Category: Economy & Society

I asked an AI if it could replace me. The honest answer was sitting in the two jobs I deleted.

Back in 2024 I updated my CV for a role at Open Polytechnic, and I left two things out. ANZ. ABN AMRO. Two banks, real years, real work, missing from the page.

I wasn’t job-hunting for sport. I was at Te Pūkenga while the whole thing was being unwound, and a good slice of the tertiary education sector was reapplying for its own jobs at the same time. The CV had to do real work, against a crowd, in a hurry.

I’m not ashamed of them. They just didn’t fit the story I was telling that one reader. A CV is a signal. You’re pointing at one person who has to decide something about you with very little to go on, and you choose what they get to see.

That’s the whole game. Most people never name it.

So when I sat down a few weeks ago and asked an AI the question half my generation is quietly asking, “Can you replace me,” I already suspected the interesting answer wasn’t going to be about what I know.

The part I’ll concede first

Let’s be honest about where the machine wins, because pretending otherwise is how you lose the argument before you start.

AI is better than me at most of what I used to get paid for, and faster. Drafting a strategy paper, producing three architecture options with the trade-offs laid out, writing the policy, building the dashboard, generating the risk register. These were once high-value cognitive outputs. They were what a senior person produced to prove a senior person was in the room. They are now close to free.

And here’s the uncomfortable bit. The knowledge underneath all that was never mine to begin with. It’s out there. It’s in the books, the case studies, the frameworks, the same material anyone could find if they read more and researched harder. Gladwell’s 10,000 hours, the tipping point, Lean, Agile, service management, the whole canon I spent decades absorbing. I didn’t invent it. I accumulated it. And accumulation is exactly the thing a model does better than a human.

So if my value were the knowledge, I’d already be replaced. I think a lot of experienced people sense this and don’t say it out loud, because the alternative feels like admitting the years didn’t count.

They counted. Just not for the reason most people think.

What the machine can’t see

Here’s what I kept coming back to. The AI doesn’t know me. It knows what I’ve published, what’s documented, what’s in the public record. It doesn’t know the things that aren’t written down anywhere: my taste, what I’d refuse to do, how I read a room, why I left two banks off a CV.

I studied economics at LSE and forgot most of it, but two ideas stuck, and they turn out to be the whole story.

The first is asymmetric information. The seller knows things the buyer can’t see. George Akerlof wrote about the used-car market, where buyers can’t tell a good car from a lemon, so they assume the average, so they only pay the average price, so the good cars leave the market and you’re left trading lemons. When the buyer can’t tell quality apart, quality stops being rewarded.

The second is signalling. Michael Spence’s idea, same Nobel as Akerlof. When you can’t show your real quality directly, you send a signal that’s expensive enough that a weaker player couldn’t fake it. The degree, the track record, the reputation you’d lose if you lied. The signal works precisely because it costs something to produce.

A CV is a signal. My About Me notes, the ones I feed to an AI so it writes in my voice instead of a generic one, those are a signal too. They’re me trying to make the undocumented legible. My taste, my values, the patterns I trust, written down so the machine can see what the public record can’t.

For about a week I thought that was the answer. Document the judgment, encode the taste, and the part of me that’s scarce becomes portable. Then I noticed the problem.

The signal gets cheap the moment everyone sends it

A signal only holds its value while it stays expensive to fake. That’s the entire mechanism. The day it becomes cheap, it stops separating the strong from the weak, and the market does to it exactly what it did to Akerlof’s used cars. It discounts everyone back to the average.

A documented “here is my judgment” file is cheap. I could generate a plausible one for a stranger in a minute, full of values they’ve never lived and lessons they never paid for. So could you. So when everyone has an AI producing polished strategic thinking, polished thinking stops proving anything. The mid-level manager now sounds strategic. The junior consultant now produces partner-grade decks. The signal floods, and a flooded signal is noise.

This is the part the AI’s own answer to me missed completely. It told me to “evolve from delivering outputs to architecting judgement systems,” to become a fractional this, an AI-era that. Reasonable, fluent, and indistinguishable from what it would tell anyone with a similar CV. A lemon. Confident, generic, average.

The drift toward the mean

I’ve watched this happen with my own hands, in a small and stupid way that taught me more than the economics did.

I draw a comic strip. Bald characters, no mouths, just eyes and a nose, dry organisational humour. I trained Gemini on a few years of my strips and asked what it needed to draw in my style. After some back and forth it rendered a panel that looked exactly like mine. Then the next one had mouths. And collars. And shirt buttons, on a character that wears nothing but eyes and a nose.

