Most HR technology companies talk about AI as if the chat box is the hard part. They focus on the assistant and its clever answers. Asking a question in plain English looks like magic. It is also the easy part.

The hard part is the data behind the answer. HR data is often old, incomplete, or in conflict with itself. A polished answer does not solve that problem. It merely gives the problem better manners.

Effective AI processes require a comprehensive system of record. The system must know what happened, who did it, and which source was used. It must also understand why sources disagree. Without that foundation, AI is just guessing faster.

That was the key lesson from a recent discussion with Aderit. The company is building a shared data model beneath existing HR systems. It brings information from separate platforms into a common layer. That layer shows where records agree and where they differ.

The demo offered an example. The same job can have different descriptions in recruiting and compensation systems. Both descriptions may be correct for their purpose.

The disagreement may reflect reality rather than bad data.

Each HR silo delivers its own version of the truth.

• Recruiting sees a job that must attract candidates.

• Compensation sees duties that must be priced.

• Operations sees the work people actually perform.

• L&D sees the underlyings skills and training requirements

These are not failed copies of one perfect description. They are facets of the same diamond. Each facet reveals something important. Grinding them into one flat surface does not improve the diamond.

That is the danger in the phrase “single source of truth.” It suggests that one version must defeat the others. The result may be consistent data that no longer describes the company. Neatness is not the same as accuracy.

A comprehensive record should preserve these differences. It should capture the source, purpose, date, and authority behind each version. It should show how the versions relate. The goal is a complete view, not one flat answer.

Written policy works the same way. One office may allow visible tattoos while another does not. Both practices can exist under the same company policy. The inconsistency is part of the operating reality.

Many HR AI stories assume every question has one clean, documented answer. Most companies have not enjoyed that miracle. Aderit’s promise is not that AI can settle every disagreement. It is that the system can make disagreements visible and usable. AI can then select the right facet for the task. That is how an automated process becomes reliable.

That foundation is required for effective AI. An agent cannot make a sound pay decision from a recruiting description alone. It cannot answer a policy question without knowing the office and date. Context is not extra data.

Where the Story Gets Dangerous

A trusted record can support reporting, planning, and automated work. It can also tempt Aderit to promise everything at once. Customers may wonder what they are buying. That would be a mistake.

The message should stay simple. Aderit creates the comprehensive record that effective HR AI requires. It preserves each system’s view without pretending every difference must disappear. The larger possibilities can come later.

Governance Cannot Stay Fuzzy Forever

Aderit described guardrails for automated agents. The system can limit freedom and record actions. Governance requires more. It needs clear authority, ownership, and rules for choosing the right facet of the truth. Someone must decide which source applies to each decision. A human in the loop is useless if the human’s role is unclear.

If Aderit becomes HR infrastructure, this issue will grow. The system must preserve context while supporting decisions. That story is still taking shape.

Why the Opportunity Is Still Enormous

These concerns do not weaken Aderit’s opportunity. They make it clearer. The company is working on the foundation that effective AI requires. Features are easier to sell, but foundations carry the weight.

Data quality does not mean forcing every fact into one approved version. It means knowing why versions differ and when each matters. It means seeing the whole diamond. AI cannot do that from inside one silo.

Aderit’s strongest story is not about replacing every HR system. It is about connecting their views into a comprehensive record. That record can expose errors without erasing useful differences. It gives AI something solid enough to reason from.

Better HR outcomes require better HR data. Better AI requires the full context behind that data. Aderit is building a foundation for both. The penthouse can wait.

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At the heart of aderit.ai’s approach is what they call the ‘readiness stack’. It is an 18 point list of all of the criteria that have to be specified and monitored to enable the machine side of AI governance.

If you’re following the series, this is where things start to get dense. Integrating AI into an organization as a trusted component of policy execution, risk managment, compliance auditing, and decision making is a complex overly on existing practices.

Most enterprises arrive at agentic AI by one of two well-traveled routes.

In the first, a pilot works. In the second, an incumbent vendor bolts AI onto a platform the company already owns and describes the result as transformation. Both routes produce something real. Neither produces something you would particularly enjoy explaining to the board after it has behaved creatively.

The problem is straightforward: pilots and product demonstrations are optimized to prove capability. Governance is designed to survive consequence. These are not the same engineering problem, although PowerPoint has done admirable work concealing the distinction.

Closing that gap requires an Agentic Readiness Stack. It has two halves, and they behave quite differently.

The first eight layers are Foundational Infrastructure. Enterprise architects generally recognize them. Most organizations have some portion of them. There are familiar names, established practices, budget categories, and people who can explain the architecture using diagrams of reassuring complexity.

The remaining ten layers form the Agentic Control Plane.

Here, the market has not yet developed a shared vocabulary. Consequently, these layers rarely appear in RFPs, analyst scorecards, or vendor positioning. Markets are excellent at naming things they already know how to sell.

The missing layers become visible only after agents begin operating—and failing—in ways that are subtle, gradual, and expensive.

The distinction matters because much of what is currently sold as “enterprise AI governance” reaches identity and authorization, pauses for applause, and declares the work complete. Identity and authorization are necessary. They are also approximately the beginning.

The foundational layers determine whether an agent can operate. The control plane determines whether the enterprise can survive the agent operating badly.

Can the agent recognize when the work is actually finished? Does an irreversible action receive more scrutiny than a recoverable one? Can someone stop the agent while it is confidently heading in the wrong direction? Afterward, can the organization reconstruct what it knew, what it inferred, what it did, and why?

These questions rarely surface during a pilot. Pilots take place in controlled environments with attentive sponsors and unusually cooperative data. This is also why houseplants flourish in hotel lobbies.

Nor does a vendor’s AI overlay solve the problem. It generally inherits the governance of the underlying platform. That governance was designed to manage software used by people, not software authorized to interpret conditions, choose actions, and pursue outcomes.

The missing controls become obvious the first time an agent is confidently wrong in front of someone who matters.

Most organizations possess fragments of perhaps four layers. Almost none have all eighteen working together.

The components themselves are not especially novel. Much of the stack maps to infrastructure, controls, and operating disciplines enterprises already own. What is new is the urgency—and the realization that agentic readiness is not a product category.

It is a checklist of organizational capabilities that must be assembled, assigned, tested, and governed.

In other words, it is something the enterprise has to build, even if the procurement process would strongly prefer another answer.

The Agentic Readiness Stack — 18 Layers

Foundational Infrastructure (1–8)

1. Identity — Agent attribution that answers which agent, acting on whose behalf, using what credential.

2. Authorization & Policy Enforcement — Fine-grained access control governing what an authenticated agent is actually permitted to do.

3. API Management & Mediation — Gates between agents and systems: rate limiting, throttling, and contract enforcement at machine speed.

4. Observability & Audit Logging — Traceability of what an agent did, why, and what data it touched.

5. Data Quality & Canonical Models — The canonical substrate agents reason against, without which they produce confidently wrong outputs derived from real data.

6. Data Privacy & Compliance — Coverage from two directions: gateway-side enforcement and data lineage tracking across PII, jurisdictional, and consent boundaries.

7. Secrets Management — How agent credentials are scoped, rotated, and revoked, including at agent retirement or compromise.

8. Integration Architecture — The connective tissue across ERP, HCM, CRM and other systems that constitutes the agent’s actual reach.

The Agentic Control Plane (9–18)

9. Agent Memory Governance — Write-tier policies and immutable write audit for what agents infer, learn, and persist back into the organization’s record.

10. Success & Failure Pattern Learning — An explicit outcome loop so patterns that work are reinforced at retrieval and patterns that fail decay out.

11. Staged Autonomy — Tiered privilege (observer → recommender → gated executor → autonomous) with graduation on measured metrics rather than confidence.

12. Closure Rules — Per-task-type criteria distinguishing “the agent completed the task correctly” from “the agent stopped.”

13. Reversibility Taxonomy — Classification of actions as reversible, compensable, or irreversible, so approval rigor scales with blast radius.

14. Kill Switches — Tested, executable stop and rollback mechanisms scoped per agent, per tenant, and per capability.

15. Structural Coherence & Drift Detection — Topology-level monitoring that catches reasoning drift before outputs degrade, rather than after.

16. Evidence Provenance — A deterministic record of which memories, at what confidence, supported a given recommendation.

17. Idempotency — Write-path guarantees ensuring retries don’t become duplicate actions in systems of record.

18. Communication Scope Enforcement — Architectural boundaries on which stakeholders an agent may reach, on which topics, at what data sensitivity.

Photo by Benoît Deschasaux

Research Note: Aderit.ai

Poking holes isn’t a noble instinct. It’s just the one I was issued. I got to do it last week with Darin Ries and Bennie Reddin, the founders of Aderit (aderit.ai). I had more fun than you’d imagine given the topic.

Aderit sits underneath a company’s existing HR systems and pulls them into one shared record of the workforce. Everything reads from it. The founders call the data layer Genome and the agent layer Cortex.

I picked the job description as a test case because it’s simple and messy.

Look closer, and it gets worse

A fractal stays jagged no matter how close you get. The mess is the whole point.

A job description is a fractal. To a recruiter, it’s a sales flyer. To a comp analyst, it’s a category with a pay band. To a manager, it’s small tasks a person can be scored on. To L&D, it’s an inventory of skills. To the worker, it’s the thing they do all day, which looks like none of the above.

Stack them all together, and it still won’t describe the moment-to-moment work.

Ask HR, payroll, and finance what headcount is, and you will get six answers, no agreement, and no fabrication. For thirty years the reflex has been to grind all of it into one clean truth.

That is the mistake.

The good part is the pressure test

They came to get their ideas pressure tested. It’s the polite version of a bar fight.

Darin’s pitch was that AI governance is really a data lineage problem. I pushed back. Governance is thirty-odd control points and sign-offs. Lineage matters, but it isn’t the whole animal.

Bennett didn’t fold.

He tracks eighteen traits of what he calls agentic readiness: not just where data came from, but why, when, and how. I left less sure of my own point. Pressure testing cuts both ways.

Many truths, kept apart

You know the old story. One blind man holds the elephant’s trunk and calls it a snake. Another holds a leg and calls it a tree. Each is right, and neither has the elephant.

The job description is the elephant. Everyone who holds a piece is sure they have created the ultimate JD.

To a recruiter, the job description is a sales flyer. It’s bait, written to attract a certain kind of applicant. To a comp analyst, the same page is a category with a pay band, built to compare against a thousand other jobs. To a manager, it breaks into small tasks so a person can be scored. To L&D it’s a skills inventory. To the worker, it’s the actual job.

Aderit’s answer is interesting. They don’t force one truth. They keep them all. Comp, talent, L&D, management, and task descriptions live side by side. Each silo keeps its own view. Aderit holds the overlap.

