Category: Leadership

Picture five team leaders in a room near the end of the year, working out an unwritten agreement: each of us will sacrifice one of our own people into the bottom performance rating, we will take turns so that nobody’s team bleeds twice in a row, and the deal extends upward too, because each year one of the five leaders themselves has to wear the bottom rating, regardless of how well their team performed. My senior leader at the time proposed it himself, openly, as the sensible way to run the year-end process. We called it the merry-go-round.

The absurdity is the point. The merry-go-round was the rational response to the system we were given, and once you see why, you start recognising the same machinery in every forced ranking exercise you’ve ever sat through, including the ones dressed up as redundancy selection.

Here’s the system. End-of-year appraisal, rank and stack. Every team sorts its people into buckets, call them A, B and C: exceptional, meets expectations, below expectations. And a fixed percentage of every team must land in C. Say 10%, and that percentage is compulsory. The spreadsheet does not ask whether your team actually has poor performers. It asks you to produce them.

Now suppose your team is genuinely high performing. Suppose you’ve spent 2 years building it, the delivery record shows it, and the team has been beating every target set for it. The quota doesn’t care. Someone in that room is going to carry a below-expectations rating home to their family, and your job as their leader is to choose who, knowing the choice has nothing to do with what they did this year.

The standard defence, and I heard it every year, is that the C ratings get compared across teams later, so the process is fair in aggregate. That comparison has a name: recalibration, and I watched how it actually ran. All the leaders come together in a room and rank everybody’s proposed ratings against each other, with HR coordinating the process. After that, the senior leaders hold their own round and rank their direct reports, the leaders themselves, the same way we had just ranked our teams. I’d guess there were further rounds above that, with rules I never got to see. And here is where the whole thing quietly falls apart, because the people settling the ratings are 2 or 3 levels removed from the actual work. They have never seen your team member deliver anything. The session runs on questions like “who is so-and-so?” and “what has so-and-so done this year?”, and a person’s rating, bonus and reputation get settled on the strength of whatever answer happens to be in the room.

Your rating comes down to your leader’s debating skill. If your team leader is articulate, if they can run logos, pathos and ethos across a conference table and argue a proposed C back up to a B, power to you. If your leader can’t debate, or won’t fight for you, then I’m sorry mate, that’s it. The rating follows the advocacy, the advocacy follows the personality of your leader, and none of it has much connection to your work. There was no shared benchmark for what performance meant across teams, so every leader rated arbitrarily, and recalibration didn’t remove the arbitrariness, it just decided whose arbitrariness won.

Once you understand that, the merry-go-round stops looking crazy and starts looking like game theory. If the quota is fixed before anyone looks at the work, the only question the system leaves open is who absorbs the damage. And once that’s the question, rationing the damage fairly, taking turns, spreading the pain across teams and across years, is exactly what reasonable people do. The five leaders in that room were queuing politely for a punishment the system insisted on handing out. They even queued themselves into it, since one leader per year took the C rating personally. You can call that integrity of a sort. The system asked for sacrifices; they organised a fair roster of sacrifices.

There’s a quieter cost underneath the game theory, and it’s the one the case studies keep naming. Forced distribution isolates people from their own performance. The rating a person receives stops being information about their work and becomes information about the quota, the roster and the room, yet it lands on them as if it were a verdict on the work. I’ve sat across the table from a team member who met every expectation we agreed at the start of the year and told them the organisation had rated them below expectations, and we both knew why, and neither of us could say it out loud. You do that to someone once and something doesn’t come back: the engagement goes first, and the trust goes with it. Multiply it across the team and you get the second effect: people stop helping each other. When the buckets are fixed, your teammate’s good year raises the odds that the C lands on you, so collaboration quietly turns into competition, inside the same team, among people whose work depends on each other. The system doesn’t announce this. It just prices it in.

