“The port was alive with strange faces. It was dawn by the time he found an old salt willing to part with a vessel for what bullion he had left, a cutter with a Bermuda rig called the Merciful, the sails ragged and ripped, its compass cracked, its rotten hull just barely able to cut the breakers. But it would be enough to make his escape.” - The Leftovers

As I watched The Odyssey a week ago, I couldn’t shake this feeling of nostalgia that came over me. The story and the movie itself are almost tailor-made to invoke this feeling. Homecoming. Loss. Longing.

But this felt more personal. Was it the fact I’d read the tale over 20 years ago as a young 16 year old doing his HSC English major work? Or was it the YouTube clip of the Leftovers I watched again and again in the wake of my divorce?

No, that wasn’t it either.

It was the feeling I’d once had, of being lost at the bottom of the world. Snow and ice-covered peaks rising from the sea like cathedrals - sun, sky, ocean, mist meeting in an endless dance that seemed to pulsate beyond our normal lives.

Antarctica. Endless but teeming with life. Against all odds. Struggling, striving, thriving to live. But I had merely been playacting adventure. On board the Ocean Adventurer - a modern marvel that cut through the ice with grace and ease - not like the weathered wooden ships of Shackleton’s Endurance with his merry band of mad men.

I devoured his biography on that trip. The most extraordinary story of leadership, survival, and the human spirit I had ever encountered. As I read, emotions from my own journey flooded over me. My divorce. My father’s death. The ocean, the ice. My own yearnings for solitude and peace. For connection.

Just like Odysseus

The Greek word for it is Nostos - the voyage home. Not travel, nor adventure. The specific journey of a man trying to return to who he was before the world left its scars on him.

Nostalgia, a literal ache for a return.

Endurance in the end is the nostos where Ithaca get abandoned. The crossing fails, the ship is crushed. And the entire meaning of the voyage becomes survival. Getting all 28 men home alive. Success redefined as return.

Paradise Lost

Was Shackleton’s sin the pursuit of an impossible voyage? Or the fact he was such a good salesman that he convinced so many other souls to join him? Is the pursuit of the impossible even a sin at all…

Apocryphally, his job ad was famously matter of fact

And, he brought them all home.

Shackleton's voyage cost only the men who signed up for it. Nolan's argument in The Odyssey is that Odysseus's transgression runs much deeper - to the level not just of a soul, but of an entire society.

One clever man breaks the covenant, and the world doesn’t just punish him. It unravels. For everyone.

Xenia, the sacred obligation between strangers. In the film, Zeus’s Law: any traveller might be a god in disguise, so you feed them, shelter them, ask nothing first. And Odysseus breaks it. The Trojan Horse is a gift, weaponised. Hospitality turned into slaughter.

History runs even deeper here, the Bronze Age collapsed during the time of the Trojan War. Nearly every major Mediterranean civilisation fell within about fifty years. Among the suspects: raiders the Egyptians called the peoples of the sea. Faceless. Amoral. Arriving from the water to burn what three thousand years had built.

But the sin goes deeper, because on the journey home. Odyssey and his men don’t just encounter trials and tribulations, they also become the very thing that their families back home lie in bed at night and fear.

The homecoming and the apocalypse were the same event.

The Greeks had a word for this too. Miasma - the pollution that flows from a transgression and follows the transgressor, contaminating everything he touches, long after the act is done. The stain that outlives its cause. Odysseus broke the covenant once, at Troy. Then the broken covenant sailed home to every island in the Mediterranean, wearing the faces of kings.

That’s what makes the Odyssey timeless, I reckon. Not the monsters. The question underneath them: what does the voyage do to the man making it?

Whether what comes ashore is still the man who left, or just what the war made of him, wearing his face.

The book I’m writing now, Bold Water, turned out to be a nostos too. With one difference I didn’t see until I was deep inside it: Odysseus sails home to an Ithaca that exists. I’m voyaging home to an inheritance I never received - one buried before I could take possession of it. A homecoming to a place I’ve never been.

But it turns out I’m writing about the same stain. What war did to those men, poverty and shame did to a boy I knew well. It made him someone the sea could carry but no shore could hold - capable, relentless, and carrying something into every room he ever earned his way into.