Keep every facet, and something bigger takes shape. Lines run from each version to the job, the worker, the source system, and whoever last changed it. Do it across every record and the lines grow into a web. That web is a knowledge graph for HR. It holds not just the data but how each piece connects to every other piece.

Every field also gets a confidence grade: SOLID, SOFT, SHAKY, or UNKNOWN. That’s data quality that respects the source instead of bullying it into a template. Even an empty field says something, once you figure out why it’s empty.

That’s an answer that thrills a data architect sends an HR generalist running for the parking lot. Aderit’s fix is a filter: show me the job from my seat first, the other views only if I ask.

The job nobody wanted

Under all of this is the least glamorous work in software: integration. Someone once labeled a field address line three and filled it with t-shirt sizes, because it was there. Both founders come out of that world, where budgets die moving data instead of using it.

AI’s best trick is reopening problems people gave up on, and field mapping is the perfect one. It was so dull the industry gave up and let the support queue absorb it. Aderit’s 300-plus connectors and self-healing maps walk back into that abandoned room. Solve it, and a whole layer opens up that nobody could afford to reach before.

First in line are the auditors and whoever writes the next RFP, not the Mobley-versus-Workday crowd asleep at the tiller.

A big crowd will nod politely. A smaller crowd will do them the real favor and try to break the thing. Aderit (aderit.ai) wants the second kind. On one afternoon’s evidence, they can take it.

Correction
The confidence grades — SOLID, SOFT, SHAKY, UNKNOWN — are being built now, iteratively. Same with the full fractal data views such as the job description, comp and talent and L&D and management and task views living side by side. Today we capture the fine-grained detail and evidence, the dynamic contextual views are in flight rather than a maintained model. If we left you with the impression either one was done, that's on us. "Being built" is the accurate phrase for both.

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There is a lot of heat in the discussions of AI use in organizations. The word ‘transformation’ gets tossed around like a hand grenade. When you scratch the surface, much of the trouble stems from the fact that AI is both immature and implemented without an abiding regard for control and consequences.

Somehow, the fact that the board of directors is responsible for governance gets lost in the angst. It turns out that the effective use of AI requires a significant redesign of basic organizational controls. Board directors remain responsible for decisions even when AI participates in them.

You can’t really start to think about HR’s (or any other function’s) role until the basic governance issues are sorted. In the absence of a clear governance structure, the use of AI is not much more than a heap of unmanaged risk.

Over the next few posts, I am going to take a deep look at the components of organizational governance in an AI informed world. I was ‘prompted’ to do this by a (still ongoing) set of interactions with the team at aderit.ai. They have built a model of the elements of ongoing control needed to use and trust agents (or the orchestration of agents). Their work is defining the machine components of governance and control.

Governance can seem like an esoteric, dense, complicated topic with limited utility. Unlike AI with its blistering pace of exciting progress, governance is about steering the organization. The terrain is at least as dry as accounting and equally (maybe even more) important. Governance can seem overwhelming, detailed, complicated, and dense. Taken in its entirety, governance is an overwhelming set of requirements that can easily seem impossible to implement.

Accounting tells the board whether the enterprise’s financial story is trustworthy; AI governance should tell it whether delegated machine judgment is authorized, controlled, effective, and safe.

Throughout this discussion, I am going to use the metaphor of a diamond and its facets. Many organizational elements look different depending on the lens with which they are viewed. The diamond is a thing of beauty because of its facets, not despite them. The metaphor is a scalable view. It can be used to think about organization wide issues as well as the microcosms of data coherence and completeness.

How does an organization safely delegate consequential authority—to humans or machines—while retaining control and accountability for what happens?

For starters, governance is the system that addresses the following four questions:

Security, compliance, risk, accountability, data governance, organizational authority, and agent controls aren’t competing definitions of governance. They are different views of the same underlying problem (facets of the diamond).

For the board, there should be a standard evidence package that includes:

• AI inventory and material-use summary

• New and retired high-risk systems

• Business value and performance indicators

• Exceptions to policy

• Incidents, complaints, overrides, and near misses

• Testing and assurance findings

• Regulatory exposure

• Third-party concentration risk

• Remediation owners and deadlines

• Morale, productivity, and sentiment analysis

Key references:

• NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework. The most authoritative general-purpose operating framework. Its model—Govern, Map, Measure, Manage—covers organizational accountability, AI inventories, risk tolerance, lifecycle controls, monitoring, documentation, and executive responsibility.

• ISO/IEC 42001: https://www.nacdonline.org/all-governance/governance-resources/governance-research/director-faqs-and-essentials/implementing-ai-governance/. The most comprehensive auditable management-system standard. It tells an organization how to establish, operate, review, and continuously improve AI governance.

• NACD Director Essentials: Implementing AI Governance: https://www.nacdonline.org/all-governance/governance-resources/governance-research/director-faqs-and-essentials/implementing-ai-governance/ . Probably the clearest US voice on directors’ fiduciary and oversight responsibilities. Its current guidance covers the board’s agenda, expertise, committee structure, management reporting, and deployment decisions

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A background-check vendor drops a new survey every few months. HR trade press reprints the headline like it arrived on a stone tablet. Nobody does the arithmetic.

This week it’s “44% admit to lying.” Last month it was 70%. Gartner says a quarter of all candidates will be fake by 2028. That’s a guess in a suit and tie.

None of these numbers are completely made up. They just don’t measure what the headline claims.

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The baseline

There were about 5.2 million hires in May 2026 (BLS). Times twelve, that’s 62 million hires a year…hires, not applications. Applications run into billions.

The survey samples almost never look like the actual hired population. View every number below from that perspective.

Tier one: embellishment

A title is adjusted to be more understandable. Director of Happiness becomes CHRO. Someone “led” a project the way a rooster leads the sunrise.

A resume is a two-page summary of a career. Compression isn’t a crime. It’s the necessary work to make years of experience intelligible.

Volume: self-report numbers run 40% to 70% of job seekers. Out of 62 million hires, that’s 25 to 43 million people a year.

Severity: very low. Reference checks catch most of it. The fix is a corrected offer.

Tier two: fabrication

Inventing an employer that never existed. Claiming a nonexistent credential. That’s not compression. That’s pure fiction.

Volume: fabrication rates run lower, around 24% to 33%, somewhere between 15 to 20 million hires a year. This overlaps with tier one. A person inventing a job probably isn’t stopping there.

Severity: high, and higher in regulated professions, where a fake credential is a lawsuit with a return address.

Tier three: identity and technology fraud

Deepfake interviews are real. Proxy interviewers ace the technical screen. Then they hand the job to the actual hire. State-sponsored schemes use stolen identities to slot IT workers into remote jobs.

Volume: this is where the math falls apart, because there’s no base rate, just anecdotes with good PR. Federal indictments paint a picture in the hundreds of cases. Hundreds of cases against 62 million hires is a rounding error with a passport.

Severity: extreme. One case can mean sanctions violations or a breach, not an awkward Slack message.

Why the numbers don’t add up

Every survey melts three tiers into one “fraud rate” and reports it straight-faced. Tier one is tens of millions of people doing something mostly harmless. Tier three is a few hundred people doing something that could end up on the evening news. Averaging those isn’t analysis. It’s a sleight of hand.

This should embarrass the industry

Resume fraud statistics come from companies that sell a fix for resume fraud. Funny how that works. Resume sites want embellishment to sound normal, that sells services. Background-check companies want fraud to sound common and rising, because rising sells contracts.

Gartner doesn’t sell screening software, but it sells something better: what the industry panics about next year. “One in four candidates will be fake” books more speaking gigs than “the rate looks stable,” even though the second sentence is closer to the truth.

The actual research is thin and about as popular as flossing. Henle, Dineen, and Duffy built a validated measurement tool, published in the Journal of Business and Psychology, funded by SHRM. It separates fabrication, embellishment, and omission into three categories instead of one blob called “liar.” The paper sits behind a $39.95 paywall. A ‘free’ press release gets forwarded around HR Twitter within the hour.

What would make this credible

Separate the tiers, and put a headcount on each, the way this piece tried to. Disclose who funded the survey. Use a validated instrument instead of a yes-or-no question that lets respondents define lying however flatters them. Track one methodology over years, instead of a new survey house showing up annually with a number higher than last year’s.

Until that happens, read every “X% of resumes contain fraud” headline the way you’d read a mattress company’s sleep survey. Something real is probably in there, sized generously by whoever’s selling the fix.

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The conversation tested that claim, then followed several HR threads: bias audits, workforce transparency, and AI governance nobody has actually defined yet. It closes as a right-time idea. Compliance work is drowning in paperwork nobody wanted to do anyway. A tool that clears that work away is worth building.

A tool that claims to beat human auditors deserves a hard look before it deserves applause.

This is a recap of my conversation with Eric Sydell, an HRTech stalwart who headed innovation at ModernHire and its predecessor, Shaker International. As CEO and cofounder, he is fully embracing the transformative power of AI.

What Vero AI does

Eric built Vero to read the 80 percent of business information that isn’t numbers. That means documents, screenshots, sign-off chains. The system checks all of it against a any compliance framework and scores each control one at a time. The platform can also process numbers, but it uses a separate analytical path for deterministic calculations, since LLMs can’t do math.

It shows its work. It names the files it used for each score. If evidence is missing, it can draft the email asking for it. When new evidence shows up as images, one AI model checks it. A second model checks the first one’s judgment. They negotiate if they disagree.

Sydell says the system agrees with human auditors 96 to 97 percent of the time. He adds that two human auditors agree with each other only about half the time. That’s meant to sound reassuring.

It’s also a number worth testing, not just trusting.

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The back and forth

This wasn’t a pitch. It was two people looking at a real project together, with one of them trying to poke holes in it. That’s the better kind of conversation. A pitch wants a yes. A working session wants the truth, even the inconvenient parts. This was the second kind.

The fix proposed for the accuracy claim was simple. Strip the names off one client’s evidence. Run it through the system a thousand times. Measure how much the answers move around.

That’s an actual test. A percentage from a small sample of past audits is not.

The second hard question was about model dependency. Vero runs on top of Anthropic and Google models that change every few months. Each new model breaks old assumptions baked into the system around it.

A compliance buyer will eventually want proof the system is validated at the model level, not just validated once against last quarter’s version. Nobody has a full answer for that yet.

The HR and HR tech threads

Several parts of the conversation ran straight through HR and HR tech, even though Sydell said he’s drifted toward financial compliance lately. One thread was bias audits. Sydell mentioned a pharmaceutical company that got quotes of 50,000 to 500,000 dollars for a one-time bias audit of its hiring system. Vero could do that same analysis and then keep monitoring continuously, for far less money than a single audit.

That points at something HR vendors have avoided for years: transparency about how selection systems actually perform on bias. Sydell brought up Workday’s own bias litigation as an example of what happens when a vendor won’t show its numbers. His read was blunt.