None of this was invented locally. The apparatus was imported, mostly from the United States. Jack Welch ran it at GE as the vitality curve: celebrate the top 20%, keep the middle 70%, remove the bottom 10%, every year, forever. For a couple of decades the big corporates copied it as best practice, Microsoft among them. Microsoft finally abandoned stack ranking in 2013, after years of it being cited, internally and in business school case studies, as a system that made employees compete against their own teammates and that people experienced as demoralising and unfair. The verdict was in long before most organisations stopped. It kept travelling anyway, because forced distribution looks rigorous on a slide, and because it spares senior leaders the much harder job of actually knowing the work well enough to evaluate it.

And now the part that still makes me laugh, in the way you laugh at things that cost people real money. Suppose the organisation finally reads the case studies and decides to drop the system. Whoever’s turn it was on the merry-go-round in that final year is now carrying a below-expectations rating on their permanent record, for a rotation that no longer exists. Conned is the polite word for it. I know, because it happened to me. I objected, I brought the delivery evidence, and I had argued against the rotation itself in the calibration room more than once. My leader insisted it was my turn that year. Maybe my time was up for arguing. The wheel handed me the C, the system got retired, and the record stayed. I left not long after.

Redundancy runs on the same maths

Performance appraisal is the annual version of this machine. Redundancy is the crisis version, and it runs on the same maths: the number comes first, the evaluation comes after, if it comes at all. When an organisation decides it needs to remove some percentage of a division, the selection is another rank and stack, done quickly, by leaders who often have no better method for deciding who should stay than they had for deciding who was a C.

So the selection follows patterns that anyone who has watched a few rounds can recite. If you’re in the leader’s bad books, you’re a target. If you’re a mediocre performer, you’re a target. If you’re a poor performer, obviously. And if you’re a top performer, you may be a target as well, because nobody loves the most hardworking person in the team, and in New Zealand the tall poppy syndrome makes standing out its own kind of exposure. Good luck to the tall poppies.

The narrative wrapped around the process will talk about skills matrices, future capability requirements and fair selection criteria. Half of it is bullsh*t. The other half, depending on the organisation, is who you know and who you hang out with. That’s a blunt thing to write, and I’ve sat on both sides of these processes over the years, as the leader filling in the buckets and as a name on somebody else’s list, so I’ll stand behind it.

So what do you do with this, other than get cynical? Back in 2008 I read a book called Bulletproof Your Job and wrote about it on this site. I won’t repeat the whole playbook here, but the people I’ve watched survive these systems, myself included on the good years, did a few things consistently. They understood that the rating system is a game running on top of the work, with its own rules about quotas, turns and advocacy, and they never assumed the work would speak for itself. They paid attention to whether their leader could and would argue for them, because in a recalibration room your leader’s voice is your performance. They kept their own record of what they delivered, with numbers, because “what has so-and-so done?” gets answered with whatever evidence happens to be at hand. And when someone proposed an unwritten agreement where you volunteer for the bottom bucket because it’s your turn, they remembered that the wheel can stop at any time, and that it stops on whoever is in the worst seat.

I’ve taken my ride on the merry-go-round. Once was enough.

AI can read what’s written down. It can’t read the room.

I was naive the first time I got close to real power.

I was CIO for the Malaysian operation at ABN AMRO, and it was the first time I’d been anywhere near the corridors where decisions actually got made. Up to that point, I’d assumed that what happened in meetings was mostly what was happening. I learned quickly that wasn’t true. There were deals behind the scenes, agreements reached in rooms I wasn’t in, what I’d call horse trading, the kind of give and take between senior people that never appears in any paper, any minutes, any business case.

I didn’t have anyone showing me the ropes before that. No mentor pulling me aside to explain how things actually worked, the way some people get. Most of what I learned, I learned by getting it wrong first, reading senior leadership moves badly, more than once, because I’d assumed, the way management textbooks teach you to assume, that everyone in the room was working toward the organisation’s stated goals and that personal interest sat somewhere well below that. I went looking for better teachers wherever I could find them, suppliers, older employees who’d seen more cycles than I had, and through my MBA, where I sought out senior executives at other listed companies who could tell me what the textbooks left out. Some of them taught strategy and financial management at business school themselves. What they gave me wasn’t in the assigned reading. Looking back, those were my most formative years, the MBA and ABN AMRO running at the same time, each one teaching me something the other couldn’t.