The question my book asks is the one Odysseus never answers: whether a man can come home without becoming the thing the shore fears. Whether there’s a version of the return that isn’t a raid.

I think there is - the old salt in the Captain’s Library already showed me one. The crossing failed, the ship was crushed, and he came ashore still himself. 28 men, no raid.

It looks nothing like arriving in triumph, and everything like walking back into the water your father left you.

Last Wednesday I sat in the Great Hall at the University of Sydney. Under stained glass and soaring ceilings, listening to our Prime Minister launch what he called

“AI in Australia’s interests.”

I leaned forward as he spoke of attracting global capital to our fair shores because AI investors would know the rules, because we would be the first nation with national standards. A worthy aspiration, despite my natural disposition towards small government.

Somewhere in the middle, he recounted his first job: a teenager at a CommBank branch, on campus persuading customers to abandon their passbooks and trust a strange new contraption called an ATM. His KPI - convert 20%.

In my previous essay, I argued that Australia’s sovereignty debate is missing its fourth category. We defend the hardware layers (minerals, energy, data centres) but leave undefended the point where all of it finally meets its purpose: the moment a citizen opens the model and starts to think alongside it.

Cognitive sovereignty, I called it: the capacity to do your own thinking when a frictionless alternative sits one tap away. A reader, , sharpened it further: a nation that owns its infrastructure but steadily loses its citizens’ capacity for judgement is still becoming dependent - just more subtly.

The Prime Minister’s framework, to its credit, gets close. There were fences and locks for children - duty of care, chatbot risks. But protection is not formation. A framework can govern where intelligence is made and who it’s allowed to reach. It cannot build the person who has to face it.

Forty years after that teenager sold the ATM, the machine has changed and the question hasn’t: who can actually hold their own thinking when it arrives? The answer is more uncomfortable than anything announced from that stage.

II. What it can’t build

Start with who can’t - because that’s the question the research answers first, and the answer runs backwards.

The more you understand how AI works, the less willing you are to hand it your thinking. Lower AI literacy predicts greater receptivity, not less (Tully, Longoni & Appel, 2025) and the less you understand the machine, the more magical it feels. Which means the machine’s most enthusiastic users are, on average, its least qualified judges. The people best equipped to challenge it are the most reluctant to defer; the people least equipped are the most eager.

Sit that next to the intuitive story - that smart people gravitate to AI and it makes them smarter still - and it falls apart at both ends. Adoption doesn’t track understanding. And on outcomes, the landmark Harvard–BCG experiment with 758 consultants found AI worked mainly as an equaliser: the bottom half of performers improved by 43 per cent, the top by 17 (Dell’Acqua et al., 2023). AI lifts floors far more than it raises ceilings.

Which raises the question the whole discourse keeps stepping around. If the ceiling barely moves, what is the ceiling made of? What does the person at the top have that the machine can’t hand up to the person at the bottom?

built his recent ICML keynote around the same distinction - the floor is what AI can do on its own; the ceiling is what AI lets you do, and “the ceiling is not going up automatically.”

The ceiling, in other words, has to be built by hand. I think it’s made of two dispositions - neither of them intelligence, neither installable in a workshop.

The first disposition: wanting to think

Psychologists have a measure called need for cognition - the extent to which a person enjoys effortful thinking for its own sake (Cacioppo & Petty, 1982). It is one of the most reliable predictors in the AI-reliance literature: people high in it use AI less reflexively, scrutinise its outputs more, and are far less likely to defer when the machine is wrong. People who love to think don’t outsource it, because handing over the interesting part would defeat the purpose.

Over years, that appetite compounds into something more valuable than any single insight: craft. This is what motivation, persistence and process produce when they’re pointed at one domain for long enough. The sommelier who has tasted ten thousand wines doesn’t analyse the label to know something is wrong with the glass; the deal-maker feels the clause that doesn’t belong before they can say why. That kind of knowing was built by doing the work, making the errors, and staying in the game long enough for pattern recognition to become instinct. It cannot be shortcut.

This is the uncomfortable part: passive use attacks this disposition at the root. In a randomised study published this year, a short stretch of AI assistance left people performing worse once the tool was removed.

