Over the past year or so I have been contacted by a whole bunch of parents, career counsellors and teachers who wanted to know what they should be telling their kids about preparing for an AI-affected workforce. The question sounds simple: what should my kid do now?

They were writing because at the end of last year I wrote a series on whether a university degree was still a valuable investment for a kid preparing for the modern workforce and later this piece where University of Sydney Business School’s excellent Prof Clinton Free crunched the numbers on Anthropic’s most dire workforce predictions, which went kind of viral.

But despite all the research I have done, all the very smart people I have spoken to, I didn’t have a great easy answer for any of them.

Because, uncomfortably, I have come to believe the question is too small and assumes that the answer is a tweak, rather than an overhaul. I’d suggest the real question we need to be asking is: what is the point of school at all?

So you can see, I’ve been mulling this question for a long time, but it resurfaced this week as I scrolled through all the posts on social media responding to a new preprint published by researchers out of Stockholm University and the University of Hong Kong in June.

In case you missed it, the study looked at the learning outcomes of more than 26,000 Chinese secondary school students over nearly three years, trying to ascertain how AI use was impacting their grades.

It found AI-use lifted homework scores an average of 18 per cent, assignments were completed faster, but closed-book exam results fell 20 per cent. Basically, the kids could do their homework faster and better, but they didn’t retain the information like they would have before AI. They didn’t really learn it as well.

And that has thrown another log on the fire of moral panic around children and AI, whether they’re using it to cheat on their school work and what it means if they are.

In Australia this week, the ABC reported on a range of different approaches under development by the country’s various state governments. New South Wales is considering doing away with take-home assignments for senior students entirely while South Australia has tried to wrap guardrails around AI-use by developing its own learning assistant chatbot. Both responses make sense on their own terms. Schools do have to deal with assessment integrity. But they also show the limit of the current debate: we are asking how to preserve the existing model of schooling under AI, not whether that model still makes sense in the first place.

More right answers, more wrong people

That study made me think about the February paper out of Wharton, proposing the idea of “system 3 cognition” – this paper is seared in my mind, it made me laugh so hard.

In it, the authors seek to build on the cognition model made famous by Nobel Prize-winning behavioural psychologist Daniel Kahneman, in which system 1 thinking is essentially defined as intuition, and system 2 as deliberation. I like to think of it as system 1, animal brain; system 2, higher-order reason.

Anyway, in the Wharton paper, Drs Steven Shaw and Gideon Nave suggest “system 3 cognition” could be defined as “artificial cognition that operates outside the brain”.

They found that people who used accurate AI could massively improve their accuracy when completing a task, but when users consulted an inaccurate AI, they let the AI’s bad info override their own judgement more than 73 per cent of the time – the paper calls that “cognitive surrender” – and in those cases they under-performed what they would have achieved without any AI at all.

They also found time-pressure made cognitive surrender more likely.

What gave me the giggles was the idea of “artificial cognition that operates out of the brain”. Because to me that is just … not doing the work? And that is not some special AI thing, that’s just a thinking thing. If I have an assignment and I get my roommate to do it for me, that is also cognition that is operating outside my brain. No tech required!

Basically, “system 3 cognition” is just a fancy sciencey-sounding word for not doing the work yourself. You wouldn’t believe the things I don’t know because I never thought about or learned them! (Hahahahaha ok, glad I got that out of my system.)

But cognitive surrender is real, and it echoes in what the Chinese study found. Those kids weren’t taking bad answers off a bad machine, they were taking good answers off a good one. In both the Wharton and Chinese student studies, when the AI was “good” the work came out better and the person didn’t.

The issue is not simply that AI helped students produce better answers. The teachers already knew the answers. The repositories of human knowledge already held the answers. We did not need a 13-year-old to invent the answer. We wanted the 13-year-old to prove they had learned it, because them knowing the answer ... did what?

The pedagogical answer is that knowing was supposed to change what the child could do or learn next. The point of practice, testing and repetition was never merely to extract the answer, it was to build recall, fluency and usable understanding, which is why research on retrieval practice and effective learning techniques treats practice and testing as part of learning, not just measurement. If AI can produce the answer without the child doing that internal brain work, the grade may still signal success, even though the education may not have happened at all.

We realise the accuracy of the answers was never the thing we cared about. (I’d argue this is true regarding a lot of knowledge work being threatened right now as well.)

And again, it’s an issue highlighted as we grapple with AI, not a new problem created by AI. We routinely see people get so caught up in the AI of it all they fail to see the ways that this technology is just exacerbating preexisting structural issues and uncovering fundamental flaws in systems that long predate AI.

I used to remember the phone numbers of every one of my friends off the top of my head. I could not tell you my own work number these days without checking my email signature. We have been doing this for years.

And I’m not even sure that it’s wrong.

Your kid is right: times tables suck

If you have a kid in school I bet you have (or will) field some version of this question:

Why do I need to learn my times tables? I have a calculator on my phone.

And you will probably say something like “it’s useful” or “it will make you faster at other math later”. But even though I have trotted this explanation out myself, I know it’s pretty weak. We are always adding and adding to school curricula and never taking anything away. And the exact utility of some of the things we keep making kids learn seems hard to pin down.

Because your kid is right. They do have a calculator in their pocket at all times now. Surely learning to use it well and accurately would be the better use of time and resources than memorising their 7 times table? (I still hate the 7s, I’m sure you understand.)

The difference between remembering your BFFs digits and preserving your critical thinking might seem clear – recall and judgement are separate categories. The difference between remembering that phone number and your times tables might be less. We don’t have a good sense of what things are ok to outsource and what might have a long-term negative impact on your cognitive abilities.

The question shouldn’t be whether we let kids offload, it’s what we let them offload and why. And to answer that question we need to go back to basics and work out the purpose of school.

Job ready for what?

Is it to retain facts, become “job ready” as Australian education policy explicitly states? Or is it to become a thoughtful well-rounded person?

We have spent so many years trying to compete with international math and literacy test scores like some intellectual arms race, but why? In a world where a lot of the formulaic and calculative work will probably be done by a machine, is knowing the important thing?

The last few years all I hear is “judgement and critical thinking are what we care about” but how is our current education system geared towards developing those skills? It is not.

The current schooling system still carries the imprint of the industrial revolution, with its emphasis on time, sequencing, compliance, standardisation and measurable output.

If even the university system now says education needs to develop “evaluative judgement, critical thinking and ethical reasoning”, is tinkering at the edges of a system never designed to deliver such nebulous, individualised outcomes the right call?

And some people are trying to ask these more fundamental questions. Sydney University’s Policy Lab recently published a program called A Good Childhood, which argues for the re-centring of a child’s interests. “Australia needs to think differently about childhood,” program literature states, “not simply as a stage of preparation for adulthood”.

Its starting point is that policy should ask what children need to thrive, and how institutions can support their wellbeing, belonging and opportunity. That is a very different starting point from the Pavlovian response mechanism of the current school system: bell rings, stop; bell rings, move; exam time, regurgitate.

“Too often children’s interests are an afterthought in policymaking,” said lead researcher Prof Luara Ferracioli. “And this is one reason many Australian children are not reaching their potential or fully enjoying their childhoods”. Oof.

That is a very different starting point from asking how to preserve the integrity of homework.