What you see in the room may not be what it actually means. And if you raise something, even something real, something you’ve genuinely identified as a problem, it can go nowhere. Not because anyone disagrees with you. Often nobody disagrees with you at all. You get lip service, you’re told it’s a good point, worth considering, and then you’re kept busy, genuinely busy, with work that feels important, until at some point you’re told to stop. No budget. Leadership doesn’t approve it. Other priorities. Whatever the reason given, it’s rarely the real one.

I used to think that was wasted effort. I don’t think that anymore. I think it’s closer to camouflage. Keeping you occupied with something real enough to feel meaningful means you’re the one focused elsewhere, not the one asking questions, not the one creating friction around whatever the actual direction of travel is. You become part of the cover, without ever being told that’s the role you’re playing.

Here’s what that looked like in practice. The Managing Director issued a directive, get rid of personal colour printers across the operation, replace them with three leased colour copiers instead. Sound economics, better cost control, easier to monitor consumption. I followed it. Then the Asian Financial Crisis hit, and in the middle of it, Treasury delivered the bank’s entire year’s profit target in a single week. The Head of Treasury came to me asking for a colour printer of his own. I declined. We had a directive from the MD, and as far as I was concerned, that was the end of the conversation.

I hadn’t read the room at all. I was reprimanded for it, called penny wise and pound foolish, for refusing to sign off something so small against the scale of what Treasury had just delivered for the bank. The policy was real. The directive was real. None of that mattered once a senior leader had just made the year for the entire Malaysian operation. That’s when I understood what equity, fairness, and meritocracy actually meant inside an organisation, and what they didn’t. The dots I needed to connect weren’t in the policy document. They were in who had just earned the standing to ignore it.

That was ABN AMRO. The lessons came one humiliation at a time, and I was new enough to all of it that I didn’t yet see the whole pattern underneath.

Years later, at Te Pūkenga, I understood it completely.

We had real work underway, a digital transformation to bring 25 separate polytechnic and vocational education platforms down to one, a programme that would have cut close to a third of the operating cost across the network. The case for it was strong. And it kept hitting roadblocks, one after another, each with its own reason, no funding yet, no approval yet, other priorities ahead of it, despite earlier agreement that it would proceed.

By that point I’d learned to read the room differently. Human nature doesn’t change much, whatever era you’re in. When the people closest to the centre, the ones who hear things in rooms you’re not in, start quietly updating their CVs, start having conversations that sound like they’re already planning their next move, that tells you something the org chart never will. It’s a filter of a filter. They’re reacting to information you don’t have access to, and their behaviour is the signal.

At Te Pūkenga, a change in direction was coming for the organisation, well before it was formally announced. I’m not going to comment on whether that decision was right or wrong, that’s well outside anything I have a view worth sharing on, and frankly it was beyond anyone at my level to influence either way. What I will say is that the roadblocks we kept hitting, the funding that never quite arrived, the approvals that kept slipping, made a different kind of sense once you understood that the organisation’s future was already an open question at a level none of us could see directly. The work was being held in place because committing real money to a transformation that would take years to deliver didn’t fit with a future that might not include the organisation in its current form.

Nobody told us that. Nobody could have, probably, even if they’d wanted to. We worked it out by watching what the people around us did, not what they said.

Sense-making is connecting what you observe, what’s said directly, what’s implied, how people’s behaviour shifts, against everything you already know about how organisations and the people running them tend to behave, until a shape emerges that nobody has confirmed but that turns out, later, to be correct.

Not every signal points toward self-interest, though. I learned that at ANZ too, watching a different kind of decision get made.