Gemini drifts. Left alone, it slides back toward its training data, toward the average idea of what a character should look like, and I have to reload my source strips and re-edit the prompt to drag it back. Five or six rounds for a strip I’m willing to publish.

That’s the mechanism, sitting right there in a cartoon. The machine reverts to the mean unless something private and specific keeps pulling it off course. Feed it your broken process and you get a faster broken process. Feed it the public average and it gives you back a more confident average. The tool doesn’t audit what you hand it. It amplifies it, at a speed that makes the sameness harder to notice.

So the documented signal doesn’t save you, because the documented signal is what the machine is built to average. The thing that survives is the source material, the actual strip, the actual record of what you really did, the part you keep having to reassert because the average is always pulling the other way.

The only signal left standing

Strip it back and one signal is still expensive to fake. The track record of real choices, made under real consequence, that you would defend out loud in a room full of people who were there.

Look at the choices. Lifting staff engagement 64% at Stats NZ while the organisation was mid-transformation and exhausted. Taking Priority 1 incidents at Inland Revenue from 33 in one year to 3 two years later. Pulling 20% out of platform costs at Te Pūkenga while the whole institution was being disestablished underneath me. Those aren’t artefacts a model can generate. They’re decisions I owned, with my name on them, where being wrong would have cost me something real.

And the omission belongs in that list too. Leaving ANZ and ABN AMRO off the CV was a judgment, made for a specific reader, with reasons I could explain and defend. The AI can write the CV. It cannot make the call about what to leave out, because that call needs private context it doesn’t have and a consequence it will never carry. The edited CV is the proof of the whole argument. The judgment is in the deletion, and the deletion is the part that isn’t documented anywhere.

So what’s left to learn

If the process work and the first-pass decisions go to the machine, and the polished output stops signalling competence, what’s actually worth learning?

More knowledge won’t do it. The machine wins that race before you’ve laced your shoes.

Two things, I think, and neither of them is new, they’ve just become the whole job rather than the senior part of it.

The first is deciding what’s worth deciding. Framing the real question out of a mess of people, politics, money and history, before anyone produces a single artefact. The machine answers questions beautifully. It has no idea which question matters, and it never carries the cost of getting that wrong.

The second is signal literacy, in both directions. Reading which signals are still real when the cheap ones are everywhere, and being able to produce one that holds. In a market drowning in fluent, confident, average output, the scarce skill is telling the lemon from the car, and not becoming a lemon yourself.

The years didn’t buy me knowledge the machine can now match in seconds. They bought me the taste to know which ten minutes of a problem actually matter, and the scars to prove I once paid for getting it wrong.

The machine has all my knowledge and none of my consequences.

That’s the gap. I’m not worried about it closing.

I watched the internet arrive. It landed on my desk as a working problem long before it was a headline, and it rearranged how the work got done while we were still looking the other way. One year we were faxing change requests and walking floppy disks between buildings, and the next we were arguing about whether email counted as a real record, whether you could trust a document that nobody had signed. Nobody stood up and announced it. It seeped in, and by the time we noticed, the old way of working had already left the building.

Twenty something years later I am watching the same thing happen again, except the word on everyone’s lips is AI instead of internet, and the people panicking and the people cheerleading are roughly the same shape they were last time, just younger and louder and faster to post about it.

So let me say the thing this whole piece stands on. The business cycle moves in circles. We keep drawing it as a straight line, an arrow that only ever points forward, and the drawing has been wrong the whole time. Things come back around, dressed in different clothes, carrying a new acronym, and we treat each turn as if it has never happened before in the history of the world. It has. We just forgot, because in between the turns we adjusted, we adapted, we made the strange thing normal, and then somewhere along the way we mistook that normal for permanent.

When I was made redundant at 57, that was a cycle too, though it did not feel like one at the time. It felt like an ending. A career of 30 plus years in technology leadership, a last role running platform engineering with hundreds of people reporting up through me, and then redundancy, and then a very quiet Monday morning with nowhere I had to be. I have since stopped calling it retirement. I call it portfolio life, which is partly a nicer word and partly the truth, because the world did not stop turning when my old role disappeared. It just turned, the way it always does, and left me to work out where I now stood on the wheel.

That is the lens I want to put on the present moment. Not prediction. I am useless at prediction, and so is almost everyone selling it. The lens is recognition.

The same river, a different season

In 1995 Peter Drucker published a book called Managing in a Time of Great Change. Read the title again, slowly. A time of great change. He was writing about the internet, globalisation, the early tremors of the information economy, the sense that the ground under management had shifted and the old maps no longer matched the territory. People read it then the way people read the AI commentary now, as if someone had finally found words for a feeling they could not name.