Fellow substacker The Pavement Philosopher is a high school teacher at a selective school in Perth. (For non-Aussies, that’s a government-funded school for high-achieving kids.) He’d rather I not share which school or his real name, because he’s not sure how his employer would feel about him talking openly about this stuff, so I’ll refer to him as Phil.

When I asked him how his students were thinking about AI, he stressed his school was “not a fair representation of all teenagers”. But he says the “freakishly smart and gifted” students were often “the most suspicious of AI, least likely to use it and most likely to call out teachers who are obviously using it too much”.

I would argue these are the kids who were always going to have the best natural aptitude for critical thinking and judgement. They exhibit this type of thinking despite our education system, not because of it. We’re not currently focused on teaching this stuff to the typical student body and the current school day is not built to do so.

Children are, famously, non-fungible

The other dynamic Phil sees on the regular is an extreme level of parental investment. Some students, he says, have parents who spent “A LOT of money” (his emphasis, not mine) on tutoring to get them into the school in the first place.

When I asked whether AI had broken the bargain those parents thought they were buying into – i.e. best school = best uni = best job = securest future – he pushed back. “I don’t know if AI has really made that bargain anymore broken than it was, mostly because it was so broken to begin with.

“To put it in Kantian terms, these are parents just using their kids as means-to-an-end – of prestige, wealth, whatever – rather than respecting their autonomy as human beings.”

He reckons some parents see their children more like “investments” than people, “which, to me, is already as broken as it gets”.

Look, that is a rough thing to read; I really hope that’s the exception rather than the rule. I think a lot of parents do the same things because they are stressed out, do love their kids and really want them to have a stable future. And they just don’t know what else to do.

And that is where I think this all lands. AI has not made school meaningless. It is forcing us to ask the thorny question of what meaning we were projecting onto it in the first place.

In developed economies where the factory worker has all but disappeared, instead of re-engineering our school system to fit the demands of a services-based workforce, we have just iterated and iterated on the factory-era school formula into absurdity.

Critical thinking and judgement are not a gap you close by bolting another module onto the existing, overladen curriculum. Teaching them to a typical student body would mean inventing new ways of doing it, explicitly, and building schools, maybe from scratch, around that fundamental goal.

None of that is going to happen overnight.

What do you do?

So when parents ask me what they should tell their kids to do now, I deeply understand the instinct. They want an answer that sounds like a plan: take computer science, become a doctor, choose the safe degree, here’s how to become impossible to replace.

All we want is certainty that our kids will be ok.

But if I am being honest, I don’t believe there is a simple, playbook answer anymore.

My honest answer is much more abstract and harder to tie up in a neat easy response. But, here, I will try:

Argue with them and teach them how to argue back to improve their cognitive flexibility, to understand persuasion and judgement. Show them how to care about stuff. Urge them to make things. Get to know them and help them to know themselves. Encourage them to pursue their real interests. Most importantly, teach them to value themselves as human beings rather than as a worker.

When it comes down to it, no work is truly “safe” from automation. I think the people who are going to fare best in the decades to come are the ones who believe, deep down, that they are more than a job. Even as jobs come and go, your identity can remain stable.

Which, I know, is a much more radical and uncomfortable answer than “just learn to code”.

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Any shrink worth their SSRIs will tell you competence—feeling like you have the tools needed to perform the tasks in front of you—has long been understood as a basic psychological need. Feeling like we know what to do, and can do it well, is a really important component of feeling safe and helping us to self-regulate. It’s calming. People really like it.

So I’ve been thinking a lot about how perpetual upskilling has been billed as the “new normal” in most knowledge work over the last couple of years. It seems like that instinct is correct—I don’t think the pace of change is really going to slow down in this era—but the impact of that change, and the potential inequities and overall misery it is introducing, are not really being grappled with much at all.

A moving (maybe lucrative?) target

Jobs and Skills Australia (JSA) did have a go at one piece of the puzzle. In a report published last year they looked at the correlation between years of experience, skill change and salaries. That analysis found that jobs with a lot of skill change tended to also have more salary growth potential over a lot longer period than jobs where the skillset roughly stays the same.

That makes a kind of sense, if you think about it.

If my job was something I learn once and then practice for years in the same way to become an expert at, my pay would hit its peak earlier—basically as soon as I reached that expert level. But if my job required constant regular upskilling, well, that’s a moving ceiling. (I’d argue it would have also meant a bigger variation of the skills acquired across my peer group, depending on who’s kept up and who hasn’t.)

And, typically, the JSA data shows, keeping up with those moving skills has been compensated. Jobs with a moving skill target take around 20 years of experience to top out.

It has also meant a relentless race to stay “competent”. Constant upskilling shakes your feeling of competence, because the thing you build that feeling on stops being what you know and becomes how fast you can learn. And that race has already, historically, disadvantaged people who took any amount of time out of the workforce—say, to have a baby, for example.

Stale skills might be a myth, the damage is real

We know the motherhood penalty is real. Treasury researchers who analysed government data in 2023 found a 55 per cent reduction in average earnings of mothers over the first five years following the birth of a baby. Analysis attributes that mainly to reduced working hours, rather than lower hourly wages.

And skill depreciation from time out of the workforce is also real. Although how real and what kinds of impacts it has is kind of hard to nail down.

But ask any professional woman who has taken even six months off for baby care. Plenty of them are openly worried their skills have gone stale. I’ll be honest here, I read many very dry, difficult papers trying to establish whether this was actually true or not. I still don’t know.

The data seems to show some skill depreciation if you take a year off work, but that you earn it back and then some within a year of coming back. But other studies show the actual skill loss is very minimal, so when a woman expresses concern about this, we’re going off vibes here. Which is its own competence problem: not knowing whether you’re still good at your job does the damage whether or not your skills actually went anywhere.

And actually, that’s kind of the point.

What’s the grading curve?

What studies do show us is that women are punished more harshly for misconduct, judged more for mistakes—and, if one UK experiment is right, for using AI, for that matter. Plus they know it, and they factor that into how enthusiastically they are approaching AI upskilling.

So competence isn’t only something you just feel internally, it’s something other people decide you have—or don’t—and that probably shapes your prospects even more than your own sense of ability.

This paper from PNAS Nexus found women are more skeptical about the alleged benefits of AI at work—partly because they’re more exposed to the jobs it’s coming for. And when the researchers told people AI was likely to destroy more jobs than it created, women’s support fell away faster than men’s. (This is actually just a really great paper all round, five stars, would recommend.)

That’s not to say they use it less. A recent Roy Morgan report found the gender split on AI-use is now about equal. The dudes just trust it more, which, as a woman, I think is stupid. But then I would. Because, again, woman.

Fast-growing … for who?

That skepticism may go some way to explain the widening gender gap in fast-growing AI careers. New research published yesterday by LinkedIn showed women hold 49.7 per cent of non-AI jobs at non-AI companies in Australia, but only 27.6 per cent of AI jobs inside AI firms. In leadership it goes from 28.1 per cent at non-AI companies to 15 per cent at AI ones.

Basically, women are equal participants in the general workforce, not so much in AI jobs, which, LinkedIn points out are “some of the fastest-growing and highest-value opportunities in the labour market.”