Graham Hodges joined ANZ New Zealand as CEO in November 2005. The decision to merge the ANZ and National Bank mainframes and systems had already been sitting in analysis since 2003, two rounds of review, a mountain of documentation, no call made. During his tenure, he made it, the kind of decision a more cautious leader might have let sit for another review cycle. Then he did something nobody had budgeted for. He proposed ANZ lead the development of a Snapper card for public transport payment in New Zealand, something like London’s Oyster card, or the Octopus card he’d have seen used in Hong Kong. Nothing in the existing programme of work accounted for it. He asked for budget to be pulled from other initiatives to fund it. I was in the room at a leaders’ update when he told the whole leadership team why, that New Zealand needed it, and that ANZ’s brand belonged at the front of building it, not because any individual business unit had asked for it. Snapper went live in July 2008.

Nobody saw that coming. Every CEO ANZ HQ had sent out before him had played it safe. This read differently to me, closer to conviction than caution, the kind of bet a leader makes when belief in an idea outweighs the safety of staying inside existing budget lines. The bet didn’t cost him either. He left ANZ New Zealand in May 2009, promoted to Deputy CEO of the ANZ Group. Reading the difference between someone protecting themselves and someone genuinely backing an idea they believe in is its own kind of sense-making, rarer than the other kind, and worth getting right, because mistaking one for the other costs you something different each time.

And here’s where I think AI sits in all of this, and where it doesn’t.

AI is extraordinarily good at the documented layer of an organisation. The papers, the reports, the meeting minutes, the business cases, the dashboards. All of that, AI can read, summarise, generate, analyse, faster than any of us could. Parts 3 and 4 in this series were both, in a sense, about that documented layer, the steering committee pack, the business case waiting for approval above a financial threshold. AI makes all of that faster and more polished.

The decision lived in belief and fear, sometimes tangled together the way they were at ABN AMRO, sometimes belief running clean on its own, the way it did with Hodges and his Snapper card. Either way, it sat with a small number of people at the apex of an organisation, employees too, whatever the org chart said about their seniority, their real stake nothing more than their position, their income, their title. And almost none of that gets written down. It can’t, often, because writing it down would make it real in a way that creates its own problems. So it travels through behaviour instead. Through who goes quiet. Through which topics never quite get scheduled. Through the slight change in tone in a room that, on paper, hasn’t changed at all.

AI has no access to that layer. It was never going to. The information doesn’t exist anywhere AI can read it, because it was never put anywhere in the first place, deliberately.

So here’s what I think happens as AI takes over more and more of the documented layer. The documented layer gets faster, cleaner, more abundant, and it becomes less and less where anything is actually decided, because it never really was. What’s left, the undocumented layer, the belief and fear sitting with a handful of people, doesn’t shrink. It becomes more clearly the only layer that ever mattered, just harder to see precisely because everything around it now looks so comprehensive and so well-documented.

AI is a product, like every other product that’s come through an organisation before it. New tools change what gets produced and how fast. They don’t change who holds the decision, or what that decision actually runs on. That’s been true of every wave of technology I’ve worked through, and I don’t see why this one would be different.

What’s changed for me, I think, is that I stopped expecting the documented layer to tell me anything useful about where things were heading, a long time ago. I learned to watch the room instead.

It’s served me well. Whether it’s something AI can ever do, I genuinely don’t know. I suspect not.

AI inherits the operating system of the organisation.

When I joined Stats New Zealand in October 2016, the technology staff were quick to set me straight. “Wayne, you don’t have to teach us Agile. We really know what Agile is.”

They weren’t wrong, just years out of date about it. A senior leader had run a serious programme to introduce Scrum across the technology function sometime before March 2014, before the CDO I’d eventually report to had even started in the role. Staff were trained. They were immersed. They had the cadence, the ceremonies, the story points, the full picture. Everyone was involved, not just a small team running an experiment off to the side, but the whole technology function, committed. By any reasonable measure, the organisation had done Agile properly, at the time.

I knew something about that period myself, from the other side of Wellington. I was at Inland Revenue from January 2013 to October 2015, and there was an active Agile community of practice running across the public and private sectors through those years, people moving between organisations, comparing notes, working out what Agile actually looked like inside companies or government work rather than in a textbook. Stats NZ’s programme would have been part of that same wave.

And then the senior leader left.

And Agile left with him.