Companies that won’t be transparent are hiding behind lawyers, not behind good products.

A second thread was AI governance itself. Sydell pointed to ISO 42001 and NIST’s risk framework as the usual reference points. But he also admitted governance is a “rat’s nest.” Companies have shadow AI running in places nobody’s tracking.

The counter-offer on the table was to build a simpler front door. Instead of asking a company to dive into full compliance work immediately, start by helping them build one AI governance system. That gets people used to the process before asking them to trust it with more.

A third thread was about who does this work and how it changes their job. Sydell, trained as a psychologist, was careful here. He said the goal isn’t to replace auditors, since most companies aren’t even close to fully compliant already. The goal is freeing auditors from reading documentation all day so they can actually consult and think.

He also flagged the harder problem underneath that. People who chose auditing because they like solitary, detail-heavy work may not want to become consultants. Tools don’t fix that by themselves. People need support to change how they work, and that support is often the slowest part to arrive.

Every organization today is buried in compliance paperwork nobody enjoys and nobody has time for. That burden is real, and it’s getting heavier as AI systems get embedded into hiring, evaluation, and everyday decisions without much oversight.

Vero AI is aimed at exactly that pile. It doesn’t just check a box once a year. It watches continuously, explains its reasoning, and produces a record any human auditor can review and challenge.

That’s the right shape for this moment. Point-in-time audits made sense when systems changed slowly. Systems don’t change slowly anymore.

The bigger opportunity Sydell hasn’t fully claimed yet is inside HR itself, especially around bias auditing and hiring system transparency. Companies have spent years avoiding that conversation.

The tools to finally have it, honestly and cheaply, are arriving right when the pressure to have it is rising too.

That’s good timing. Good timing doesn’t guarantee a company survives long enough to cash in on it, but it’s the right problem, aimed at the right moment, with a method that actually shows its work instead of hiding behind a black box.

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We’re entering a world with specific economic and demographic headwinds. As population plateaus and declines, the growth we’ve gotten used to changes intensity. Economic output has slowly disentangled from population, but GDP still leans on it.

We’re betting AI bails us out. The missing word is how. People everywhere are automating existing processes instead of redesigning work. That’s doing dumb things faster.

Yogi Berra said, “It’s tough to make predictions, especially about the future.” He also said, “You’ve got to be very careful if you don’t know where you are going, because you might not get there.” How we use the new technology is yet to be revealed.

Andrew Marritt, the seminal People Analytics practitioner who actually gets systems thinking, recommends two books. Charles Goodhart and Manoj Pradhan (London School of Economics) think hard about where economics meets demographics. The earlier one is The Great Demographic Reversal. The newer one is The Unanchored Central Banker. (You can tell from the titles they’re well-regarded professors.)

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The core idea: demography is starting to dominate fiscal policy, and fiscal policy will come to dominate monetary policy. We’re leaving a historically unique moment behind. Several forces drove the era of abundance:

• falling fertility

• higher female labor-force participation

• boomers entering peak working years

• China’s entry into the world economy

• Eastern Europe’s integration

The result was a generation of abundant workers, weak wage growth, cheap manufacturing, low inflation, low interest rates, high corporate profits, and few recessions.

Now the drivers reverse — fertility collapses, longevity rises, boomers retire, fewer young workers replace them, and scarcity replaces abundance. Two things push inflation up at once, and they work through different doors. On the wage side, scarce workers get higher pay and stronger bargaining power, and shortages persist. On the money side, aging populations spend more than they save, government outlays for pensions and health care climb, and deficits become the permanent condition rather than the emergency measure.

That’s the chain the two books trace. Demography drives the fiscal picture. Fiscal instability then drives the monetary one: when governments have to keep borrowing to cover the old, central banks lose the room to fight inflation the way they used to. Higher rates raise the cost of that debt, so the pressure runs toward tolerating inflation instead of crushing it. Structurally higher inflation isn’t just a wage story — it’s what happens when the fiscal side stops leaving the central bank a clean shot.

Most important, the burden of supporting seniors grows just as the workforce shrinks. (That’s the aging ratio from the Pyramids piece.) Health care becomes the dominant economic force. Tax revenue falls because the workforce is a shrinking slice of the population — which is exactly what feeds the deficits above.

Demographics are fixed. Today’s babies join the labor force in 15 or 20 years — we already know how many. Predicting the cohort is just math.

Even so, the problem is hard to see. Everything we know is the opposite of what we need to navigate the next chapter.

Expect management’s focus to change. Plan on organizational design and function to morph. Anticipate rethinking what we measure in the name of progress. Tomorrow’s workforce runs on people augmented by intelligent technology. The way it’s always been is not the way it’s going to be.

“The future is not what it used to be.” (Thanks, Yogi.)

These articles are a foundation for imagining next steps. Overwhelm with application volume gets replaced by the need for more. The tools built to treat today’s apparent problems are liable to become the shackles that block forward movement.

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You can’t forecast the impact of AI without understanding the world it hits. It does not hit the same everywhere.

I’m trying to sort out the relationship between population, economic performance, and AI over the next fifteen years. This analysis covers the US, its states, and its top 25 metro areas. The forces involved are not American peculiarities.

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Birth control, broad spectrum media, and the educational attainment of women push fertility rates down wherever they appear. The result is an aging workforce. Population growth slows, stops, then reverses.

The last piece, Peak Workforce, covered the shrinking labor force. The decline started in November 2025 and will run at least a generation. Declining immigration speeds it up.

For all of history, population growth drove economic growth. That’s over. We are entering a period where economic performance depends on productivity gains, not headcount.

Quality of life depends on how the transition goes.

AI’s impact will not be even. Some states and metros will do better than others. Understanding your local situation matters more than understanding the national one.

The two downloads at the end define the situation. One is an atlas of the states, DC, and Puerto Rico. The other covers the 25 largest metro areas.

This piece teaches you to read the charts.

The heart of both atlases is the population pyramid. A pyramid shows the distribution of each sex in the population, in five-year age bands. For most of history, young people vastly outnumbered old people everywhere, and the shape was an actual pyramid.

Here’s a pure one.

There are no pure population pyramids in the wild. The workforce in each is the group of people between 16 and 65. That’s the formal definition of the labor force. Here are the four types you are most likely to see.

Expansive
Example: Utah (aging index 0.44)
The base is wide. Every cohort of kids outnumbers the one above it. That’s fertility doing the work, year after year. It means the workforce of 2040 is already sitting in elementary school. It also means schools, pediatricians, and starter homes eat the budget now. Exactly one American jurisdiction still looks like this. It’s Utah. Nobody else is close.

Stationary
Example: Iowa (aging index 0.78)
A column, not a pyramid. Cohorts run the same size from kindergarten to middle age. Births and deaths cancel each other out. The workforce replaces itself one for one. There’s no surplus, no deficit. This is where most American states live right now. It reads as stable. The aging index says it’s in motion.

Constrictive
Examples: Maine (1.27), Puerto Rico (1.50)
The base has inverted. There are fewer children than retirees. Read that again. The replacement workforce is smaller than the one walking out the door. This is not a forecast. It has already happened in eleven states. Census counted them. Every succession plan in these places is competing for the same shrinking pool of thirty-year-olds.

Working-age bulge
Example: Washington, D.C. (26–34 = 19.9% of population)
This is not a pyramid. It’s a bulge of twenty- and thirty-somethings stacked in the middle. Migration built it, not maternity wards. Magnet cities have this shape. Participation looks terrific. Don’t be fooled. The shape holds only as long as the next van arrives. This workforce is rented, not owned. Landlords should know the difference.

For each state and metro area, there are a bunch of bits of numeric data besides the population pyramid.

Aging index · forecast: The share of people 65 and over divided by the share 18 and under. Cross 1.0 and retirees outnumber children. This one number drives every classification in the atlas. The projections next to it run the index’s own 2022–25 drift straight out to 2030, 2035, and 2040. That’s a trend extrapolation, not a demographic model. Most of 2040’s children haven’t been born, and we decline to invent them.

Total population: Resident population, July 1, 2025. Census Bureau Vintage 2025. The one number in this atlas nobody argues with.
[Metro edition: built up from each CSA’s component metro and micro areas. The parts sum to the whole.]

Labor force: Everyone working or actively looking. States, D.C., and Puerto Rico come from BLS/LAUS, seasonally adjusted, May 2026, preliminary. The national figure comes from a different survey: the CPS household survey, same month. Two agencies’ methods, two answers. The state numbers will not add up to the national number. They aren’t supposed to. Anyone who tells you otherwise hasn’t read the footnotes.
[Metro edition: BLS metro data, May 2026, not seasonally adjusted, metro components only. The micropolitan areas are missing about 3% on average, closer to 10% around Dallas–Fort Worth. We say so because you’d find out anyway.]

Avg annual growth: Compound annual growth from the April 2020 census to July 2025. We computed it; the two endpoints are official. If you want to check the math, the math is checkable. That’s the point.

Labor force participation: The share of civilian noninstitutional adults 16 and over who are working or looking. BLS/LAUS, seasonally adjusted, May 2026. Puerto Rico runs its own household survey through its own Department of Labor; that figure is from January 2026. Different island, different survey, same definition.
[Metro edition: there is no official metro participation rate. Nobody publishes one. So this is labor force divided by resident population 16+, same geography top and bottom. It’s an approximation and it’s labeled like one.]

Average (mean) age — No agency in America publishes an average age. We built one from the 2024 cohort shares using bucket midpoints. It’s good to about a year either way, and it wears the label “(est.)” everywhere it appears. When a number is homemade, it should say homemade.

Median age — The age that splits the population in half. Census Bureau, 2024. Published, official, citable. When the argument gets heated, this is the number that survives.

National rank · U.S. share — Rank among the 52 geographies in this atlas (50 states, D.C., Puerto Rico) by July 2025 population, plus each one’s share of the 341.8 million national total. Census publishes the populations. Somebody had to do the ranking. That was us.
[Metro edition: ranked among all 181 stateside Combined Statistical Areas. Being top-25 is the price of admission.]

Top 5 Industries: For each state or metro, the Top 5 industries, their workforce size, and the percent of the labor force are presented. This is the ground where the impact of AI is most likely to be felt.

Components of Change: Population growth comes from one of three sources: Internal Growth (births minus deaths before migration and immigration
Migration (movement in or our of state)
Immigration (net growth from international sources)

Population and Labor Force Forecasts: Growth in five year increments starting now and ending in 2041.

The fine print, up front — The vintages differ on purpose. Each number is the newest official figure its series offers, and pretending they all share a date would be tidier and wrong. Below the grid on every page: the official Census components of change, April 2020 through July 2025, and the migration trend. The industry figures come from the 2024 ACS and count employed civilians. That’s a different universe than the labor force number two inches away. They won’t reconcile. Universes don’t.