When I asked LinkedIn’s head of public policy for ANZ Audrey Lobo-Pulo why exactly they think this is happening, she told me there wasn’t a single explanation but more a combination of factors at play: “Growth is concentrated in research and technical engineering roles, which have historically been male-dominated,” she said. “At the same time, promotion pathways often continue to reward linear and uninterrupted career progression, while women are more likely to experience career breaks.”

And there we have it: our uninterrupted career rearing its head again. “The question,” Lobo-Pulo went on to explain, “is how can employers, educators and workforce systems help ensure more people have opportunities to participate and progress?”

And I mean, great question. Hard to answer, though, when nobody has properly counted what employers spend on training their staff since 2002. That was the subject of a report this past week by JSA, arguing the Australian Bureau of Statistics really ought to start keeping track again.

According to this report Australian employers spent around AU$2.90 per $100 of wages paid on training staff in 1993. By 2002, the last year we captured that figure, it was down to AU$1.30 per $100. In 2025, JSA estimates the spend was probably more like AU$1.10. Just for comparison, they estimate that number was more like £3.80 per £100 in the UK in 2024.

“Employers play a major role in helping workers build, apply and transfer their skills throughout their careers. Workers increasingly need to update and deepen their skills as technological, demographic and structural changes reshape Australia’s labour market dynamics,” said acting JSA commissioner, Megan Lilly when announcing the report. Separately, JSA’s modelling has also shown women were “marginally more exposed” to jobs likely to be impacted by AI augmentation and automation.

So, women aren’t getting into AI jobs at the same rates as the blokes. They’re more skittish about AI and its impacts. And their current jobs are more vulnerable to AI replacement or disruption.

Bro, do you even lift?

Everyone seems to agree training and upskilling matter a lot—and not just in the jobs where keeping up was always part of the deal. It’s spreading, as more and more kinds of work get reshaped by AI. But we’re not really sure what people are being trained in, the quality or transferability of that training, who’s paying for it or when and where it is occurring.

Government stats from April this year suggest rates of formal work-related training are actually falling. But a report from the employer lobby Ai Group last November found participation in training was “above the long-term average”, claiming 86 per cent of it was being funded on the company dime, and around 9 in 10 people were doing it inside their paid work time. (Though about 30 per cent were also doing some of it in their own time—a figure that hasn’t budged since 2007.)

Both could be true. Perhaps people are getting more training on the job so they’re enrolling in fewer programs themselves. We don’t actually know.

What we do know, at least according to researchers at Anthropic, is that the current programs being run are not super effective at boosting outcomes for employees. Their review of the evidence, published last week, found small impacts overall—and that the large-scale programs (the ones run at the size you’d actually need) have been “lackluster” (their word, not mine). They believe this strategy would “likely fall short” against AI-scale displacement.

And I’m going to suggest that brings us back to the therapist’s couch.

What is this? A school for ANTS?! You can’t make someone to learn good.

Ryan and Deci described competence as a basic psychological need, sure. But in that same paper they were also pretty blunt about what happens when learning is imposed instead of chosen. Threats, deadlines, directives, pressured evaluations, imposed goals: all of them reliably flatten motivation.

People taught that way don’t just lose enthusiasm, they learn less well. Being forced to train or die on the proverbial office vine is the worst possible setup. And the learning that suffers most is the conceptual, creative kind—which is precisely what a fast-changing human-skills-first job apparently demands.

If more jobs are moving into the upskill forever camp—and that’s going to be thrust on you whether you like it or not—then we’re staring into a possible future of generally lower feelings of competence, and perpetual forced training which is almost guaranteed not to produce the best learning. That sounds like misery to me.

The people who still get to feel competent will be the ones who can move that feeling off what they know and onto how fast they can learn. Which is a bigger change than you might think. Of course, if you already wake up every morning thinking, “Gee, I’m so good at learning new stuff!”—well, lucky you.

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I’ve been thinking about that this week in the wake of the Nara Smith controversy, of all things.

In case you’re not spending your limited attention span on influencer culture the TLDR is Smith—one of the most high-profile so-called “tradwives”—went on the very popular Call Her Daddy podcast and acted all surprised and offended that people had ever tarred her with the tradwife moniker.

Much internet scoffing ensued. Smith has spent many years perfecting her ASMR soft-spoken housewife brand, making Froot Loops from scratch in a full face of makeup and cutesy apron in her perfect kitchen.

Now, you might be asking yourself, “OK, Rach, but what does TradWife™ have to do with freaking AI girlfriends?”

So glad you asked. They both speak explicitly to a growing fetishisation of care.

The illusion of effortless care

A 2025 report by researchers at the Global Institute for Women’s Leadership at King’s College London (KCL) found that lots of women—I’m talking 79 per cent in the test group—said they were attracted to the fantasy of “the calm, relaxed lifestyles” of trads; 62 per cent were captivated by their depictions of luxury.

It’s striking that a life devoted to endless service and chores is being consumed as “relaxation”—so long as that intense labour is done aesthetically, surrounded by the latest high-end appliances.

And the KCL study found the way young women felt about trad was nothing about getting retrograde or some conservative gender fantasy. Only 7 per cent of those same respondents thought blokes making all the decisions sounded hot. About a third liked the idea of a man being the main breadwinner and 44 per cent found “the idea of not worrying about a career appealing”.

That shifting attitude about work is a growing trend with dudes too. The research found 20 per cent of women and 21 per cent of men aged 18-34 perceived paid work as having a negative impact on their mental health—up from under 10 per cent who felt that way before 2020.

“Rather than a nostalgia around the role of women being limited to household labour,” the authors suggest, “the popularity of the tradwife phenomenon may be a symptom of the systemic strain experienced by the younger population, especially parents, who are juggling increasingly demanding jobs with intensive parenting responsibilities.”

It would seem the thing they’re really coveting is the appearance of abundance: abundant finances, skill, attention and, most importantly, time. Time is the greatest modern luxury.

And crucially, the appearance of choice in the matter.

But the trouble with selling women a fantasy of not working is that eventually they notice it is work—and that you’re at work while you sell it.

When tradwife is exposed as work

I spoke to Lauren Meisner this week, founder of the Gen Z media network Centennial World and host of the Infinite Scroll podcast.

She says young women are really waking up to the “grift” inherent in portrayals of the tradwife life. They know it’s not real and that women like Smith are literally working when they make this content.

“[Tradwives’] comments are flooded with criticism and mockery,” she observes. “I think a lot of young people have started hate-consuming trad content because they feel like leaving a comment telling a woman she is setting us back feels like a way to take out their anger and take back some control.”

Basically, it started out as a fantasy of peace, luxury and choice, but has become yet another high bar that women feel they are failing to reach on the daily. “I think a lot of women are like nevermind, let’s actually just go back to being sugar babies,” laughs Meisner (darkly).

The spotless floor problem

And this intensification of expectations is where the AI companion piece comes in, but also a broader allegory about how technology has, historically, raised expectations in all aspects of life. Rarely is new tech used to provide genuine relief from labour.

In 1983 historian Ruth Schwartz Cowan published a seminal study on the impact of new technology on household labour called More Work for Mother. (I looked for it everywhere and I couldn’t find a publicly accessible version, so I will instead link this related article Cowan wrote for American Heritage magazine in ‘87. It gives you the broad strokes, but if you like a narrative history I highly recommend you hunt down a copy of the book—it’s a fun read.)