By the time I arrived, two and a half years later, the practice the staff were so proud to tell me they knew had already been gone for years. I asked, genuinely curious: how come you’re not doing Agile now? The room went quiet in the way rooms do when people know the answer but don’t want to give it. When it came, the answer was simple: leadership stopped it. The leaders above them had ended the practice, and so the practice ended.

I found that puzzling, frankly. If the team believed in Agile, if they had the knowledge and the experience to run it well, why did they need a senior leader’s permission to continue, more than two years after that leader was gone? Why didn’t they fight for it? The answer to that question tells you more about an organisation than any strategy document ever will.

Now contrast that with BNZ.

BNZ Digital operated as its own self-contained unit, deliberately isolated from the traditional technology development cycles running on waterfall. They had their own product managers, their own designers, their own development team, their own mobile development team, their own leadership, their own funding allocation. They were inside BNZ but running a completely different operating rhythm, and their interaction with the broader technology function was structured around getting changes into production without the traditional organisation absorbing them back into its own slower pace.

They weren’t waiting for the rest of the organisation to be ready. They built a perimeter and worked inside it.

The result was visible enough that when the broader BNZ Technology function restructured, it adopted the BNZ Digital model. No mandate from above, no big transformation programme, no consultants brought in to explain why digital delivery mattered. The results made the case, and the wider organisation accepted the approach as the way forward.

Two organisations. The same method. Completely different outcomes.

The difference sat underneath the method, in something I’ve come to think of as the immunity system. Both organisations understood Agile, the people at Stats NZ knew it well enough to tell me I didn’t need to teach them, so knowledge was never what separated them.

Every organisation has one. It operates independently of the official structures, the stated values, the executive messaging, the strategy deck that gets presented at the all-hands. It’s the collection of behavioural patterns, power structures, unspoken rules, historical grievances, and self-interest calculations that determine what the organisation actually accepts and what it quietly rejects. The immunity system does not announce itself. It appears in small decisions. In which proposals get funded and which get delayed until they die of inaction. In who gets consulted and who doesn’t. In which meetings matter and which ones produce nothing except a follow-up meeting.

At Stats New Zealand, I heard a phrase more than once: “If it’s not invented here, we don’t use it.”

Stats is a deeply technical organisation. Smart people, rigorous people, people who deal in mathematics, statistics, census methodology, where precision matters and intellectual credibility is the currency. That culture produces excellent analytical work. It also produces a particular kind of resistance to external ideas, because if the idea didn’t originate inside the organisation, it carries a faint suspicion, a sense that it might not be suited to the specific complexity of the work, or that adopting it would imply the organisation hadn’t already solved the problem itself.

The immunity system at Stats NZ was protecting something real: the organisation’s sense of its own expertise and identity. The logic is coherent from the inside. The cost is that good ideas, including Agile, get treated as foreign objects and rejected when the circumstances that introduced them change. When the senior leader left, the immunity system reasserted itself, and Agile, which had been tolerated because of that leader’s authority, went with him. The staff didn’t fight for it because the immunity system had never fully accepted it. They knew how to do Agile. They had just never decided it was theirs.

BNZ Digital made a different calculation. When the restructure came, the wider technology function could see clearly enough that the BNZ Digital model was something the organisation needed for its own survival. The immunity system accepted rather than rejected, because the case for acceptance was strong enough, and because the threat was positioned as a competitor in the market rather than a threat to the people inside the building.

This is the deeper mechanism, and I think it leads back to people’s own self-interest. If something new is introduced and it affects the livelihood and power structure of people in an organisation, there will be resistance. The polite word is resistance to change. The more accurate word, and I’ll say it plainly because I’ve watched it happen, is sabotage. It’s rarely dramatic. It’s the report that doesn’t get filed, the meeting that gets rescheduled until the momentum is gone, the budget approval that keeps getting pushed to the next quarter. It happens in organisations more often than leadership is willing to acknowledge, and it almost never shows up in the post-implementation review.

Now consider what AI introduces into this same system.