The atlases each contain detailed descriptions of methodology and references used. Going forward, the discussion of AI’s likely impacts will rest on the data in these two atlases. Let me know if you’d like the powerpoint versions.

Here’s a sample:

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Photo by Osama Elsayed on Unsplash

The American workforce peaked in November 2025. It hit 171.5 million people and started shrinking. That has never happened outside a recession in the modern record.

The workforce is getting smaller, and almost nobody is talking about it.

A few questions organize what follows. How is workforce participation actually calculated? Who is in the count, and who got left out? How do population and GDP connect, and what happens to the economy when the population stops growing? What does any of this mean for people who plan workforces for a living? And is the next decade a war for talent, or something stranger?

The short version goes like this. The participation rate is a fraction, and most people get the bottom half wrong. The count includes every worker in the country, papers or no papers, and excludes a third of America. GDP is workers times output per worker, full stop. A flat population means productivity has to carry the whole load. And the planning implications are bigger than the headlines suggest.

The data comes from the Bureau of Labor Statistics, the Bureau of Economic Analysis, and the Congressional Budget Office. The forecasts are mine, built on their assumptions, which are printed on every chart. When a number is an estimate instead of a measurement, I say so.

1. How Workforce Participation Is Calculated

The participation rate is the most quoted number in labor economics. It is also the least understood. The math is one fraction.

The top of the fraction is the civilian labor force. That means everyone sixteen or older who is working or actively looking for work. One hour of paid work in the survey week counts as working, which tells you something about how generous the definition is.

The bottom of the fraction is the civilian noninstitutional population, age sixteen and up. It is not the total population. Getting this wrong is the most common error in workforce commentary, and it is not a small one.

In May 2026 the math ran like this. The labor force was 170.1 million. The eligible adult population was 275.1 million. Divide, and you get 61.8 percent.

Nobody counted anybody. Both halves of the fraction come from a survey of 60,000 households.

The survey is the Current Population Survey, run monthly by the Census Bureau. Every number here is an estimate with a margin of error. The definitions have held steady since 1994, which is the only reason fifty-year comparisons are honest.

Figure 1. The numerator: the civilian labor force, monthly, 1975–2026. The count peaked in November 2025.

The history reads differently once you know the math. Participation rose from 61 percent in 1975 to 67.3 percent in early 2000. Women entered the workforce and the boomers hit their prime years at the same time.

Then it fell, and the falling was mostly demographics. A retiring boomer stays in the bottom of the fraction and leaves the top. COVID knocked the rate to 60.1 percent in April 2020, and the recovery stalled at 62.5.

The past year shows a slide to 61.5 percent. That is the lowest sustained reading since the late 1970s. It is worth your attention.

Figure 2. The participation rate, monthly line over annual-average bars, 1975–2026.

One measurement problem matters right now. The population estimate on the bottom of the fraction runs on autopilot between annual Census corrections. When deportations and a closed border remove people, the denominator does not notice until January.

People leave the country. The population estimate keeps growing anyway. So the measured rate falls further than actual behavior explains.

Expect a downward correction in the January 2027 numbers, and do not let anyone sell it to you as news about worker motivation.

2. Who Is — and Isn’t — in the Count

America holds about 342 million people. The participation math only sees 275 million of them. The other 67 million are invisible to the statistic.

Children under sixteen sit outside the count entirely. So do about 3 million people in prisons, jails, and nursing homes. So do 1.3 million active-duty troops, which surprises people every time.

Measure against the whole country instead of the eligible adults and the picture changes. Less than half of all Americans are in the labor force. About 47.6 percent actually hold a job.

Figure 3. Where all 342 million Americans sit, mid-2026. The participation rate divides the blue slice by everything except children, troops, and the institutionalized.

Now look at the 105 million adults who are eligible but not participating. The commentary treats them like a mystery or a moral failure. They are neither.

About 54 million are retired. About 17 million are in school. About 16 million are too sick or disabled to work, 14 million are caring for family, and 3 million are everything else, including the few hundred thousand who wanted a job and gave up looking.

The people outside the labor force are not slackers. They are your parents, your kids, and your neighbor with cancer.

On immigration, the count is broader than most readers think. The survey never asks about legal status. Citizens, green-card holders, visa workers, and undocumented workers all get counted the same way.

Foreign-born workers were 19.2 percent of the labor force in 2024. What the survey cannot tell you is whether a missing person left the country or just stopped answering the door. Right now, some of each is happening, and nobody can split the difference precisely.

One more distinction completes the picture. Of the 170.1 million in the labor force, 162.8 million were working in May 2026 and 7.3 million were looking. Employment peaked in December 2025 and has given back 1.7 million jobs since.

3. The Churn Beneath the Surface

Here is the habit that ruins most workforce reporting. Everyone stares at the monthly net change. Almost nobody looks at the gross flows underneath it.

About six million people leave the labor force every month. Roughly as many come in. The famous net number is the tiny difference between two huge flows.

The workforce is not a reservoir. It is a river.

Over a year, the exits add up to about 73 million person-transitions. The net change in a good year is one to three million. That ratio should change how you read every jobs headline you see.

Two cautions about the exit numbers. They count transitions, not people, so a person who leaves twice counts twice. And the data simply does not exist before 1990, because the government did not collect it.

Figure 4. Annual gross exits (top) versus annual net change (bottom), 1975–2025. Check the scales: tens of millions versus single-digit millions.

Figure 5. The same story, monthly. April 2020 produced twelve million exits in one month.

Three companion charts finish the picture. The labor force nearly doubled from 93 million in 1975 to just over 171 million at the peak. Exits have been running at record levels through 2025 and 2026.

And the net-change chart shows 2026 on track to be the worst non-recession year in the whole fifty-one-year record. Sit with that for a minute.

Figure 6. Total labor force size, annual bars with monthly line, 1975–2026.

Figure 7. Gross exits, 1990–2026. The right axis is exactly twelve times the left, so the bars and the line compare directly.

Figure 8. Net labor force change, 1975–2026. The January spikes are Census paperwork, not hiring.

4. Who Is in the Workforce: Gender and Age

The gender story is a revolution followed by a stall. Women were 40 percent of a 94-million-person workforce in 1975. They are 47.7 percent of 170 million today.

Look closer and the revolution ended around 2000. The line has been flat for twenty-five years, five or six points short of parity. Any honest forecast holds the mix steady, because that is what the data says.

There is one wrinkle worth watching. Women’s share ticked up in early 2026. The immigration-driven decline is hitting men harder, because immigrant workers skew male.

Figure 9. The labor force by gender, 1975–2041. Hatched bars are the forecast.

Figure 10. The same shares as lines, zoomed to where the movement actually is.

The age story is a boomerang. The median worker was 34.6 years old in 1980, the youngest modern workforce America ever had. Then the same generation that made it young spent thirty years making it old.

The median hit 42.0 by 2014 and then, surprisingly, went flat. Boomer retirements started pulling old workers out as fast as everyone else aged. BLS says the climb resumes, reaching 42.4 by 2034 and roughly 43 by 2041.

One data caution here. BLS publishes the median age about once a decade, not annually, and it publishes the median rather than the average. The line between the dots is my interpolation, clearly marked.

Figure 11. Median age of the labor force, published BLS values with interpolation, 1975–2041.

5. The Outlook to 2041: Three Scenarios, One Lever

Workforce forecasting is arithmetic wearing a crystal ball costume. Everyone who will be forty in 2041 is twenty-five right now. The native-born workforce of 2041 is already born and already counted.

Only one input is genuinely adjustable, and it is immigration.

The official assumptions just moved violently. CBO says net immigration collapsed to 410,000 in 2025, down from over three million at the peak two years earlier. Meanwhile deaths start exceeding births around 2030.

After that, every net addition to the American population is an immigrant or an immigrant’s child. That is not an opinion. That is what the birth and death tables say.

The scenarios in Figure 12 spell it out. The baseline follows the government assumptions and reaches about 176 million workers in 2041. High immigration gets you 183 million, and near-zero immigration shrinks the workforce to 166 million.

Watch the heavy line, the growth rate. It ran 3.3 percent a year in 1978, about 1 percent in the 1990s, under half a percent since 2008, and negative in early 2026. The whole game for the next fifteen years is whether that line lives above or below zero.

Figure 12. Growth rate (heavy line, left) and size (right), with three scenarios to 2041.

What would prove me wrong? A surge of older workers staying on the job, a fertility recovery no rich country has managed, or a change in how the counting works. Absent those, the fan holds.

The shakiest assumption is political, not demographic. The baseline bets that labor scarcity eventually forces the immigration tap back open. That is a judgment about Congress, and you know what that is worth.

6. How GDP and Population Are Related

The chain from population to GDP has three links. Babies become workers sixteen years after they are born. Immigrants become workers the day they arrive.

Working-age people become the labor force through the participation rate. The labor force becomes GDP through productivity. Total output is workers times output per worker, and there is no third ingredient.

GDP growth equals workforce growth plus productivity growth. Write it on the whiteboard.

The last fifty years show the formula at work. The economy is 3.9 times its 1975 size on a workforce only 1.8 times as large. The gap between those numbers is a half century of machines, software, and better organization.

Figure 13. GDP and the labor force, both indexed to 1975 = 100. The wedge between the lines is output per worker.

The forecasters all live inside this formula, which is why they agree. CBO says 1.8 percent growth through 2036, easing to 1.6. The Fed says 2.0 for the long run, the IMF says 1.8, and private consensus sits in the same range.

Then there is the White House at 3 percent. That number requires either workers that current immigration policy forecloses or productivity America has not sustained since the dot-com years. Every administration does this, and it is a budget device, not a forecast.

Compound the difference to 2041 and the gap is an economy about 20 percent larger. Several trillion dollars are riding on which assumption you buy.

Figure 14. Competing GDP forecasts, 2026–2041. One of these lines is not like the others.

7. What a Zero-Growth Population Implies for GDP

Set population growth to zero and run the formula. Three things fall out, and none of them are optional.

First, the arithmetic. If the workforce stops growing, every point of GDP growth must come from productivity. A 1.8 percent economy needs 1.8 percent productivity growth, every single year, forever.

American productivity has averaged about 1.5 percent since 2005. So the standard forecast is possible, but it leaves zero slack. Miss on productivity, or get the shrinking-workforce scenario, and the projected economy simply does not show up.

This is not a thought experiment. Deaths pass births around 2030, and the workforce may have peaked already.

Second, the difference between total and per-person prosperity. Zero population growth does not mean your standard of living stalls. If productivity grows, income per person grows even while total GDP flattens, and Japan has been demonstrating this for thirty years.