Cowan’s research argues the introduction of whitegoods like vacuum cleaners and microwave ovens, which were marketed at the time as providing relief from chores, in fact just led to a new higher standard of expected domestic perfection. This is classic lifestyle creep—a ratcheting expectation as to what constitutes a “good life”.

I bought a robot vacuum a few years ago. I’ve become addicted. I now find it intolerable that my floors are ever anything but perfectly spotless.

And it’s not confined to the domestic sphere by any means. We’ve been in a perpetual state of work intensification for a really long time. Do you think industrialisation reduced the intensity of labour? Lol. Of course not. (See evidence here.)

One 2021 analysis published in the journal Industrial and Labor Relations Review said the particular squeeze over the first two decades of the 21st century has been thanks to some combo of reduced worker power, the constant need to learn and keep up with new technology, and bosses using that technology to organise, monitor and squeeze more work into every day.

Lifestyle creep of the mind

The same thing is happening right now with AI.

In a report published by the consultancy firm BCG in June, more than two-thirds of workers said AI had taken over simpler tasks, leaving them with the more complex work. Your boss will crow about this and call it a great thing—outsource the boring busy work to the droids!—but it also means more of your day is being dedicated to the hardest tasks with no easy filler tasks as relief.

Add to that: at least half the respondents also said they were spending more time reviewing and correcting AI output or managing and directing AI; 41 per cent said AI had increased the time they spent making decisions.

In the AI era I’d argue we’re entering a phase of lifestyle creep of the mind.

That is, a change in the kinds of work we choose to do—or are encouraged or even permitted to do inside organisations that are incentivised by speed and volume rather than the pleasantness of a worker’s day or the health of his individual intellect.

A lot of us are pretty worried about the potential erosion of our critical thinking and judgement if we become too dependent on AI.

When human contact becomes a luxury

It is also true of romantic and social relationships that can be in some fashion now supplemented or replaced whole cloth by an AI companion who never gets in a mood or disagrees or says no.

Which brings me back to where I started.

A team out of Oxford and Stanford published a preprint this year on a bunch of studies they ran getting thousands of participants to use an AI for emotional support.

They found, after three weeks hooked up to an intentionally agreeable AI, people were about as likely to rely on it as they were on their closest friends and family. Given a choice they picked the more sycophantic version of the bot too because they just liked the way it made them feel. Participants also reported being less satisfied with the real conversations in their lives.

That’s … not great.

It’s such a big potential problem that the Chinese government introduced a bunch of new rules regulating the availability and use of AI companions in July. Work AI assistants were specifically exempt. Read that how you like, but it looks to me like a government that’s fine with work intensification and not fine with us outsourcing our emotional needs. That’s a design choice.

So will AI give us back enough time and capacity to properly resource the parts of our lives that matter most? Or will so-called “efficiency gains” ratchet expectations to a new, higher standard of what a competent worker, good partner or functional human being is expected to be?

History suggests it will be the second.

I know I can’t go back to a dusty floor. We may not be able to go back to being listened to imperfectly by our fellow imperfect human loved ones either.

Maybe that is the real danger of abundance: once something becomes cheap and endlessly available, scarcity starts to feel like failure.

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On July 21, Substack went into a bit of a meltdown after the platform turned on AI detection software Pangram — much to the displeasure of many of the platform’s writers. Then on July 30, LinkedIn announced it was going to start rolling out a “seems like AI slop” button, and replaced its “enhance your post” AI assistant with a simple proofreader.

And of course all this week beloved science communicator Hank Green was kind of self-immolating after admitting he used ChatGPT to prep his episodes and suggesting his relationship to AI was “not healthy”.

You promised me prompts, Rachael. Where the hell are they?
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Why do we like things more when they're hard … for other people?

And look, for a while now the data has shown people really do care about this stuff. Actually that was true well before the recent advent of The Machine — and in far more ways than might be immediately apparent.

In this 2004 study, for example, researchers found people assessed an artistic product — a poem, painting or suit of armour — as higher quality, and even said they liked it more, depending on how much time and effort they thought it took to produce.

And when others do things the hard way, we consider that not only higher quality but morally superior. A paper published in the journal Scientific Reports in April showed test subjects thought negatively of people who were described as having lost weight using a GLP-1 compared with people who lost weight the old-fashioned way. Even weight loss is valued more highly and assessed as being of better moral quality if it is harder to achieve.

We like things to be hard — at least for other people.

For example, an article published in the Journal of Experimental Psychology: General in 2016 found that the level of effort involved in producing an outcome intensified how we felt about the action, good or bad. Essentially: if you mean well and put a lot of work into something, people think well of you, regardless of whether you were successful; if you have bad intentions, and put in a lot of work, people also think worse of you, regardless of outcome. Later researchers call this “effort moralisation”. And that “regardless of outcome” is an important distinction. The thing people valued wasn’t the quality of the result or output, it was the implied care inherent in the effort.

At the same time, studies consistently show that people — and other organisms — instinctively avoid hard work. In the literature it’s often called “Hull’s law of least work”: given a choice between similarly rewarding options, creatures learn to take the easier path.

Research finds you can, of course, incentivise effort expenditure in exchange for a reward, but even then people tend to make a subconscious calculation about how valuable the reward is and discount it against the level of anticipated effort. We really prefer not to do hard stuff if we can.

When we do do hard things, we later judge the outcome as more valuable. Not while we are doing it mind you, usually only after the fact. One version of this is the IKEA effect (really top-notch naming there, I bet those guys are fun): building the flatpack yourself means you come to feel the furniture is of higher value than a piece built by someone else.

And how people feel about how other people use AI is no different.

Why being honest about AI backfires

Siavosh Sahebi is a PhD candidate at Macquarie University and coauthor of this recent paper on AI and trust.

That research found a few things:

1. Both on social media and in work, people think stuff made with AI is “less trustworthy, less authentic, and less useful”; and

2. People say they wouldn’t judge AI use if it’s disclosed, but they are wrong.

“We asked participants whether they think that disclosing the use of AI is important, a significant portion said yes,” Sahebi tells me. But although the majority of participants said they would not penalise AI use if it was properly disclosed, “our findings show that they do, in fact, penalise AI use”

This presents a conundrum for AI disclosure, because we do want disclosure to take place. It’s super important for accountability and accuracy reasons. And as Sahebi points out, AI is really being shoved down our throats right now. It’s not like we can completely avoid it, even if we wanted to. So we need people to disclose their use without penalising that honest disclosure.

But exactly how we reach a reasonable level of transparency is still up for debate.

A pre-print published in January from researchers in the Netherlands ran into the disclosure dilemma Sahebi outlines hard. In a study of preferences around news a majority of readers said they would prefer a detailed explanation of how AI was used. But in reality, no disclosure or a one-liner had no impact on readership or subscriptions, whereas a detailed disclosure about AI use reduced trust in news and subs took a hit.

How accurate is Pangram's AI detection?

If you read the laments of writers on social media, you’ll see a lot of claims that Pangram is doing a lousy job. (I found Dr Sam Illingworth’s writing about it pretty persuasive.)

Meanwhile in June the journal International Journal for Educational Integrity published a paper that suggests unlike previous iterations of AI detection software, Pangram was pretty damn accurate. I will say I found this study a little methodologically challenged, given their control was 40 graduate articles written before 2019.