AI redistributes cognitive work. It compresses tasks that previously required specific expertise. It changes who can produce what and at what speed. An analyst who spent years building the ability to synthesise large volumes of information is looking at a tool that can do a version of that in minutes. A manager whose authority came partly from controlling access to information now operates in an environment where information is far less scarce. These are real shifts, and the immunity system in most organisations will register them as threats to livelihood and power, because that is exactly what they are.

Only this time, the disruption shows up on an invoice as well.

This is already playing out, and it’s more underreported than it should be. Uber reportedly exhausted its entire annual AI coding budget by April 2026, because adoption was higher than expected and nobody had modelled what happens when engineers start using agentic workflows at scale, where a single task can trigger multiple model calls, repeated context loads, verification loops, and retries. The budget assumption was built on a SaaS licensing model. AI behaves more like cloud infrastructure: usage-based, scaling with adoption, and expensive in ways that only become clear after the bill arrives.¹

A KPMG survey found that only a minority of organisations have a comprehensive understanding of their AI costs, with many discovering the numbers after the fact. KPMG’s own global head of AI has said some of their clients exhausted annual token and cloud budgets within months, one of them watching usage climb sixfold.² CFOs are getting surprised. This is an organisational maturity problem more than a technology one, the same governance gap that Agile exposed a decade ago, now running at the speed of token billing.

There is a term circulating in enterprise AI circles now: tokenmaxxing. Organisations, in some cases deliberately, encouraging employees to maximise AI usage. Some built token-consumption leaderboards, rewarding the most active users. Replit’s own AI head has called these leaderboards “very dystopian,” arguing publicly that more tokens do not equal more business value.³

The Agile comparison is almost too clean. Messy Agile measured velocity instead of value, and teams ran sprints full of story points that delivered nothing useful. Tokenmaxxing measures consumption instead of outcome, and organisations run up bills generating AI-produced work that doesn’t change anything meaningful downstream. The metric changed. The dysfunction is the same.

The games an organisation chooses to play internally with AI will be its own demise if the path is wrong. And the path is almost always determined not by the technology but by the invisible operating system underneath it.

In organisations whose immunity system is oriented toward protecting existing power, AI generates a particular kind of dysfunction: the theatre of adoption without the substance. Tools purchased, training announced, dashboards built, constraints untouched. In organisations with a healthier operating system, AI costs get managed the way cloud infrastructure costs eventually got managed: per-team budgets, model routing, usage governance, ROI tracking per workflow. What is now being called AI FinOps. The discipline looks familiar because the problem is familiar. Every time a new technology scales faster than the operating model around it, the organisation has to build the governance infrastructure after the fact, and the bill for that delay is always larger than expected.

Two organisations can buy the same tools, run the same training, appoint the same internal AI champion, and arrive at completely different results, because the immune response they brought to the implementation was different. One accepted. One rejected. And neither the acceptance nor the rejection showed up in the original business case.

The organisational question is worth more time than the technology question before any AI rollout: what does this institution’s immunity system protect, what will it accept as necessary for survival, and who controls the informal decisions that will actually determine whether this takes root or gets quietly extinguished?

Most organisations don’t ask it. They buy the tools, announce the programme, train a cohort, and spend the next eighteen months wondering why adoption is low and the results are thin.

I’ve seen this before. The technology was different. The immunity system was the same.

1. Uber capped AI coding spend after exhausting its annual budget by April 2026: PYMNTS, CryptoBriefing. ↩︎
2. KPMG AI Quarterly Pulse Survey on AI cost visibility (only around 26% of firms report a comprehensive view), plus KPMG’s global head of AI on clients exhausting budgets within months: Implicator.ai, KPMG. ↩︎
3. “Tokenmaxxing” and Replit AI head Michele Catasta calling consumption leaderboards “very dystopian”: AOL, CIO, Pragmatic Engineer. ↩︎

I’ve been on both sides of this. At Stats NZ, at ANZ, I sat in rooms where hiring decisions were made, and I listened to managers explain, without much discomfort, why certain candidates had filtered themselves out before the conversation even started. Too old. Too expensive. Too set in their ways. I filed those observations away as a feature of how organisations work, the kind of thing you absorb without examining closely when you’re on the side where it doesn’t cost you anything. I never thought I’d be the person they were describing.