Here is what struck me when I went back to it. Almost nothing in that book was actually new in 1995. Drucker was not inventing fresh wisdom for a fresh crisis. He was reminding people of things they already knew and had stopped doing, because the long stretch of relative calm before the storm had let them get comfortable, and comfort has a way of erasing the lessons of the last storm. His advice reads as evergreen because it is. It works in 1995, it works in 2008, it works now, and it will work in whatever year the next great change arrives wearing whatever the next acronym turns out to be.

The reason it feels new each time is simple, and a little embarrassing once you see it. We adapt. We are good at adapting, it is one of the better things about us. But adaptation has a cost. Once the new technology becomes ordinary, once the new process is just how we do things now, we file away the discomfort that taught us anything, and we go back to managing the present as if the present were the permanent state of affairs. Then the wheel turns, the norm we adjusted to stops being the norm, and we stand there blinking, certain that nobody has ever been this confused before.

What actually does not change

The technology changes. The application of it spurs new processes, new business models, new centres of power. The internet gave us e-commerce and remote work and the slow hollowing of the high street. AI is busy doing its own version of that right now, and the specifics will surprise us, they always do.

But underneath the changing technology, human behaviour sits more or less still. The same fears, the same herd instincts, the same rush to either panic or worship whatever is new. The same handful of people quietly getting on with the work while everyone else argues about whether the sky is falling or ascending.

There is an old line I keep coming back to. There are three types of people in this world. Those who wait for things to happen. Those who do not even know that anything is happening. And those who make things happen. The technology of the day does not change those three categories one bit. The internet did not abolish the people who waited, and AI will not either. All a great change does is sort people more sharply into the bucket they were already heading toward.

So the question worth asking when the world is changing is not “what will happen next.” Nobody knows, and the confident ones are usually selling something. The better question is “which of the three am I being right now,” and that one you can actually answer, because it is about you, and you are the one variable you have some say over.

Drucker’s list, and why we forget it

If I strip Drucker’s advice back to its bones, the most useful piece is the first one, and the rest hang off it. Stop trying to predict the future, and start understanding the impact of what has already happened. The future is fog. But the change that has already landed is sitting right in front of you, fully formed, waiting to be read. The internet had already happened by the time most companies got serious about it. AI has already happened. The work is to metabolise what is already true and act from there, which turns out to be a different muscle than forecasting, and a much more reliable one.

The rest of his list is the same sort of unglamorous, durable common sense. Know what you are genuinely good at, and protect it, because a changing market punishes the company that forgets its own strengths and goes chasing someone else’s. Pay attention to the people who are not your customers, because the reason they stay away usually tells you more about where the world is going than the praise of the people who already love you. Treat information as the raw material it has become. Watch how new industries quietly redraw the map of who holds the power, the way the tech firms did in the 90s and the way the AI firms are doing right now, while the rest of us are still looking at where the power used to be.

None of that is clever. That is exactly the point. It is just true, and it stays true across every turn of the cycle. We do not fail to follow it because it is hard to understand. A twelve year old could understand it. We fail to follow it because between the storms we got comfortable, we adjusted to a norm, and we forgot that the norm had an expiry date stamped on it the whole time.

Standing on the wheel

I do not think AI is a revolution in the breathless sense the headlines want it to be. I think it is a bicycle. It can carry you a great deal further than your own legs, much faster, and it will not get you anywhere at all if you do not know how to ride it, and it will happily tip you into a ditch if you treat it like a horse that thinks for itself. The real question was always whether you would get on and pedal, and in which direction, and that question is as old as every tool humans have ever picked up.

So when people ask me how to make sense of a world that is changing, I do not give them a forecast. I tell them to look down at the wheel they are standing on and notice that it has been here before. Read the change that has already happened. Hold on to what you are actually good at. Watch the people drifting away from you more closely than the ones cheering. And decide, honestly, which of the three types of person you are going to be this time around, because the wheel does not care, it turns either way.

I have ridden two of these turns now, the internet one and this one, and I lost a career between them and found a quieter, better-fitting life on the far side. The technology was different each time. I was not. And that, more than any prediction, is the thing that has made sense to me.

It’s worked for me.

Energy sits at the base of everything. When global oil flows get disrupted, that disruption doesn’t stay in the energy sector, it travels through the whole system: into transportation costs, which feed into food prices at the supermarket, into manufacturing, which moves through supply chains before landing on retail shelves, into LNG, which New Zealand imports. Once energy rises, everything rises. That’s just how the mechanism works.