What flattens is everything scaled to total GDP. Debt ratios get worse without any new borrowing. Social Security and Medicare run on the ratio of workers to retirees, and that ratio rots fastest exactly when population growth stops.

Look back at the pie chart in Figure 3. About 170 million people in the labor force are carrying about 172 million who are not. Every forecast in this paper says the carried half grows against the carrying half.

Third, what becomes valuable. In a labor-scarce economy, the premium moves to anything that raises output per worker. That means automation, better matching of people to jobs, and longer healthy working lives.

CBO put a machine-productivity dividend into the national forecast for the first time, worth about a tenth of a point a year. Whether that is timid or wildly generous is the biggest open question in American economics. The vendors will tell you it is timid, and the vendors are selling something.

8. What This Means for Workforce Planning

Workforce planning grew up in a world where labor supply was somebody else’s problem. You forecast demand, you posted requisitions, and the market delivered. That world ended in November 2025.

Supply is now the constraint, and the plan that ignores it is a wish.

Start with the arithmetic your own building. If the national workforce is flat, your net new hires come from someone else’s payroll. Every growth plan in your industry is a raid on a fixed pool, and the pool knows it.

That changes the economics of retention. When replacement is easy, turnover is a nuisance and retention programs are soft. When replacement means outbidding a competitor for scarce people, every point of retained turnover is headcount you did not have to buy at market price.

It changes the value of the workers you already have. The median worker is 42 and heading older. Health, ergonomics, flexible schedules, and phased retirement stop being benefits-brochure padding and become supply-chain management for labor.

It changes where skills come from. If you cannot hire the skill, you build it, and internal mobility becomes the cheapest labor market you have access to. The companies that treat their own people as the primary talent pool will out-staff the ones still waiting for applicants.

It changes the automation conversation. Automation used to be a cost play with a layoff attached. In a shrinking labor market it is a coverage play, and the honest question is which work should stop requiring a person at all.

And it changes planning horizons. Immigration policy can swing your local labor supply faster than any birth rate ever will, in either direction, on an election cycle. A workforce plan without policy scenarios is a plan for exactly one version of the future.

Plan the supply side. Demand was never the hard part.

9. A Meaner War for Talent — or Something Stranger?

Every shortage produces the same headline. The war for talent is back, and this time it is forever. Before you buy it, remember where the phrase came from.

McKinsey coined it in 1997 to sell consulting. It has been declared permanent in every tight market since, and repealed in every recession. Skepticism is the correct opening position.

The case for a hotter war is real, though. Supply is flat or falling for the first time in the modern record. The demographic engines are spent, the immigration tap is closed, and six million exits a month means your best people always have somewhere to go.

In that world, scarcity is structural, not cyclical. Wages ratchet. Poaching gets industrial. The talent war stops being a metaphor and starts being a line item.

Now the case against. A war for talent assumes demand for labor holds steady while supply shrinks. That assumption is doing a lot of unexamined work.

The same machine-productivity bet that props up the GDP forecast is a bet against labor demand. If the software does what its sellers claim, demand for whole categories of work falls faster than the workforce shrinks. You do not fight a war for people nobody is hiring.

Here is the uncomfortable middle: both cases can be true at once, just not for the same jobs.

The likelier future is not one war but violent weather. Scarcity in care work, skilled trades, and anything that requires hands and presence. Surplus in routine cognitive work that software eats. And the boundary between the two moving every year.

Volatility, not shortage, is the real forecast. Two unstable inputs drive the whole system, and both are set by people who do not answer to your workforce plan. Immigration policy swings on elections, and machine adoption swings on vendor promises meeting reality.

The old war for talent had one front and one weapon, which was money. The next one has a dozen fronts that open and close without notice. The winners will be the organizations that can redeploy, retrain, and re-plan faster than the weather changes.

Do not build a war machine. Build a fast one.

10. How to Stay on Top of These Trends

Everything in this paper came from free public sources. You can run this watch yourself with modest effort. Here is the routine.

The monthly habit is the BLS Employment Situation release. It lands at 8:30 a.m. Eastern, usually the first Friday of the month. One document carries the labor force level, the participation rate, employment, and unemployment.

The single best tool is FRED, the St. Louis Fed’s free data service. A handful of series codes are worth memorizing. CLF16OV is the labor force, CIVPART is the participation rate, CE16OV is employment, CNP16OV is the adult population, and LNS17800000 plus LNS17900000 are the gross exits.

FRED will email you when a series updates. The watch runs itself, and it costs nothing.

The annual calendar matters as much as the monthly one. Every January the survey absorbs new Census population controls, which is what caused the jumps in 2000 and 2025 and will likely cause a drop in 2027. Read January data knowing the break is coming.

Each winter, CBO publishes its Budget and Economic Outlook and its Demographic Outlook, which hold the immigration assumptions under every long-run number here. Each late summer, BLS updates the ten-year labor force projections. Each May, the foreign-born workers report updates the immigration picture, and the Fed publishes its growth outlook quarterly.

Three reading habits separate a serious watcher from a headline consumer. Watch levels and gross flows, not just rates, because a rate can fall for reasons that have nothing to do with behavior. Ask what happened to the denominator before you believe any ratio.

And learn to tell a measurement event from an economic event. A January revision, a survey change, or a shutdown data gap will masquerade as news to anyone who does not know the calendar. The October and November 2025 holes in these charts are exactly that kind of thing.

The numbers are good. They reward people who know how they are made.

Appendix A: Methods and Sources

Historical series are Bureau of Labor Statistics and Bureau of Economic Analysis data, pulled through FRED, the St. Louis Fed’s data service. All BLS series are seasonally adjusted. October and November 2025 are missing at the source for several series, and January values include annual Census population-control revisions, flagged on the affected charts.

· Civilian Labor Force Level — CLF16OV (BLS via FRED)

· Employment Level — CE16OV (BLS via FRED)

· Civilian Noninstitutional Population — CNP16OV (BLS via FRED)

· Labor Force Participation Rate — CIVPART (BLS via FRED)

· Civilian Labor Force, Women — LNS11000002 (BLS via FRED)

· Labor Force Flows, Employed to Not in Labor Force — LNS17800000 (BLS via FRED, from Feb 1990)

· Labor Force Flows, Unemployed to Not in Labor Force — LNS17900000 (BLS via FRED, from Feb 1990)

· Real Gross Domestic Product — GDPC1 (BEA via FRED, chained 2017 dollars)

Projections, official outlooks, and reference publications:

· BLS Employment Projections, median age of the labor force (2004–2034): bls.gov/emp/tables/median-age-labor-force.htm

· BLS Employment Projections program (labor force growth averaging 0.3 percent per year, 2024–34): bls.gov/emp

· BLS Monthly Labor Review, “Labor force change, 1950–2050” (historical median age): bls.gov/opub/mlr/2002/05/art2full.pdf

· BLS, Labor Force Characteristics of Foreign-Born Workers: bls.gov/news.release/forbrn.nr0.htm

· BLS, The Employment Situation (monthly release): bls.gov/news.release/empsit.nr0.htm

· CBO, The Demographic Outlook: 2026 to 2056 (immigration and population assumptions): cbo.gov/publication/61994

· CBO, The Budget and Economic Outlook: 2026 to 2036 (GDP projections): cbo.gov/publication/61882

· Federal Reserve, FOMC meeting calendar and Summary of Economic Projections: federalreserve.gov/monetarypolicy/fomccalendars.htm

· IMF, World Economic Outlook, April 2026: imf.org/en/publications/weo (April 2026)

· OMB, the President’s FY2027 budget (growth assumptions): whitehouse.gov/omb/budget

The scenario paths, the median-age extension past 2034, and the not-in-labor-force subgroup estimates are my calculations anchored to those sources, with methods stated in the figure notes. Gross flows count transitions, not people. The subgroup figures in Section 2 are good to plus-or-minus one to two million.

A closing note on hygiene. Everything here rests on a survey of sixty thousand households, under definitions last revised in 1994. The numbers are the best available, and they are good. They are not the economy itself. That distinction always matters. It matters more than usual now, when the gap between who is in the country and who is in the population estimate is the widest it has been in a generation

Everybody starts the story of HR technology in 1951. That was the year an electronic computer first ran a routine business job. It is a fine place to mark the electronic inflection.

It is the wrong place to begin.

Punched-card machines were running payroll, accounting, and personnel records for half a century before anyone switched on a stored-program computer. The longer lineage is worth understanding. It shows HR being shaped by data-processing capability before the computer existed.

Technology drives the evolution of HR. It was already doing the driving when the machines were still mechanical.

The punched-card era (1890s to 1940s)

The first large-scale human inventory in this country was the census. Herman Hollerith built punched-card tabulating machines to process the 1890 count. The machines replaced manual review with mechanized counting and sorting.

Hollerith founded the Tabulating Machine Company in 1896. It merged into the Computing-Tabulating-Recording Company in 1911. CTR was renamed International Business Machines in 1924.

IBM went on to define corporate computing. The work to count people in the census made HR’s adoption possible.

For the first half of the twentieth century, large employers ran payroll, accounting, and personnel accounting on punched-card equipment. Railroads, insurers, and government agencies did the same. Keypunches, sorters, tabulators.

None of it was a computer in the modern sense. All of it was machine-readable employee data at scale.

That is the real ancestor of the HR database.

When electronic computers finally arrived, their first job was taking over the work the punched-card systems were already doing.

The electronic inflection (1951)

The first business application on an electronic computer was not payroll. It was not American.

In November 1951, J. Lyons and Company ran a job called Bakery Valuations. Lyons was a British tea-shop and catering business.

The machine was the Lyons Electronic Office. Lyons built it themselves.

People generally regard this as the world’s first routine business computer application. Payroll for Lyons’ own staff followed around 1953 or 1954. By 1956, LEO was running payroll as a bureau service for Ford UK and others.

A tea company built the first business computer.

The conventional history loses that detail. It is worth keeping, because it tells you something true about how this industry actually moves. The pressure to pay people and value inventory was concrete and recurring.

HR technology is almost always a use case for technology invented for other purposes.

American payroll went electronic in the mid-1950s. The UNIVAC I and the IBM 650 were reaching large employers.

General Electric ran payroll on a UNIVAC I at its Appliance Park facility in 1954. That run is usually cited as the first American business-payroll application.

There are two different firsts here. Most histories blur them.

Lyons was the first company to run its own payroll on a computer. Automatic Data Processing was the first payroll services company.

ADP was founded in 1949 as Automatic Payrolls, Inc. It processed payroll on behalf of clients. It eventually dominated the outsourced business.

From master files to the system of record (1958 to 1990)

Once payroll data existed in machine-readable form, personnel data followed. By virtue of being first, Payroll became the organizing principle for all HRTech. You can think of most HRTech/WorkTech as an extension of the payroll record.