Then Te Pūkenga restructured, and I found out what the view looks like from the other direction.

What made that redundancy different was the scale of what arrived at the same time. I had lost roles before, and so has anyone who’s spent a career moving through organisations. New Zealand’s unemployment rate was rising. SMEs were closing. Retail strips were emptying. Office blocks across Auckland and Wellington sat vacant. The government had already signalled it was disestablishing Te Pūkenga entirely. Geopolitical pressures were moving through GDP numbers, and GDP numbers were moving through hiring decisions, producing fewer roles at every level, not just at the top. A single professional or individual contributor role could attract 200 to 300 applications or more. These weren’t forces I could network or position my way around. When conditions compress that hard, across that many variables at once, you stop asking what you did wrong and you start asking what is actually happening.

At one of the career transition forums that came with the redundancy package, I raised ageism directly. The coach running the session disagreed. Ageism is a myth, he said, because experience is value and the market rewards it. His prescription was presentation: revise the CV to be value-focused, strip graduation dates and anything older than 15 years, reframe long tenure as transferable skills, apply for roles one or two ranks below where you were, consider fractional or part-time arrangements to make yourself more affordable, offer reduced hours to lower the risk for potential employers. I’ve since seen the same prescription in outplacement guides, HR publications, YouTube channels aimed at “professionals in transition,” and LinkedIn posts about managing redundancy over 50. The advice is consistent across all its sources. It travels well because it sounds reasonable, and because it gives people something to do while they wait for the outcome to change.

The outcome doesn’t change. The advice is solving the wrong problem.

The moment you walk into an interview room, the CV has done its job and stopped mattering. The panel sees you, not your document. Your age is in the room whether your graduation date is on the page or not, and the panel’s response to it has already formed before you’ve opened your mouth. If the panel is younger, which it usually is, their assumptions about what you represent have been running since you came through the door. Older applicants move more slowly. Older applicants are set in their ways. Older applicants have seen too much, and “I’ve been there, I’ve done that, this won’t work” reads as obstruction in an organisation that needs to believe it’s trying something for the first time.

The irony is plain once you see it. The pattern recognition that comes from 30 years of watching the same decisions play out, which is the thing you’re supposed to be selling as value, is exactly what reads as resistance to someone who hasn’t yet sat through the cycle. A younger hiring manager who hasn’t watched a rebranding exercise fail twice, or a restructure reduce headcount without changing anything structural, or a digital transformation stall in precisely the way the last one stalled, cannot easily distinguish between an older employee who is obstinate and one who is simply right. And here’s the part that makes this genuinely hard: they may never know the difference, because every generation of employees has to learn certain lessons by living them, regardless of what the previous generation already knows. The pattern recognition is real. The inability to transfer it is also real. So the panel defaults to the safer assumption. You get filtered out, and the exit is labelled overqualified.

Overqualified is a polite word. In most of the cases I’ve experienced or observed, it doesn’t mean your experience exceeds what the role requires. It means the panel has run a calculation that won’t survive being said aloud. You might leave once something better surfaces. You might be harder to manage if the manager is 15 years younger. You might not adapt quickly enough to how the team prefers to work. None of those concerns appear in the feedback, because none of them would survive a formal grievance process. Overqualified covers them cleanly, and the conversation ends there.

A better CV doesn’t reach any of this. Perception doesn’t change in a 45-minute interview, regardless of how well the transferable skills are packaged.

Some advice goes further. Career coaches recommend working personal networks, reaching out to contacts who might know of opportunities, letting people in your industry know you’re available. I understand the theory. But what I’ve found is that professional relationships don’t carry the weight that word “network” implies. The people you work alongside are colleagues while you share the same floor. Once you leave, most of that contact fades, and fades faster than most people want to admit. And where a genuine relationship does exist, a peer-level contact inside another organisation has limited reach. The hiring decision sits with someone more senior. A peer’s recommendation rarely travels far enough up the chain to change what the panel has already decided, especially when that panel is already running the calculation I’ve just described.