I’ve been watching three macro scenarios for the New Zealand economy: stagflation, inflation, and deflation. They’re easy to conflate, and the confusion matters because the responses to each are different, sometimes opposite.

Let me explain what each one actually means, because the definitions get muddled in most coverage.

Inflation is when economic growth is rising, prices are rising, and unemployment is falling. You’re paying more, but you also have a job and likely a pay rise. Uncomfortable, but manageable if you’re positioned for it.

Deflation is when economic growth slows, prices fall, and unemployment rises. Prices going down sounds good until you realise the economy is contracting and jobs are disappearing.

Stagflation is the ugly one. Economic growth slows, inflation stays elevated, and unemployment rises. You get the worst of both: a contracting economy that still costs more to live in. There’s no relief valve. Slow economy, high costs, rising unemployment, all at the same time.

That’s the scenario worth watching right now.

Why New Zealand is unusually exposed

Small open economies don’t carry many buffers, and New Zealand is about as open and as small as it gets among developed countries.

We import energy, capital, and most manufactured goods. Our household wealth is disproportionately tied up in housing. Domestic consumption drives a large share of GDP. That combination creates a specific structural vulnerability: even when domestic demand weakens, imported inflation can stay sticky. Costs keep rising even as the economy contracts.

A global oil disruption that barely registers for a large, diversified economy with domestic energy production lands differently here, because there’s less in the system to absorb the shock. It passes through more directly into the price of petrol, which passes into groceries, which passes into the cost of running a household.

New Zealand’s dairy export earnings are also worth watching. Dairy is a primary current account earner. A 15% year-on-year drop in dairy prices hits the exchange rate, which hits the cost of everything we import, which hits household budgets. These things connect. Pull one and you feel the others move.

The NZD/USD rate matters for the same reason. A weak kiwi dollar isn’t just a problem if you’re travelling. It makes every imported good more expensive, and in a country as import-dependent as ours, that’s most goods.

The 1970s rhyme

History doesn’t repeat exactly, but it rhymes often enough to be worth paying attention to.

Structurally, the world today is beginning to resemble the 1970s in ways that haven’t been true for 50 years: fragmented geopolitics, high commodity sensitivity, governments running large deficits while managing inflation, weakening productivity growth across developed economies. The exact causes are different. The shape is similar.

The 1970s stagflation hit savers hard. Fixed income returns didn’t keep pace with inflation. Equities stagnated in real terms for extended periods. The people who preserved purchasing power were the ones who understood what was happening at the structural level, not the ones reacting to daily news cycles.

We may be entering a similar period. I’m not saying it’s identical. I’m saying it’s rhyming, and that’s usually enough to be useful.

Financial repression, quietly

This is the part most commentary skips.

Governments carrying excessive debt have a limited menu of options. They can default outright, but advanced economies almost never do this, because central banks can print money to service the debt. They can cut spending dramatically, which is politically very difficult to sustain. Or they can do what actually tends to happen: allow real returns to go negative over a long period, quietly.

Inflation erodes the real value of the debt. Currency debasement helps. Keeping interest rates below inflation suppresses borrowing costs for the government. Taxation takes its share of any nominal gains. None of it gets announced as policy. It happens through the cumulative interaction of those levers, and it slowly transfers wealth from savers to debtors, with governments being the largest debtor in the system.

This happened after World War II. It happened through major debt crises across the 20th century. It’s a well-documented mechanism, and it tends to be the path of least political resistance when the debt load gets large enough. The process is gradual, which is exactly why most people don’t notice it until years later when they try to understand why their savings feel like they’ve shrunk.

I think we may be entering another version of that environment.

The maths that should concern you

Here’s where it gets concrete.

At current settings (April 2026) :

OCR at 2.25%. CPI at 3.1%. Assuming a 30% tax rate on interest income, which applies to most working New Zealanders.

Work through it:

2.25% interest earned on savings. Minus 30% tax leaves you about 1.58% net. Against 3.1% inflation.

You’re losing roughly 1.5% purchasing power every year in a “safe” cash position. Before currency risk. Before fees. Before any consideration of whether the official CPI figure accurately captures your actual household cost of living, which for most people it tends to understate, because the basket of goods they measure doesn’t weight housing and food costs the way most household budgets actually do.

The money is still there, nominally. But it’s buying less every year. That’s not a market crash. It’s a slow drain.

In a stagflationary environment, where both inflation and currency pressures can worsen simultaneously, the erosion accelerates. The nominal number in your savings account can go up while the real number, what it actually buys, goes down. That gap is the trap.

