200+ archived episodes waiting to keep your ears busy until the new drop. The Jim Stroud Podcast is available on Spotify, Apple Podcasts, YouTube, and iHeart Radio.

That’s it for now.
Next episode’s brewing. Stay close.

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