Computerized employee master files showed up in the late 1950s and early 1960s. Integrated payroll-and-personnel systems came together through the late 1960s and early 1970s. Databases, disk storage, and management information systems made that possible.

Again, HR’s evolution follows the emergence of technology.

The first commercial HR software vendors arrived in the early 1970s. The term HRIS was in common industry usage by decade’s end.

The single system of record for the whole employee population is largely a product of the 1980s. That is what most people picture when they hear HR technology. PeopleSoft, founded in 1987, together with Lawson and SAP, brought client-server architecture and relational databases to the function.

For roughly its first four decades, HR technology answered three questions and not many more.

Who works here. What do we pay them. What benefits do they get.

It was record-keeping and transaction processing. It was good at it.

The internet, the cloud, and the shift to recruiting (1990s to 2010s)

From the 1990s the center of gravity moved from records to workflow and recruiting. Enterprise HCM suites, networked PCs, and graphical interfaces standardized HR across the organization.

Then the internet moved recruiting online through job boards and applicant tracking systems. The web let employees and managers serve themselves.

Cloud and SaaS architecture turned HR software into a subscription. Workday marked that shift in 2005. Cheap storage and machine learning made people analytics a discipline of its own.

The job changed from recording what happened to predicting what would happen next.

From records to prediction to judgment support (2022 to present)

The current inflection is generative AI. The cleanest way to frame it is as the fourth move in a long progression.

HR technology started as a record-keeper that told you what happened. It became a reporter that told you what was happening. It became an analyst that told you why.

Then it became a forecaster that told you what would happen next.

Now it is trying to become a participant in the decision itself. It wants to tell you what to do.

The difference between prediction and judgment is the whole game.

A prediction states a probability. This employee has a meaningful chance of leaving in the next six months. That is where it stops.

A judgment-support system goes further. It frames the decision. Here are the interventions worth considering, with the evidence behind each one, the cost, the risk, and the likely result.

A human still owns the call at the end.

Large language models are what make that possible. They can reason across different kinds of evidence. They can say their reasoning out loud in plain language.

The machine-learning models that came before could not do that.

The emerging applications run across recruiting, internal mobility, workforce planning, compensation, and employee relations.

A word of caution about the vendor names attached to this category. Treat them as directional rather than settled.

The space is moving fast. The line between a shipping product and a roadmap slide is still blurry.

Some of what’s getting sold is a prediction wearing a better interface. Some of what’s sold as an AI agent is employee self-service with a fresh coat of paint.

The buyer’s job is to tell the difference.

Here’s another way of thinking about it.

Read the whole history this way and the pattern is hard to miss. Each new capability arrived and opened a bottleneck or an opportunity. HR reorganized itself to handle what the machine made possible.

That engine has not stopped.

The interesting question now is not whether the software gets smarter. It will. The question is what stays human once the machine can assemble the evidence, weigh the alternatives, and draft the recommendation.

Accountability does not automate.

Somebody still has to own the decision. Somebody has to own being wrong about it. The history suggests that is the part of human resources that goes last, if it goes at all.

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My fascination with electronics started at about 7. That Christmas I got a crystal radio kit. If you’ve never seen one, it runs on nothing but the electricity in the radio waves themselves. No batteries, no plug. Six parts, headphone included.

It was like magic. You held the headphone to your ear and the sound arrived out of nowhere. Night was the best time. Radio waves move differently after dark.

The reason is the ionosphere. It reflects certain frequencies, and the waves bounce between the ground and that layer on their way around the planet. At night, with the sun gone, the layer behaves differently, and some distant stations come in that you’d never catch at noon.

Ten or so stations were licensed at 50,000 watts. From my bed in Virginia I could pull in the Grand Ole Opry out of Nashville and Cousin Brucie on WABC in New York. I stayed up listening.

Tuning was fickle. Weather changed everything. Some nights I got Los Angeles or San Francisco. Some nights I got nothing.

It was like having wings. When the house went quiet I could leave it, reliably, and go anywhere. That’s where my scattered taste in music came from. I knew there was a bigger world, and I knew I’d get to it.

The history runs the same way. Vacuum tubes were expensive, so cheap crystal sets are what actually put radio in front of the public in the early 1920s. People built them at home or bought them. Quaker Oats sold a kit for a dollar and two box tops.

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When tube prices fell, louder amplified sets pushed the crystal radio aside. Then the Depression brought it back. Broke people buy what’s simple and cheap, and quality loses to affordability every time the money’s gone.

So the crystal set was the gateway. Simple, cheap, no power required. It put receivers in millions of hands, and those hands became the audience that made commercial broadcasting a business in the first place.

New communication technologies always draw fire, and radio drew plenty. Two of the worries rhyme with what you hear about AI now, though I’d watch the rhyme more than I’d trust it.

The first was that reality would come apart. In the 1930s, critics treated radio as a machine for mass manipulation. The panic story around Orson Welles’ 1938 War of the Worlds broadcast got repackaged as proof that listeners could no longer tell broadcast fiction from fact. The current version says generative tools, deepfakes, and automated disinformation will wear away any shared sense of what’s true, until you can’t trust audio, video, or text.

The second was that somebody’s livelihood was finished. In the early 1930s the newspapers fought radio in the Press-Radio War, trying to choke off its access to the wire services because a fast, free medium looked like the end of print. The current version is the wave of lawsuits from writers, artists, and journalists who expect their work to be ingested, copied, and undercut.

The radio panic didn’t get answered. Television came along and the worry moved over to it by the mid-to-late 1950s. That is the pattern worth noticing. The dread attaches to whatever showed up last, which should tell you something about where it actually comes from.

All new technologies create negative blowback. While it’s not all misplaced, the shakeup of the status quo generates waves of fear as a part of the adoption process. In future articles, I’ll be digging into the evolution of technology and the associated fears.

Fear and panic are natural responses to new technologies. The certainty that relationships will be damaged, industries and jobs destroyed, and young people will be hurt echoes across hundreds of years of tech transitions.

In HR, HRTech, and WorkTech, the debates about the horrors of Ai will probably grow in intensity for a few more years. Then, the next thing will start taking the heat.

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Crystal Radio Parts List

1. Diode (Detector): Traditionally a galena or germanium crystal; it rectifies the alternating current radio signal into direct current audio pulses.

2. Ground Connection: Hooks to a cold-water pipe or metal rod to complete the antenna circuit safely.

3. High-Impedance Earphone: Piezoelectric or ceramic earbud which ran on the micro-amounts of electricity generated by the radio waves.

4. Antenna Wire: 50–100 feet to trap enough physical radio wave energy to power the system.

5. Tuning Coil (Inductor): Copper magnet wire wound tightly around a form to filter out overlapping frequencies.

6. Earphone: A single bakelite can holding a magnet, a coil, and a thin steel diaphragm.

I spend my days analyzing the intersection of HR tech, the job market, organizational culture and the sheer hysteria surrounding Artificial Intelligence. Vendors are running around with their hair on fire, pitching Large Language Models (LLMs) as the ultimate cure for workplace friction.

Using these tools feels like strapping on a mental exoskeleton. But the hard part about technological disruption is looking past the immediate magic to see the hidden tax. We are stumbling into a psychological trap.

It’s the hallucination of competence.

We worry constantly about AI hallucinating facts, but we miss the bigger problem: AI makes us hallucinate our own mastery. We are decoupling the painful, messy work of thinking from the final product. Here is how this illusion works.

Seduced by the Surface

Generative AI produces impeccable, authoritative prose. Historically, flawless text meant the author had put in the hard cognitive work to deeply understand the material. Because of this, our brains use a biological shortcut called the “fluency heuristic.” If it reads beautifully, we instinctively assume it’s true.

AI hacks this shortcut.

It hands a worker a perfectly formatted document. Because it looks so polished, the worker’s brain never engages the analytical gears required to check for underlying logic gaps. We mistake aesthetic perfection for conceptual reality.

The Ego and the Prompt

Then there is the blurred line of agency. A manager types a sloppy, ten-word prompt. Three seconds later, the AI spits out a comprehensive strategic analysis. Instead of realizing the machine’s statistical weights did 99% of the heavy lifting, the user unconsciously takes the credit. Over time, workers view the AI’s vast capabilities as a reflection of their own intellect. It’s a massive inflation of self-assessed competence.

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Confusing the Map with the Territory

Generative AI doesn’t just fetch links like a search engine; it instantly synthesizes them into a polished conclusion. Workers receive this highly competent document and commit a fundamental category error: they confuse access to synthesized information with internalized understanding.

Because they didn’t sweat over the logic, they haven’t built the mental scaffolding to actually comprehend the material. They possess the final product, but if asked to defend it in a meeting without their laptop, they fall apart. They have the presumption of wisdom with none of the baseline knowledge.

Engineering Out the Muscle Tone

From an HR and Learning & Development perspective, this is where things get truly dangerous. Real human learning requires friction—what cognitive scientists call “desirable difficulties.” Grappling with a complex problem physically changes your brain.

AI short-circuits this loop. It delivers a finished artifact—and a massive dopamine hit of task completion—for zero cognitive effort. It trains the workforce to view mental struggle as an annoyance to be bypassed. We are optimizing away the very friction that makes us smart.

The Illusion of Rigor

When a human writes from scratch, they know exactly where their logic gets fuzzy. The manual process leaves a transparent trail of their limitations. AI hides all of this. The machine doesn’t highlight its own uncertainty; it presents every claim with the exact same authoritative voice. Workers feel like they’ve conducted exhaustive research, when they’ve actually just outsourced their critical thinking to a black box.

The Strategic Advantage of Awareness

It’s easy to look at this intellectual decay and panic. But recognizing the hallucination of competence isn’t a reason to ban AI. It’s actually your greatest strategic advantage.

Once HR leaders and executives understand that frictionless output degrades human expertise, everything changes. Understanding this trap is the blueprint for using the technology correctly. It allows us to intentionally design workflows that reintroduce friction where it actually matters.

When you know the illusion exists, you stop training employees to be passive prompt-pushers and start developing them into rigorous editors, critical interrogators, and systems thinkers. You use the AI to gather and organize raw material, but you force the human to do the heavy lifting of logical vetting.

The organizations that win won’t be the ones that automate away all their cognitive friction. They will be the ones who understand this psychological trap and use that awareness to build a workforce where technology scales output, but the human brain retains its muscle tone.

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Dunning-Kruger Model from AgileCoffee

For Frederick Taylor, management was about discovering the one best way to do a job. Work could be measured, standardized, and optimized. The manager’s role was to design the system and ensure that everyone followed it. Expertise lived at the top of the organization. Workers executed.

That idea worked well when organizations were primarily collections of routine work.

It fell apart as work became more specialized.