The instinct many people have at this point is to reach for higher yield, take on more risk to generate more return. That impulse is understandable, and it can also be exactly wrong if it leads you toward credit and duration risk you don’t fully understand. Higher-yield instruments aren’t free returns. The risk is real, and it tends to surface precisely in the kind of environment we’re discussing.

Most of the mental models people use for financial safety were built in a different era, when real interest rates were positive, global trade was expanding, and geopolitics was relatively stable. Those conditions no longer reliably apply.

What I’m actually watching

Avoid most of the media coverage on this. Most of it is calibrated for engagement, not for helping you think clearly.

The indicators worth tracking: the Global Shipping Cost Index, because it moves before consumer inflation does, it’s a leading indicator; NZD/USD, because our currency exposure runs directly through household costs; US Core PCE, because American monetary conditions still set the global tone, and if the Fed stays tight, the RBNZ has limited room to cut without making our currency situation worse; and dairy prices year-on-year, because that’s where NZ-specific external shock shows up first.

But honestly, the most useful thing to track is your own household cash flow. What your income actually buys, month to month. That’s where the real economy shows up, usually well before the official numbers catch up.

If what you buy every week is noticeably more expensive than 12 months ago, and your savings return isn’t keeping up, you’re already inside the scenario I’m describing. You don’t need a macro framework to confirm what you’re already feeling in your grocery bill.

One closing thought

In a stagflation and geopolitical shock environment, real purchasing power erodes fast, even when the savings balance looks fine on paper.

I’m not calling a specific outcome. I’m watching the indicators, understanding the structural conditions, and thinking about what they mean for my own financial position.

This is not financial advice. It’s what I’m doing.

The world looks more like the 1970s than it has in 50 years. That’s probably worth more than a passing thought.

For the past 2 years, I’ve been tracking New Zealand’s unemployment rate using 3-quarter and 8-quarter moving averages. Nothing sophisticated, just adding the last few quarters together to smooth out the noise and watch the direction of travel over time.

I started doing this because headlines tend to swing emotionally from quarter to quarter, optimism one month, panic the next, while labour markets usually move much slower than people realise.

The March 2026 unemployment rate came in at 5.3%.

From my observation, the situation hasn’t changed very much. The curve has flattened slightly, but the broader trend still looks like a slow unwind rather than a recovery.

And I think many people can already feel this without looking at the charts.

You see it in the number of applicants chasing a single role. You hear it in conversations around restructures, delayed hiring, shrinking budgets, and experienced people applying for jobs well below where they were 2 years ago. Even recruiters, who were once overwhelmed by shortages, now sound more cautious.

This cycle feels different because it never arrived like a crisis.

No dramatic collapse.
No single shock event.
Just a long gradual tightening.

Those are often the harder cycles to navigate because people keep waiting for things to “go back to normal”, while the system underneath has already changed.

New Zealand is also tied closely to what happens elsewhere. We are a small open economy, whether we like it or not, and the broader Western economies have all been wrestling with similar pressures: weaker growth, high living costs, cautious investment, geopolitical tension, and businesses delaying decisions longer than usual.

The patterns rhyme.

One thing I’ve noticed is that activity can return before confidence does. Job ads might increase, projects restart, conversations pick up again, but employers still hesitate at the final step. Hiring becomes selective. Expansion gets delayed. Permanent roles quietly become contracts. Everyone waits for someone else to move first.

That creates a strange disconnect where the data says conditions are “improving”, while lived experience still feels tight.

Both are true.

I also think remote work changed the labour market more than many expected. A role that once attracted applicants from one city now attracts applicants from everywhere. Geography matters differently now. Competition widened quietly while most people were still arguing about working from home policies.

And underneath all this sits a bigger structural question.

What does stable employment even look like going forward?

I’m not sure the old assumptions fully hold anymore, especially for younger workers entering the market now. Career ladders look less linear, tenure matters less than adaptability, and many people are stitching together portfolio careers, contract work, side income, and remote opportunities across borders.

The system is changing while people are still using the old mental models to interpret it.

So personally, I’ve spent less time trying to predict the exact turning point and more time thinking about resilience, cashflow, adaptability, health, keeping skills current, maintaining relationships, and staying mentally steady while the cycle runs its course.

Economic cycles come and go. They always have.

But slow cycles test people differently. They wear people down gradually because the signals stay mixed for a long time.

I guess that’s what I’ve been observing these past 2 years.

Not collapse.
Not recovery either.

Just a long adjustment phase that still hasn’t fully played out.

It’s worked for me.