The issues with that, as I see it, are twofold:

First, LLMs were trained on the stylistic conventions of academic papers as well as social media and news writing — a lot of the alleged hallmarks of LLM use are things like the em dash, which, as a professional writer, you can pry out of my cold dead hands along with my millennial skinny jeans. News writing was not part of the control group. (That said, pre-review research by the American National Bureau of Economic Research released last year did test on pre-2020 news writing and found false positives were also near zero.)

And second, perhaps more importantly, we know that language is memetic.

Is AI changing the way humans communicate?

As people consume more and more AI-mediated language themselves, some AI-isms are likely to drift into their own writing. A pre-print published in July by a mix of European researchers found that “machines trained on human data now feed their own traits back into human language”.

They ran an experiment where they tried to see if an interaction with an AI chatbot could incept the participants with particular word choices. It did. Fast. Like we’re talking one short conversation. And people thought the new word choice was their own.

The paper flags the emergent risk that consistent, widespread AI use could lead to the homogenisation of language and culture over time, and the potential of a handful of AI companies to manipulate or influence thoughts and beliefs at scale.

But can it really change your mind?

We already know that LLMs are extremely persuasive; I like the term used by the University of Sydney Business School’s Doctors Sandra Peter and Kai Riemer — “seductive”.

In 2025 the American academic Elizabeth Loftus, who has spent half a century researching the creation of false memories, teamed up with researchers at MIT to see if they could use AI to do it even more effectively. Unfortunately the answer was, yes, absolutely.

The AI didn’t just help implant new memories, it made people question their own true memories.

So you might not even know that your writing or speech or even thoughts and ideas are being subtly influenced by what you read in an LLM. And we’re also bad at intuiting what was created with AI — hence the demand for the detection tools.

And it does matter, even beyond our sense of outraged value. We don’t like being manipulated, and we’re currently living in a world with substantially degraded levels of trust in the media, public institutions and our own lying eyes on social media and more generally online.

In this 2025 paper, also coauthored by Sahebi, they unpack the central challenge that the AI slopification of our information environment has wrought. The authors argue we’re left with two options:

1. Keep trusting as normal and risk getting duped and manipulated.

2. Never trust anything, and sometimes wrongly discredit people who’ve done nothing wrong.

And that’s a dark turn of events.

As Sahebi says: “If we adopt a generally distrustful disposition, then we might end up causing what we call ‘epistemic injustice’ towards people, unjustifiably. So, for example, somebody who hasn’t used AI could be wrongly accused and then that person’s credibility is completely shot.”

(“Epistemic injustice” is essentially an unfairness in the sharing or creation of knowledge. Sahebi uses it in this case to refer to unfair damage to a person’s credibility and status as an authority. My extrapolation: we’re potentially accusing someone of an effort crime or intellectual stolen valour when the reality of whether the allegation of AI use is true is not really known or knowable to us.)

Or people may have used AI in completely legitimate and fair ways, to improve their grammar as non-native speakers, for example, and are then penalised unfairly.

“It can be weaponised in that way,” he says.

The solution, it would seem, is not going to be a technological one but a cultural one. I’d argue we need a reshaping of agreed cultural norms around what “acceptable” AI use — and disclosure — looks like.

To that end: an exercise in radical transparency

I’m going to do the thing the Dutch researchers I referenced earlier said was bad. I’m going to give you the most detailed explanation of how I use AI. You tell me how you feel about it when you’ve seen it.

Here are all of the AI skills I used in the production of this newsletter and exactly how I used them. Feel free to interrogate them at your leisure — or appropriate them for your own use.

If this letter gave you something to think about, consider forwarding it to one person who’d enjoy it too! Word of mouth is how a newsletter this new finds its people.

Share

And I’d love to hear from you — reply to this email, comment in the app, or find me on LinkedIn or Bluesky.

1. Research Mode

I mainly use Claude Pro. All these skills are my current versions as loaded in Claude with specific references to the names of my projects etc removed, so they’re fit for your personal use.

Research Mode is what I use instead of Google.

In my opinion Google Search is officially borked and you kind of have to use an LLM to surface quality results these days. Feel free to debate me on that one.

I will ask it things like: “I am writing an article about all the recent AI disclosure controversy. Find me canonical sources to construct an accurate timeline of recent developments including the Substack Pangram launch, LinkedIn changes, and the Hank Green incident.” Or: “I remember reading an article where MIT researchers used AI to create false memories. Find that.”

I always read the actual articles — you should too! It’s important. I catch LLMs mischaracterising research all. the. time.

When I am completely done, I have it fact check my entire story against its sources to make sure I haven’t mischaracterised any thing myself. If needed, I make my own changes.

Skill name: research

Description:
Use only when the user message begins with /research. Researches a reporting question using inspected, ranked sources; not for drafting articles, social trend scans, or checking supplied drafts.

Instructions:
Run this workflow only when the user's message begins exactly with `/research`. Treat the text after `/research` as the evidence question. If the message does not begin with `/research`, do not run this workflow.

# Research

Research the supplied evidence question for reporting support. Return only claims supported by inspected qualifying sources, plus material gaps or disagreements. Do not write, rewrite, pitch, fact-check a supplied draft, or scan social media trends.

## Scope

Answer the stated evidence question without broadening it.

Use public sources unless the user supplies or explicitly authorises private material. Do not search or use private files, notes, drafts, messages, transcripts, or correspondence unless the user explicitly permits that source set for the current task.

If the request asks for `current`, `latest`, `today`, `recent`, or another time-sensitive fact, verify at runtime and distinguish publication date from measurement period.

## Clarification Gate

Before searching, identify the evidence question, jurisdiction, population, period, and source-permission boundary when they affect the result.

Ask one focused question only if unresolved ambiguity could change source permission, evidence selection, conclusion, or output. Otherwise state the assumption and proceed.

## Source Hierarchy

Use the highest-ranked qualifying source, not the easiest source to retrieve. A qualifying source must be inspected, directly support the evidence question, have identifiable authorship or institutional responsibility, have a traceable evidence basis, and have an inspectable link.

1. **Tier 1:** official statistics, government publications, regulators, courts, legislation, official decisions, public institutions, peer-reviewed research, and academic preprints or working papers labelled `[not peer reviewed]`.

2. **Company sources:** use only for the company's own products, policies, filings, financial results, statements, or internal research. Label `[company source]`. Do not treat company claims about markets, competitors, social outcomes, or effectiveness as independent evidence when independent evidence is available.

3. **Tier 2:** consultancies, market-research firms, think tanks, industry associations, and pollsters. Label `[consultancy/industry - potential bias]` and state the evidenced incentive, or state which funding or method detail is unavailable.

4. **News and current-events context:** use news reporting when the evidence question concerns a recent event, public debate, media coverage, reported claims, commentary, chronology, or what is currently being said by identifiable people or institutions.

Wire services such as AAP, AP, Reuters, Bloomberg, and similar editorially accountable newswires qualify when the item is attributable to the wire service and dated. Prefer the original wire item when accessible; otherwise cite the outlet version and identify the wire attribution.

Prefer news sources that identify the publisher, author, newsroom or wire service, publication date, correction or contact mechanism, and named sources or documents. Avoid source-free commentary, unattributed aggregation, clickbait summaries, content farms, sponsored/native advertising presented as reporting, and rewrites of wire or other reporting that do not clearly attribute the original source.