The growth of engineering, finance, marketing, software, medicine, and human resources created organizations in which no manager could possibly know enough to direct every important decision. Peter Drucker called these people “knowledge workers,” but his larger point was about management itself. Once knowledge became distributed throughout the enterprise, the manager’s job changed.

Managers no longer made decisions because they knew the answers.They made decisions by deciding whose answers mattered.

Every organization is a network of distributed expertise. No one, not the CEO, not the head of engineering, not the CHRO, understands enough about every discipline to resolve every question. Knowledge is scattered across the enterprise, accumulated through education, experience, and repeated exposure to problems that never appear in a manual.

That means managers rarely evaluate problems directly. They evaluate the people interpreting those problems.

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When a production line fails, the question isn’t simply how to fix the machine. It begins with identifying the engineer who understands this particular failure. When a key employee resigns, the issue isn’t merely how to replace them. It starts by determining which recruiter, manager, or colleague has the clearest understanding of the labor market. In every function, managers begin by locating expertise before they act on it.

Experience matters, but not because experts possess more facts. Experts recognize patterns.

They know which details deserve attention because they have seen similar situations unfold before. They remember the warning signs that preceded a customer defection, a project failure, or a hiring mistake. Much of that knowledge is never documented. It emerged through practice rather than instruction.

Organizations depend on experienced employees for another reason. They carry institutional memory.

They remember why a policy exists, why an acquisition failed, why a particular customer requires special handling, or why an apparently sensible idea has already been tried three times. Every organization contains knowledge that exists only in conversation and recollection. Managers rely on that memory far more than most organizational charts acknowledge.

The problem is that expertise cannot be observed directly. Managers see its shadows.

They infer competence from credentials, job titles, years of experience, reputation, previous results, and confidence. These are useful indicators, but they are only indicators. Long tenure can conceal complacency. Confidence can overwhelm evidence. Titles often reflect history more than current capability. Some of the most valuable experts are almost invisible because they are careful rather than charismatic.

The practical work of management is learning to distinguish demonstrated judgment from persuasive storytelling.

That challenge has become progressively more difficult.

Organizations now generate oceans of information. Dashboards, assessments, surveys, financial reports, labor market data, customer analytics, and predictive models all claim to reduce uncertainty. They do, but only partially. Data still require interpretation. Evidence still competes with evidence. Every important decision arrives before enough information exists to eliminate risk.

The manager’s responsibility remains unchanged.

Someone must decide which evidence deserves confidence, whose judgment carries the greatest weight, what risks are acceptable, and when enough certainty exists to move. Accountability cannot be delegated, even when analysis can.

This is why management has steadily moved away from supervision and toward judgment. Taylor’s manager optimized work. Drucker’s manager coordinated expertise. Today’s manager assembles evidence from multiple sources, weighs competing interpretations, and commits the organization to a course of action that will only later be revealed as wise or mistaken.

Management is often described as the allocation of capital, labor, and time.

Increasingly, it is the allocation of confidence.

The organizations that consistently outperform their competitors are not necessarily those with the greatest concentration of expertise. They are the ones that become better at identifying expertise, testing it against evidence, combining diverse perspectives, and knowing when to challenge even their most trusted authorities.

Managers do not make decisions because they know the answers.

They make decisions because they have learned whose judgment deserves to shape the future.

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Pattern Image by Arthur Mazi on Unsplash

It’s been awhile.

When I moved to California, my two oldest kids and I (they were 8 and 10) camped across the country. It was a reprise of my first cross country road trip… east coast to Denver to Tuscon to LA to San Francisco. It was a privilege to watch their faces as they saw the unimaginable geography and geology of our country.

As we left Arizona and headed to LA, we went from the darkness of the desert night into the bright lights of the megalopolis. The Beach Boys informed that leg of the trip. We ended up staying the night in Anaheim, across the street from Disneyland.

The next morning we tackled the amusement park. While there were a number of magic moments, Space Mountain stands out clearly in my memory. I’m quite tall and most of my height is in my torso. I slouched down in the seat so that I wouldn’t get decapitated on the ride.

That’s the kind of thing the ride’s designers hope you feel.

Space Mountain is all about disorientation. It is a fast, smooth indoor roller coaster that uses darkness, sound, and carefully controlled lighting to convince your brain that you are flying through space at impossible speed. The ride lasts only about two minutes, but the sensory experience makes it feel longer.

Your eyes never adapt because there is almost nothing to see. Tiny stars appear overhead. A comet flashes past. A spiral galaxy rotates off to one side. Your brain desperately tries to construct a horizon, but there isn’t one. The track twists left. Then right. You bank sharply. A small drop feels much larger because you cannot see it coming.

Occasionally a glowing planet appears close enough to seem reachable before vanishing behind you. Meteor streaks flash by. Blue-white stars whip overhead. Brief bursts of light reveal just enough of the track to heighten anticipation before darkness swallows everything again.

You see just enough to almost get oriented. Then it gets dark again. The glimpses of stars, planets, and (occasionally) the track create a sense of coherence that keeps escaping.

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AI feels just like that.

This morning ChatGPT spent a bunch of energy telling me how smart I am and how unique my ideas are. It glossed right over the inconsistencies and incoherence of my forming notions. Like Space Mountain, it harnessed my mind’s desire for clarity to create an illusion.

It takes serious work to see that the darkness is central to the illusion. It happens much faster in AI, but the trick is the same. Unlike Space Mountain, AI has no lines and wants to keep me on the ride for as long as possible.

I can’t begin to tell you how deeply I enjoy exploring ideas with all of the LLMs. It’s fast, responsive, and shallow. It spends its time looking for the fastest way to please me.

Unlike Space Mountain, I try to use LLMs for work. To do so, I have to continuously route ideas through all three of the tools I use regularly. I get an answer in one place and then ask a different LLM to review and critique the idea. Then I do it with a third LLM. What I gain from that process is a clearer understanding of what I’m missing.

What I’ve discovered is that using three LLMs isn’t really about getting a better answer. It’s about getting off the ride.

Each model has its own blind spots and its own ways of flattering me into believing I’ve reached clarity. When they disagree, the darkness becomes visible. The contradictions are the point. They tell me where I still have work to do.

Space Mountain ends when the lights come on and you roll back into the station. You realize that what felt like an impossible journey happened inside a carefully designed building. AI doesn’t have that moment. The ride has no natural ending. It will happily keep generating confidence long after it has stopped generating understanding.

The job isn’t to find the smartest model. It’s to build habits that make the darkness visible. Curiosity isn’t enough. Skepticism isn’t enough. You need a way to step outside the illusion and ask what you’ve missed.

That’s where the real work begins.

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Most big changes arrive quietly, as paperwork. That is how work changed in 1949. No parade, no headline, just small moves that, stacked together, reshaped how we think about every job in America.

Before 1949, hiring was a bet on a person. A boss looked at you and asked whether you could learn, grow, and get better. You were hired for your potential. The person was the point, and the job formed around them.

After 1949, that question turned inside out. Instead of asking who a person was, employers began asking what a job was. The job became what mattered. The person became something you poured into it. Several forces drove it, all at once.

During World War II, the Army had to place millions of people fast, so it built a test that ranked each soldier by five levels of ability. When the war ended, its builders carried it into business. By 1947 you could buy a civilian version. Meanwhile the GI Bill flooded the workforce with graduates, stamped with degrees that proved what they could do.

Then two more pieces fell into place. Two brothers in New Jersey built a company that did one boring thing: it ran other companies’ payroll. You know it today as ADP. Payroll stopped being a clerk with a pen and became a service you bought.

The other piece was a law almost nobody outside government remembers, the Classification Act of 1949. The federal government was the country’s largest employer, and the law sorted nearly every federal job onto a ladder of fifteen grades. Each rung was defined by the job’s difficulty, responsibility, and required skills/qualifications. That system still runs, which is why a job is called a GS-9 or a GS-12.

Sit with what that meant. The job carried its grade and its qualification requirements before any human walked through the door. The slot came first. The person came second. We designed the box first, then went shopping for someone box-shaped.

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Once you see it, you see it everywhere: job descriptions, pay grades, “required qualifications,” each treating a job as a fixed slot the worker happens to fill today. It is where specialists come from. Once a job carries its own must-haves, you stop hiring people who can learn anything and hire only those who already match the list. Potential takes a back seat, and the checklist drives.

Intended to inhibit discrimination, the approach caused its opposite. Marginalized people without the ability to gain qualifications are inherently discriminated against.

The pattern peaked in the 1990s, when the reengineering craze urged companies to blow up their job structures and rebuild around how work flowed. People called it new, but it was 1949 reaching its conclusion. You can only blow up jobs that are already solid objects, and 1949 is what made them solid.

For seventy-five years the job won and the person lost, for a mechanical reason: no company could know its people one by one at scale, so it managed categories instead.

Artificial intelligence is dismantling that constraint.

For the first time, we can begin to see what a person actually knows, how fast they learn, and what they might become, across thousands at once. The very thing that forced the position to come first, our blindness to individuals at scale, is what AI will take away.

The early echoes are already here: skills-based hiring, the fading power of the college degree. As machines get better at reading people as individuals, the pre 1949 question becomes worth asking again: who is this person, and how far can they grow? The shift that once put the job first will now quietly put the person back ahead of it. And it will probably arrive the way the first one did, not with a bang but as paperwork.

Photo by Boston Public Library on Unsplash

Image: Army General Classification Test. First Civilian Edition © Smithsonian Institution/

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Agents wear out.

It happens in a variety of ways. And the closer your work is to agentic engineering, the more complex those ways become.

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Sidequest:

This diagram is how Google currently imagines the necessary components of agentic architecture (white paper, audio summary). They say, “Model = LLM (10%) + Harness (90%).” They mean that the integrated pieces of agentic design include:

• Specification

• Instruction

• Evaluation

• Testing

• Guardrails

• Orchestration

• Deployment

• Scaling

• Governance

• Security

• Memory

It’s where all of the development work lives. In the image, the LLM is in the center. Everything else is the harness.

This is a large and complex rabbit hole. At the frontier, agentic engineering is remarkably sophisticated, and getting more so. As it evolves, it looks less and less like hobbyist tinkering and more like a form of enterprise software development. Just because you can build using natural language doesn’t mean that you’ve figured out how to control the LLM.

Each of those eleven components also ages on its own clock, which is exactly why this gets complicated.

Back to the Mainquest:

Work, with the possible exception of high-volume, repetitive tasks, is an emergent phenomenon. As time passes and the world changes, so do processes, methods, tools, and outcomes. Even though organizational structure, team composition, and job titles remain somewhat fixed, the content of the work changes with circumstance. This is part of the reason that job descriptions and jobs so rarely match up.