Applying a rank lower doesn’t escape it either. A junior role attracts more applicants, not fewer, both younger candidates and older ones in the same situation as yourself, and the same cost and perception logic runs again, except now you’re competing against people who represent lower salary expectations and carry none of the assumptions that follow seniority. The advice to lower your sights is framed as widening your options. In practice, it narrows the corridor and adds more people to it.

What surprises me is how long the mechanism has been running without anyone naming it cleanly.

At Stats NZ, I had hiring managers tell me, without lowering their voice, that certain candidates were too old and had effectively removed themselves from consideration. This wasn’t a crisis conversation. It was a routine observation, offered the way you’d note a missing qualification, and then the meeting moved on. At ANZ, I watched older employees, some past their own stated retirement age and still contributing meaningfully, get quietly sidelined before restructures arrived. The restructure wasn’t the cause. The sidelining came first, and the restructure was the instrument. In both cases, the organisation wasn’t responding to any particular economic event or technology disruption. It was running a function it runs continuously, as a matter of normal operations, well below the level of deliberate policy.

Ageism is the current word for that function. The word is useful in the sense that it gives people a shared language for an experience that is real and measurable. Research confirms what many people already knew from inside it: across OECD countries, employment starts falling from age 50;¹ older workers who lose a job stay unemployed longer and are less likely to find another;² and employers say openly that they are less willing to hire them.³ But the word also carries an implication I find misleading, that this is a problem generated by contemporary conditions, one that could be addressed through better hiring practices, stronger legal enforcement, or, as the coaches would have it, a more strategically constructed CV.

The function predates the word. Organisations were running it in the 1990s, in 2005, in 2015. What changes across those periods is the economic context that makes it more visible to more people at once, and the vocabulary available to describe it. The function itself is stable. It’s closer to an immune response than a crisis, the organisation’s natural mechanism for cycling through generations of people, managing cost and perception thresholds in ways that rarely get said plainly. Like most immune responses, it operates below the level of deliberate decision-making. Nobody sits in a room and decides to discriminate. They make a series of smaller judgements about fit, energy, risk, and cost, and the accumulated weight of those judgements produces a consistent result. The result has a name now. It had the same shape before it had the name.

The coach who told me ageism is a myth wasn’t lying. He was describing the system he believed he was operating in, one where experience carries value, the market is rational, and presentation determines outcome. That version of the system does exist in certain organisations, for certain roles, under certain conditions. I’ve seen it. But it is the exception, not the default. The default is what I watched at Stats NZ and ANZ before I had any personal stake in understanding it, and what became undeniable during the Te Pūkenga redundancy when every economic force in the country arrived at the same moment and left very little room for alternative explanations.

I’m not writing this to argue that older employees should stop applying, or that the coaching advice fails in every situation. I’m writing it because there’s a real cost to explaining a structural function as an individual presentation problem. When the mechanism operates at the level of the organisation and the advice operates at the level of the CV, the only explanation available when the advice doesn’t work is that the individual didn’t execute well enough. Try harder. Polish the story again. That conclusion suits the system perfectly.

I’ve been on both sides of the table. I know which side has more information.

1. Employment rates begin to decline from age 50 across OECD countries, and older workers who lose a job spend longer unemployed and are less likely to be re-employed: OECD Employment Outlook 2025, chapter 3, “Navigating the golden years”. ↩︎
2. In New Zealand, workers aged 50 to 64 who lost a job involuntarily between 2001 and 2010 were 11 to 12 percentage points less likely to be employed four and five years later than comparable workers who kept theirs, while the effect for workers under 30 faded; the authors attribute the larger impact to older workers’ firm and industry-specific skills. Stats NZ survey and administrative data: Hyslop and Townsend, Motu Working Paper 17-12 (2017). ↩︎
3. Survey of hiring managers in eight countries in Europe and the United States, February to March 2023: 35% would definitely hire a candidate aged 45 to 54 and 13% one aged 55 to 65, against 47% for 30 to 44: OECD and Generation, The Midcareer Opportunity (2023), Generation. ↩︎