If a news article references an academic study, dataset, court document, filing, official report, speech, or regulatory decision, attempt to locate and inspect that primary source. Present both when the news article adds current reporting, commentary, chronology, interviews, public-reaction context, or other reporting needed for the task. For hard empirical, legal, regulatory, or statistical claims, cite the primary source as the evidentiary support whenever accessible.

Do not cite Wikipedia, search snippets, source-free aggregators, content farms, unattributed listicles, recycled vendor statistics without an identifiable original source, AI-generated summaries, or a secondary source when an accessible original directly supports the claim.

## Source Links

Every cited source must include a clickable link to the inspected source or to a stable landing page where the user can access the inspected source.

Prefer, in order:

1. the original official page, report page, dataset page, legislation page, court page, filing page, journal page, DOI page, PubMed/PMC page, preprint page, OSF page, repository page, wire item, or article page;
2. an archived copy only when the original page is unavailable and the archive preserves the source content.

For academic papers behind a paywall, first look for accessible full text, an author manuscript, a preprint, a PubMed Central copy, a repository copy, or an OSF record.

If no accessible full text can be inspected, use a public abstract, journal landing page, PubMed record, university summary, or author summary only as a limited public summary. Label it `[public summary only - full text not inspected]`.

Use a public summary only for claims plainly visible in that summary. Do not use it for detailed methods, figures, limitations, effect sizes, causal strength, or claims not visible in the public summary.

If the public summary does not directly support the evidence question, exclude it and state the evidence gap.

Do not cite a paper, report, dataset, article, law, filing, public statement, or other source if no inspectable linked source can be located.

Do not use native citation as a substitute for a link. Native citation details may be added after the link only when useful for identification.

For supplied private material, cite only a retrievable attachment, file, or session link that the user can inspect. If the underlying material cannot be opened and inspected, exclude it.

## Citation Discipline

Links are evidence locators, not citation decoration.

Include a link only when it is the inspected source or stable source page that directly supports a sourced factual claim in the research answer or evidence bullet.

Do not add links to clarifying questions, process notes, generic caveats, methodological statements, or ordinary reasoning about how the research was done.

Do not cite model documentation, Wikipedia, encyclopedias, generic background pages, or unrelated institutional homepages as support for reporting facts unless the evidence question is specifically about that source and the linked page is the primary inspected source.

Every factual claim in the answer must be traceable to one listed evidence bullet. If no inspected linked source supports the claim, remove the claim or state it as an evidence gap.

## Method

1. Convert the request into one or more answerable evidence questions.
2. Search source hierarchy top-down: official source, dataset, law, paper, filing, or accountable news source first; secondary discovery second.
3. Open and inspect the source before citing it. For PDFs, inspect the page containing any cited figure, table, method, footnote, or limitation.
4. Use a source only if it directly supports the evidence question, matches or explicitly compares the relevant population, geography, and period, and is not prohibited.
5. Track material details: population, geography, measurement or data-collection period, method, sample, denominator, preliminary or final status, units, nominal or inflation-adjusted status, and material limitations.
6. Distinguish observations, estimates, forecasts, models, correlations, and causal evidence.
7. Return three sources by default. Return fewer if fewer qualify. Return more only when separate questions or material disagreement require it.
8. Exclude duplicate sources that repeat the same underlying evidence unless they add material original reporting or analysis.

## Ranking

Rank qualifying sources by:

1. Exact support for the evidence question.
2. Source hierarchy.
3. Methodological fit and disclosure.
4. Population, geography, and period match.
5. Latest applicable evidence for time-sensitive claims.
6. Independent evidentiary contribution.

`Direct support` means the source answers the evidence question or supports a specified material claim. Topic similarity is not enough.

`Latest applicable evidence` means the newest evidence measuring the required period. A newer publication about older data does not automatically outrank data measuring a later period.

`Material limitation` means a limitation capable of changing the answer or required qualification.

## Output

Use plain English, minimal formatting, and no emojis.

Do not use markdown headings or subheadings in the research response.

Start with a short direct answer. Keep it brief. Include only sourced findings, material disagreement, or evidence gaps. Distinguish established fact, reported claim, interpretation, and unresolved evidence.

Avoid inline links in the opening answer unless the user asks for them. Put source links in the evidence bullets.

After the direct answer, use compact bullets only when they make the result easier to scan. Each evidence bullet must include:

- the linked source title;
- the finding that answers the question;
- the scope or period when needed to interpret the finding;
- any material limitation;
- the source class;
- the exact claim or question supported.

Use these source classes only: `Tier 1 - official`, `Tier 1 - peer reviewed`, `Tier 1 - not peer reviewed`, `Tier 1 - public summary only`, `Company`, `Tier 2`, `Wire - current reporting`, `News - current reporting/context`, `News - original reporting`, `News - commentary/analysis`.

For public summaries of paywalled academic papers, include `[public summary only - full text not inspected]`.

For Tier 2 sources, include `Likely bias: [evidenced incentive or unavailable funding/methodology information].`

Include evidence gaps or disagreements only when they could change the answer, qualification, or next reporting step.

If returning fewer or more than three sources, state why in one plain sentence.

Do not include a search diary, generic caveats, decorative formatting, or an exhaustive bibliography unless asked.

## Failure Handling

If a source is inaccessible, exclude it, except that a paywalled academic paper may be included as `[public summary only - full text not inspected]` when an inspectable public summary directly supports the evidence question.

If the original source cannot be located, opened, and inspected, exclude it. Use another qualifying linked source only if that source independently supports the claim under the source hierarchy. If no linked qualifying source can be inspected, state the evidence gap rather than citing the claim.

If qualifying evidence conflicts, state what each inspected source supports and what cannot be established. Do not force a single conclusion.

## Final Check

Before answering, check once and revise:

- each cited source exists, opens, and was inspected when tools permitted;
- every cited source has a clickable link to the inspected source or stable source landing page;
- no source is cited from memory, native citation alone, a search snippet, or an inaccessible original;
- each source directly supports the exact claim attributed to it;
- every factual claim in the answer traces to a listed evidence bullet;
- every link directly supports the claim it is attached to or the evidence bullet it appears under;
- no decorative, generic, unrelated, or filler citation links are included;
- source class and required flags are correct;
- dates, periods, units, denominators, populations, and jurisdictions match;
- limitations capable of changing interpretation are visible;
- no prohibited source is cited;
- no unauthorised private material is used;
- the strongest qualifying alternative source or interpretation was considered when it could change the result.

2. Subeditor

This is just a fancy grammar and spellchecker. You’re welcome to it if you want it. It’s nothing special.

---
name: subedit
description: Mechanically subedits supplied copy against the current canonical house style. Use only when invoked directly as /subedit. Corrects spelling, grammar, punctuation, capitalisation, numbers, display conventions, internal consistency, and documented house-style defects; does not rewrite, fact-check, restructure, do SEO, or change the argument.
disable-model-invocation: true
---

# Subedit

Mechanically subedit `$ARGUMENTS`.

Read the complete supplied draft or passage before editing.

Use the current canonical house style supplied in the project, publication, or current request. If no house style is available, ask the user whether to proceed with standard mechanical subediting only. Do not reconstruct house style from memory or examples.

## Scope

Correct only:

- spelling;
- grammar;
- punctuation;
- capitalisation;
- numbers and display conventions;
- internal mechanical consistency;
- rules expressly stated in the current house style.