It gets worse with agents. The harness drifts naturally while the underlying model gets better. Harnesses are partly built to compensate for a model’s specific blind spots and failure modes; when the model improves, those blind spots shift, and the guardrails, prompts, and evaluations built around the old ones quietly stop matching what’s actually in front of them. Each of the layers of the harness decays at different speeds. Sooner or later, the agent needs to be recalibrated, updated, repaired, or replaced.

This is in addition to the natural drift of the job itself.

Recalibration means re-running the evaluation suite against the new model to see which guardrails still catch real problems and which are now just ceremony. It means rewriting instructions and prompts that were quietly compensating for a weakness the model no longer has. It means reexamining and revising the fundamental specification. And it means deciding, layer by layer, whether to patch, replace, or retire, because not every part of the harness breaks for the same reason or on the same schedule.

That sounds like a job description.

Organizations already employ people whose entire function is to notice this kind of drift in software systems and correct for it: site reliability engineers (SRE) exist because infrastructure and demand evolve at different rates, and someone has to keep them aligned. Agents have the same problem on a faster clock with more dimensions, since the model underneath can change on a vendor’s release schedule rather than a depreciation schedule. Does that make “agent reliability engineer” a coming job title, the same way SRE became one for infrastructure?

Maintenance, in the end, is the process of keeping purpose and function aligned. For an agent, purpose changes faster than function does, which means maintenance isn’t a one-time setup cost. It’s the what you have to do for the agents you build. It’s the job.

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This one’s a little different. It involves hiring, probable ATS rejections, and questions about bias in tech. But Swanson v. IBM (filed in Federal District Court for the Western District of Texas) is by a former employee against his employer, IBM. It’s not about an HR Tech Company, at least directly. Yet.

The reason why this matters is because the employer is the one on the hook no matter who gets sued. They are the ones who created the adverse employment action when a candidate gets rejected, no matter how the candidate gets rejected.

And even when the employer doesn’t get sued, they are still on the hook because they’re the ones with all the evidence. For example, in Mobley v. Workday, the plaintiff only sued Workday. That’s because they want to create a class action for as many people as possible who have been rejected by employers using Workday tech. Suing employers would limit the class and make things very messy. But it’s the employers who will get subpoenaed to produce their hiring data, tech settings, and other records relating to the rejections.

It’s part of doing business. But it sucks to deal with subpoenas all day. Ask Facebook who has a whole team of people who do nothing but respond to records requests for copies of people’s feeds for use as evidence in employment, custody, domestic violence, and injury disputes. Really. I don’t care how funny it was in the moment, if you’re off work for a back injury, don’t post the photos of doing belly flops off a cliff in the Bahamas. (The employment lawyers are all nodding and thinking, “I had a case like that.”)

Back to Mr. Swanson and IBM. He worked successfully at IBM for over 24 years and was let go. They specifically told him it had nothing to do with performance. He suspected that the company was trying to shed older, more expensive workers and prioritize “Early Professional Hires.”

After leaving, he started seeing postings for jobs surprisingly similar to the one he had left, which undermined any argument that his position was eliminated. So after a couple months, he applied for one. Why not? He was certainly qualified.

But he received a GLIYFE (good luck in your future endeavors) auto rejection within 2 days of applying. Hmmmm. We have a candidate with direct experience who was definitely qualified for the role but rejected without even an interview. It is not exactly surprising that now there’s an age discrimination suit.

IBM hasn’t answered the complaint, but all answers deny liability so we know what it will say. The real issue is what is the evidence?

There may have been a filter or setting on the ATS that created biased outcomes based on age. It may have even been a don’t-rehire-people-who-were-terminated filter, which seems wrong but may not be illegal. Or maybe the guy was a jerk to deal with and someone actually saw the application and said, “Oh hell no, not him again.” We’ll eventually find out.

In the meantime, I’m getting calls from older white men who think they might have been discriminated against based on age. They are fascinating discussions since most of these people have never experienced discrimination before in their lives; they’re often in shock that it actually happened to them.

The reality is that many investors don’t care. Money is more important than people to them. And it makes perfect sense, some even argue that they have a fiduciary duty, to get rid of more expensive employees if it increases profits.

The trouble is that it’s illegal under both state and federal employment law. Civil rights really are for everyone. And the minute it's okay to discriminate against anyone, then everyone is next.

Discrimination is also stupid and wrong. I guess it’s time to quantify the value of institutional knowledge, wisdom and good judgment.

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The responsible deployment and use of AI begins with governance. Modeled (to some extent) on the ideas of financial accountability, AI governance looks to the future while its financial progenitor looks backwards. Financial governance is retrospective. It explains and controls the movement of money that has already been spent. AI governance is prospective. It establishes the boundaries within which future decisions, models, and deployments can occur.

Governance is the process of translating organizational values, risk tolerances, and authority structures into operational rules.

Financial accounting covers levels of financial authority and documentation requirements. Its sole focus is the management of cash and capital. It determines who can sign for what, what constitutes an acceptable invoice or receipt, how costs/budgets are allocated, and the elements of good reporting.

AI accountability is a couple of layers more complex. That’s because the underlying elements are dynamic and likely to change over time. Large organizations are likely to require dozens of policies. Many of the key pieces of AI governance remain to be understood and formalized:

• Data Governance

• Model governance

• Human oversight

• Security and compliance

• Vendor and license management

• Performance and productivity measurement

This is not exciting stuff.

But, attention to governance prevents a future of locking the gate after the horse has left. It’s easier to build in advance than it is to manage after the catastrophe. It requires the very hard work of navigating layers of approval levels for interdepartmental work.

And, we are learning what we need as we go. The Silicon Valley philosophy of leaping before you look is great for discovery and innovation.It’s also an excellent way to create risks that only become visible after the system is embedded in the business. Actually harnessing innovation and disruption demands clear repeatable constraints.

As dry as it is, AI governance is going to require the attention of our best minds. This is where the dryness of the arena is a significant problem. The sorts of people who are good at defining constraints are not the same people who succeed with discovery.

The challenge is not finding the perfect balance between control and innovation. The challenge is building institutions that can accommodate both. The people who discover new possibilities are rarely the people who define durable constraints. AI governance requires both disciplines, working together while the terrain continues to shift beneath them.

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Youth unemployment is back in the headlines. Recent coverage often attributes young workers’ struggles to AI, return-to-office policies, corporate cost-cutting, or other contemporary forces. Before looking for new explanations, it’s worth asking a basic question: Is youth unemployment unusually high?

Today’s entry-level rate is roughly half of what it was when I entered the workforce in 1978 (17.8%). The unemployment rate for people between 25 and 54 (Prime-age unemployment) was about 8%. That gap persists over time. The entry level unemployment rate is always 2 to 2.5 times the Prime age rate.

The difference is structural and remarkably persistent across economic cycles.

Throughout this article, “entry-level” refers to workers ages 16-24, the age group most likely to be entering the labor market for the first time. It just takes longer for young people to find their place in the workforce. Today’s entry level unemployment rate is near an historic low. It’s actually falling slightly. So, what’s the fuss about?

In the following chart, it’s clear that the entry level unemployment rate starts to spike in advance of a recession (the grey vertical bars). Historically, youth unemployment often begins rising before recessions become visible in broader labor-market data. Entry level jobs are always hit harder and earlier.

Over the past 50 years, the entry-level unemployment rate has averaged 2.5 times the prime-age level. The most striking feature of the data is not the unemployment rate itself. It’s the relationship between youth and prime-age unemployment. For half a century, young workers have been roughly 2 to 3 times more likely to be unemployed than workers in their prime earning years.

If young people are supposedly unable to find work, we would expect them to be actively searching. Participation data suggest something different. The reason that the entry-level participation rate declined is a marked increase in college enrollment. Over the 50 years in the chart, the percentage of entry-level aged people who are enrolled in college nearly doubled! College attendance reduces labor-force participation because students are less likely to seek full-time employment while enrolled.

None of this means that today’s graduates face no challenges. Wage growth, housing costs, credential inflation, and changing employer expectations are real concerns. But the unemployment data do not support the claim that young workers are experiencing historically unprecedented difficulty finding work.

The real story is not that young workers suddenly face an unprecedented labor market. Youth unemployment isn’t a sign that the labor market is broken. It’s a reminder that entering the labor market has always been harder than being established in it.

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Here are the relevant links

• Youth unemployment, ages 16–24 (series LNS14024887): https://fred.stlouisfed.org/series/LNS14024887

• Prime-age unemployment, ages 25–54 (series LNS14000060): https://fred.stlouisfed.org/series/LNS14000060

• Labor force participation, ages 20–24 (series LNS11300036): https://fred.stlouisfed.org/series/LNS11300036

• College enrollment stats: Full historical table (1970–2017), NCES Digest Table 302.60:https://nces.ed.gov/programs/digest/d18/tables/dt18_302.60.asp — direct Excel download: https://nces.ed.gov/programs/digest/d18/tables/xls/tabn302.60.xls

• College enrollment stats: Current figures + methodology (NCES Condition of Education, “College Enrollment Rates”):https://nces.ed.gov/programs/coe/indicator/cpb/college-enrollment-rate

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In 1949, Vitamix aired what is often considered the first infomercial. A 30-minute Vitamix demonstration from that era feels like a period piece. A W.C. Fields lookalike explains how the expensive blender can turn whole wheat, rice, soybeans, and corn into flour. The sales pitch is instantly recognizable: benefit after benefit, layered one atop another.

Value stacking is the technique of adding benefits one at a time as a sales process. It works because it reframes the decision. Instead of asking whether to buy, the prospect begins asking what they’ll miss if they don’t. This is the heart of an infomercial.

Rather than merely lowering prices, a company layers supplementary benefits, perks, or bonuses into a comprehensive package. Continuously adding complementary items—such as expert guidance, additional resources, templates, or cheat sheets makes the core price feel fair and highly compelling.

Modern SaaS marketing often uses the same technique: templates, playbooks, communities, coaching, and bonus resources are bundled around a core product to make the offer feel irresistible.

The same logic is beginning to shape online publishing.

Have you noticed that posted material is growing longer and longer and more and more frequent? There is a feedback loop between algorithmic engagement and texts powered by ‘AI as a performance enhancing technology.’ When quality is difficult to measure automatically, length becomes an easy substitute. It’s engagement versus information.

When length is confused with depth in this way, there is a big incentive to go longer. And, I see that in the increasingly long but shallow material that makes its way to my feed. I assume we are all buried in it.

They look like infomercials for personal brands. Like classic infomercials, they accumulate lessons, frameworks, anecdotes, and promises one layer at a time. Every additional paragraph functions as another bonus.

The rhetorical structure is familiar:

• “But wait, there’s more.”

• “And that’s not all.”

• “What could you possibly be waiting for?”

The goal is the same as it was in 1949. Keep the audience engaged long enough that the question changes from “Do I need this?” to “What might I miss if I stop paying attention?” The modern infomercial isn’t selling blenders. It’s selling attention.