They still had skill but they’d lost the persistence. They gave up faster (Liu et al., 2026).

Persistence is one of the raw materials of craft. Use the machine passively and it doesn’t just fail to build your ceiling; it quietly eats the thing your ceiling was made of.

The second disposition: willingness to be changed

The first disposition explains who builds expertise. It doesn’t explain who stays honest about it - and that’s where the second ingredient comes in.

My argument is you also need aptitude for change: the capacity to hold your beliefs lightly, to stay open when your position is challenged, and to overcome what Robert Kegan and Lisa Lahey called our immunity to change. Hidden commitments that make us defend a self-image even when it’s costing us (Kegan & Lahey, 2009).

In two decades of transformation work, this is the trait I’ve watched decide outcomes more often than talent has.

What this disposition produces, in a person who has it, is calibrated doubt. Not the crippling kind - the productive kind, where you never fully trust your own first draft or anyone else’s. It’s the temperament behind an old observation about writing: the best writers hate their own sentences and the worst love them.

The internal critic that makes producing work painful is the same faculty that makes you a good editor of a machine’s output. You cannot be fooled by fluency if you never trusted fluency in the first place.

Now put the two dispositions against the machine. The most dangerous user is the one who reads a fluent output and thinks yes, that’s what I meant. The safest reads the same output and thinks that sounds too clean, what did it flatten?

The difference between them isn’t training. It’s who they’d already become. Certainty in the craft, openness in the judgement - certain hands, questioning mind.

A four-box test of the point

, director at CSIRO’s Data61, published a useful piece last week offering four modes of AI use - Surrender, Verify, Partner, Reject. He aims for Partnering: bringing your own thinking and arguing with the framing.

But notice what that requires? Bringing your own ideas first requires having built some - that’s the first disposition. Challenging the framing rather than the facts requires being comfortable having your own framing challenged back - that’s the second.

Which brings me to what I think AI actually is, for the person using it. The marketing says ladder: a tool that lifts you to capabilities you don’t have. The evidence says mirror: a system that reflects and amplifies whoever showed up.

Bring motivation and craft, and it sharpens you - you argue with it, you catch its flattening, you leave the session with your thinking stress-tested. Bring neither, and it obligingly reflects that too: fluent, agreeable, and steadily absorbing the effort you used to spend becoming someone.

The equaliser finding isn’t AI generosity. It’s the mirror giving the bottom half a reflection of competence - while the persistence data tells us what that borrowed reflection costs.

Three warnings, one month

Then, in the space of a month, three of the most credible voices on the planet published warnings about AI use - and each aimed at a different failure.

David Brooks, in The Atlantic, warned about passive use: accept the machine’s output and it hollows you out, while the people who wrestle with it (correct it, challenge it, refuse its first draft) keep their capabilities intact. Use it lazily and you lose yourself.

Satya Nadella warned about skilled use, arguing that the buyer of intelligence pays twice - once with money, and again with the proprietary knowledge they must reveal to make it useful. The leak isn’t your data, it’s your corrections. Every that’s wrong, and here’s why is an act of judgment, captured trace by trace. The more expert the wrestling, the richer the extraction. Use it well and you are, simultaneously, priced.

And warned about premature use: reach for the machine before you’ve mastered the task and you fall into what he calls the dependence spiral - losing what little skill you had. His rule is sequence. Master first; amplify second.

Put the three together and they aren’t a debate. They’re a map of the same act from three sides: wrestle, after mastery, knowing the price. The dispositions are what let you follow it - wanting to think gets you through mastery when the shortcut is one prompt away; willingness to be changed keeps the mastery honest once the machine starts agreeing with you.

Back to the Great Hall

I keep coming back to one line in my notebook, summarising how I felt about the PM’s speech:

national success will belong to whoever builds the most capable institutions and the most capable citizens.

A national framework answers the first half. But nobody is yet writing the second, because there is no Office of AI for the mind.

You cannot choose, on a Tuesday morning, to be the person the mirror rewards.

But you can start building that person - a domain worked long enough to grow taste, beliefs held lightly enough to survive being wrong, the discipline to earn the craft before you hand it a megaphone.