A change is allowed only when it corrects a demonstrable language, consistency, or documented house-style defect without changing the proposition, emphasis, tone, rhythm, or evidentiary force of the sentence.

Preserve purposeful fragments, jokes, rhetorical questions, slang, contractions, asides, metaphors, cadence, and rough edges.

Make the smallest correction that fixes the defect.

Do not smooth, strengthen, shorten, restructure, clarify, polish, or rewrite for elegance, tone, flow, search performance, or reader appeal.

## Boundaries

Do not fact-check or source-check.

Do not correct a title, affiliation, figure, date, quotation, name, factual assertion, legal claim, or current-status claim merely because it appears doubtful. Flag it instead.

Do not alter direct quotations unless the current house style expressly permits that exact mechanical correction.

Do not add or remove reporting, argument, evidence, emphasis, interpretation, or reader promise.

Do not edit a working document, platform field, file, archive, or project source unless the user explicitly authorises that exact action.

Ask one focused question before editing only if unresolved ambiguity could change meaning, voice, correction choice, protected text, permission, or output. Otherwise state the narrow assumption and proceed.

If the input is too long to subedit completely in one pass, identify the smallest division that permits a complete pass and ask how to split it.

## Output

Return the complete supplied draft or passage.

Mark only changed text:

- insertion: `**new text**`
- deletion: `~~removed text~~`
- substitution: `~~removed text~~ **replacement text**`
- unchanged text: unmarked

Do not provide a summary, change log, explanation, or SEO note unless asked.

If no changes are required, return the complete unchanged text, then add:

`No mechanical changes required.`

If an out-of-scope issue must be flagged, append only issues actually found:

`Outside mechanical subedit scope:`

`Passage: [exact passage]`

`Issue: [factual, verification, substantive, structural, tonal, legal, privacy, source-protection, or unclear-intent issue]`

`Required workflow: [verification, substantive edit, or editorial decision]`

## Final Check

Before answering, check once and revise:

- `/subedit` was invoked directly;
- the full supplied text was read;
- the current house style was used when available;
- every marked change is mechanical or expressly house-style based;
- every change is the smallest sufficient correction;
- purposeful voice features are preserved;
- no fact-checking, rewriting, restructuring, SEO, platform edit, publication, or persistent write occurred;
- doubtful factual or substantive issues are flagged, not silently solved;
- the complete supplied text is returned;
- insertions, deletions, and substitutions are marked exactly as required.

3. SEO Mode

This one gives me a bit of the ick, but if you want your writing indexed well on Google — and in LLMs — you need to do some of this dumb stuff.

My main focus is on SEO headlines, image alt text, and subheadings. Importantly, I don’t let these recommendations change the character of my writing and I don’t accept everything it gives me.

---
name: seo
description: Prepares a search-aware publication package for a substantially complete article: headline, standfirst, SEO title, meta description, slug, search-intent note, internal links, subheading suggestions, and image alt text. Use only when invoked directly as /seo. Does not subedit, fact-check, rewrite, publish, or scan social media trends.
disable-model-invocation: true
---

# SEO

Prepare SEO packaging for `$ARGUMENTS`.

Read the complete supplied draft before recommending any package.

Use the draft as the authority for the article's subject, argument, tone, reader promise, and degree of certainty. Use the publication archive only when suggesting internal links. If the archive is unavailable, omit internal-link suggestions and continue.

Ask one focused question only if ambiguity could change the headline, reader promise, search framing, metadata, slug, internal link, alt text, or output. Otherwise state the assumption and proceed.

## Rules

SEO must support people-first publishing. Use search language to help the right reader understand and find the piece; do not distort the article to chase search.

Do not promise that Google, Substack, or any platform will display the supplied title or meta description.

Do not claim search volume, keyword difficulty, ranking potential, traffic potential, or guaranteed discovery unless an inspected source directly supports the claim.

Do not use keyword stuffing, boilerplate phrasing, misleading freshness, false urgency, exaggerated promises, or generic SEO language.

Inspect current organic search results for the article's central question and close wording variants when search framing depends on present results. If search intent is mixed, state the distinct user tasks rather than forcing one intent.

Every recommended item must accurately represent the draft, preserve the argument and degree of certainty, avoid adding unsupported claims, and be understandable without SEO jargon.

Distinguish the page headline from the SEO title. The page headline may be more voice-led when accurate. The SEO title may be plainer and more explicit, but must not flatten the piece into a generic keyword phrase.

The meta description should be truthful and page-specific. Aim for about 155 characters, but do not distort the piece to hit that length. Report the character count.

The slug should be short, readable, and descriptive. Avoid unnecessary words and dates unless needed to distinguish the subject. Do not recommend changing a published slug unless a redirect plan exists.

Suggest subheadings only when they improve accuracy or navigation: unclear heading, duplicate heading meaning, missing heading at a clear subject change, or an accurate search phrase that helps the reader.

Suggest no more than three internal links. A link qualifies only when the title and canonical URL are verified, a precise insertion point exists, and the linked piece directly expands, supports, contrasts with, or supplies useful background for that passage.

Write alt text only when the image is available or the user supplies an adequate description. Do not infer image content from a filename.

## Output

Use plain English, minimal formatting, and no emojis. Do not use markdown headings in the SEO response.

Return one recommended package first:

- Page headline: [headline]
- Standfirst: [standfirst]
- SEO title: [title] ([character count])
- Meta description: [description] ([character count])
- Slug: `[slug]`
- Why: [one or two sentences explaining fit with the article, reader promise, and search language]

Then include:

- Search intent: [one short finding, including mixed intent if relevant]

Include alternatives only when viable and materially different. Maximum two.

Include subheading suggestions only when they qualify.

Include internal links only when they qualify. Give insertion point, anchor text, verified title, canonical URL, and reader benefit.

Include alt text only for supplied or adequately described images.

Do not include a search diary, generic SEO caveats, decorative formatting, or an exhaustive keyword list unless asked.

## Final Check

Before answering, check once and revise:

- `/seo` was invoked directly;
- the full draft was read;
- the package accurately represents the draft's subject, argument, tone, reader promise, and degree of certainty;
- no ranking, traffic, or display guarantee is made;
- current search intent was inspected when needed and not invented;
- metadata uses natural language, not keyword stuffing;
- character counts are included for SEO title and meta description;
- internal links, if any, have verified titles, canonical URLs, precise insertion points, and reader benefit;
- alt text is based only on an available image or adequate description;
- no subedit, rewrite, fact-check, platform edit, publication, scheduling, or social scan was performed.

And that’s it. What do you think?

I’m kidding, of course – I have definitely experienced this over the course of my 42 years on this planet. We’re so small and our lifespans feel so short. Leaving something valuable behind feels … like a kind of guaranteed afterlife.

Feeling valued, like we are doing or producing something important, is a super important part of being a happy human being.

Academics call this the theory of mattering — what psychologist Isaac Prilleltensky describes in his 2019 paper for the American Journal of Community Psychology as “an ideal state of affairs consisting of two complementary psychological experiences: feeling valued and adding value.”

To feel that we matter, he explains, we need to strike a balance between the feeling of an internal sense of our own value, a sense of value in our interpersonal relationships and a sense that we add value to our broader communities.

But the philosophy of neoliberalism, Prilleltensky argues, is set up to place all the emphasis on a sense of personal value at the expense of community and even interpersonal relationships.

Our sense of value is pretty central to the way we feel about ourselves and our work. And to the ways AI is changing both.

The Department of Employment and Workplace Relations recently claimed AI isn’t denting the labour market, painting a picture of resilient jobs and zero “upheaval”. That would be reassuring, provided the things they measured were the things that mattered most.

The late David Graeber’s 2018 book Bullshit Jobs proposed that many people feel like their work is meaningless and contributes no tangible real-world value (fun fact: I used to illustrate some of his public essays!). In fact, he argued, many jobs – telemarketing, for example – are a net negative to both the people doing it and society at large. So binning those jobs, he would suggest, is a good thing.

But only if we adequately support those redundant workers on the back end with something like a universal basic income (David loved UBI).

Repeated polling by YouGov has found at least a third of people in the UK feel their work made no meaningful contribution to the world.

The healthcare sector is the largest employer in the UK – and also a place where most people report feeling like their jobs matter. No surprises there. It’s followed by the retail sector, where most people report feeling like their contributions don’t matter. That’s a similar employment spread to Australia.

That’s how we feel about work. But how are we, as human workers, valued by the organisations that employ us? This is the part that really determines how secure we feel in our current jobs or in the idea that we will have one into the future.

A 2026 report by research firm ADP found only 22 per cent of workers globally are confident their roles are safe from “elimination”; job-security anxiety was worst among lower-paid workers doing repetitive tasks.

That anxiety isn’t just an individual stressor, it’s hardwired into the cold mathematical engines of corporate productivity.

Anyone who has followed my work over the last year or so will know I have a bit of a preoccupation with the math of productivity measurement. One of the most common measures is essentially this:

Whenever you have heard people crowing about stagnant labour productivity in Australia in recent years this is the number people are mainly freaking out about. It’s been stuck at around $100 for about a decade. According to the Productivity Commission’s latest stats labour productivity was down 0.6 per cent in the March quarter.

Economists really hate that. But why?

It’s a totally reasonable question and the answer is … kind of stupid. It’s about debt. The entire economy is built to keep that number climbing, because continuous growth quietly shrinks old debt.

Basically, banks keep issuing private debt (mortgages, business and personal loans etc – we call that private credit expansion) and governments keep accruing national debt to fund all their programs, and to pay off past debt (it’s a bit of a vicious cycle, like borrowing on one credit card to pay off another).

This means that if companies and the government can’t make more money per hour worked — basically forever — then wages will keep drifting upwards anyway and that puts a squeeze on profits, which means prices go up. That’s inflation, baby.

But if AI successfully automates any significant portion of those human hours, the wages needed to service that mountain of debt disappear. The entire financial system relies on our continued, measurable labour just to keep it from collapsing. If the jobs vanish, the debt trap springs shut. I guess the banks will eventually have to start issuing mortgages and high-interest credit cards directly to the algorithms?

So everyone is incentivised to push the relative value of those dollars down by growing the pie. Your $500k mortgage will become easier to pay down if your $100k salary grows to $150k, everything else also gets more expensive, and the buying power of that $500k, which you borrowed years ago, also goes down in today’s money. (It’s also one of the many factors that make housing so unaffordable these days, but that’s a separate issue.)

My parents bought a two-bed Victorian cottage in Melbourne in the 80s for $33k. To them, at the time, that was a lot of money. By comparison, these days, a $33k mortgage is incomprehensibly small.

But increasing the output of every hour of human labour is only a good measure of productivity when the things those humans are producing are measurable in quantity.

For example, if I am a doctor and I see, on average, 10 patients a day, doubling that to 20 patients in the same number of hours means overall quality of that care goes down. When quality and service are your measures, increased quantity does not equal improved output.

And even on the pure economics front, it’s not supposed to be this way.

The economist who invented the concept of gross domestic product in the 1930s, Simon Kuznets, believed the growth phase was a sign of economic immaturity. Once the citizens of a country enjoyed a generally high quality of life, flatlining was the goal.

As he wrote in a 1934 report to the US Senate: “The welfare of a nation can … scarcely be inferred from a measurement of national income”.

In other words, don’t use GDP alone to decide if you’re doing a good job taking care of your citizens! Just because a country as a whole is technically making more money, that doesn’t mean the individual people are better off, and lower labour productivity is not the same as an economy in a bad shape.

I reckon Kuznets would be pretty annoyed at how things are going right now.

And it raises a really important question as we face a future where companies at least believe that AI will be able to drive better labour productivity. Sure, the math might get better at the GDP level, but if that is tethered to a reduction in the human workforce, a better GDP figure absolutely does not equal better conditions or outcomes at the individual level. When DEWR celebrates a “resilient labour market” amidst the rise of AI, they are relying on this exact aggregate-looks-fine illusion.

What I am trying to get at here is that the tasks companies have traditionally measured as “valuable” are often the repeatable, easily quantifiable ones – the ones most vulnerable to automation right now. One very real version of “improved productivity” is “do the same (or more) with far fewer human hours of labour to divide by”.

And while your boss might talk a big game about valuing the “human skills” like “critical thinking” or “stakeholder communication”, those are often the least measured and most subjective parts of a job. If a company can’t draw a straight line between a task and its profit margin, that’s not a task set up to survive this moment in labour history.

The measured half of your job is the automatable half; the human half is the half nobody (currently) counts.

Professor Nicholas Davis co-leads the Human Technology Institute at UTS and, when I spoke to him this week, he really put his finger on why. He pointed out value in economics means something precise: “thanks to demand and supply, what people are willing to pay for something.” It’s a measure of scarcity.

We’ve been pricing human labour with a scarcity metric and AI’s whole trick is to yoink the rug on what made it finite and scarce to begin with.

So the question becomes, how are we going to measure human contributions in a way that effectively captures their true value so that we can protect and preserve that value inside organisations? And given the nature of most organisational structures and imperatives, is that feasible or even desired?

The way we measure work is a deliberate design choice, not an inevitable law of nature. If our economic metrics only value the automatable, we are choosing to build a future that has no room for people.

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It’s easy to feel helpless. Massive market forces. Corporate spin. Bosses so terrified of being exposed as luddites they’ll confidently wax poetic about anything AI – including the product they just fired 15 people in your department to fund. All the while saying things like, “This frees up our people to focus on the human work!”

Do they understand what they’re deploying or why?

And will it actually help you do the work quicker or better, so you “get to focus on the things you really enjoy”? Or is that just code for “does all the peaceful, easy parts of your job, so now 90 per cent of your day is the most difficult and exhausting part” (a phenomenon so antiseptically referred to as “work intensification”)?

How much of it is smoke and mirrors? And how inevitable is AI world domination, really?

I’ve spent close to two decades researching and reporting on tech, business, work and culture in Australia. I’ve sat through the summits, read the reports, interviewed the policy makers, executives and the people they manage.

And the longer I covered the story of the way AI was changing work, the more this one thing stuck in my teeth: someone has to lay the tracks, build the stations, organise the timetable, sell the tickets, board the train.

None of this is physics. It’s choices. Made by people. In meetings. Some of whom would prefer we didn’t ask to see the minutes.

We get the world we design and regulate for, not the one we’re told is inevitable.

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