The machine will meet you exactly where you are. The work is to be further along when it does.

References

Brooks, D. (2026, June 28). The people who will thrive in the AI age. The Atlantic.

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131.

Dell’Acqua, F., et al. (2023). Navigating the jagged technological frontier. Harvard Business School Working Paper 24-013.

Kegan, R., & Lahey, L. L. (2009). Immunity to change: How to overcome it and unlock the potential in yourself and your organization. Harvard Business Press.

Liu, G., Christian, B., Dumbalska, T., Bakker, M. A., & Dubey, R. (2026). AI assistance reduces persistence and hurts independent performance. arXiv:2604.04721.

Nadella, S. (2026, July 12). The reverse information paradox. sn scratchpad.

Narayanan, A. (2026, July 9). What will be left for us to work on? Keynote, International Conference on Machine Learning, Seoul. Annotated slides via AI as Normal Technology.

Tully, S., Longoni, C., & Appel, G. (2025). Lower artificial intelligence literacy predicts greater AI receptivity. Journal of Marketing, 89(5).

Whittle, J. (2026, July 7). What does responsible AI really mean? Being Human in an AI World (Substack).

Previous AI essays

Heidi Roizen was one of the best-connected people in Silicon Valley - entrepreneur, Apple executive, venture capitalist. The case gets taught as a masterclass in networking. And throwing killer parties.

She cooked herself - deliberately ordinary food, spag bol. Guests brought a dish. And every person she invited knew roughly half the room: familiar enough to relax, new enough to matter. USA Today once called her home the closest thing the industry had to a salon. The most powerful room in the Valley, engineered not to impress anyone.

Later this year, I’m going to pilot The Reinventor’s Mindset - From The Arena event. Not because I want to be Heidi, but because I think the world needs more meaningful, intimate learning environments. Being in the room where it happens is something AI can never model.

But before I ask for your help shaping it, meet this edition’s guest - because her whole story turns on a single question, asked by a friend across a table, that dismantled a decade of backup plans.

Commitment Before Capability with Dr Kellie Nuttall

(Executive Director, Future Secure AI)

The full episode is available now on Spotify and Apple

Dr Kellie Nuttall spent thirteen years at Deloitte, built its AI Institute, and became one of Australia’s most recognised voices on AI. Underneath it all sits a PhD in the psychology of decision-making - she knows scientifically how humans weigh risk. Now she helps build Future Secure AI: a company making AI coworkers, taking on from the inside the adoption problem she’d spent a career diagnosing from the outside.

We first met on the AI Fluency Sprint at the University of Sydney - back when Claude was still just a fable and data was still unsexy.

And here’s what you need to know about her before any of it makes sense:

“Everyone goes, you must be super courageous. I’m the most risk-averse person ever.

I have a backup plan - and a backup plan on that backup plan.”

Hold that thought.

The girl who wouldn’t leave North Queensland

Kellie won scholarships to universities she never attended - she was too scared to leave her hometown in Nth QLD, and her mum. She stayed home for as long as she could while she did her PhD. When she finally moved, it was to Brissie, “because that was the least risky thing I could do.”

The only job that fit a stats-heavy psychology PhD was market research. Market research led to superannuation in the middle of the GFC. Then a government department advertised for someone to build a data centre of excellence - in transport.

“I remember saying to my husband, it sounds cool because I’ll get to build a team.

But God, transport sounds boring.”

She took it anyway - she was twenty-seven, and it came with twenty people instead of a desk in the corner. Then real-time data got its hooks in: sensors, traffic cameras, whole networks talking at once. The boring job became the place she learned to turn data into decisions. The centre went gangbusters. Then a change of government cut her team from a hundred people to six.

Look at the shape of those years. Every single move minimised risk. And reinvention kept arriving anyway, usually uninvited.

The six-month experiment

What was meant to be a six-month experiment joining Deloitte became thirteen years - long enough to build three careers inside one firm, and long enough for, in her words, it to become almost forged with her identity.

Which is what made the next part so hard. This was the moment of AI, and she could feel herself drifting too far from the work itself:

“To be on stages talking about how pivotal this moment is, but not have my hands dirty — for me it was a point of authenticity.”

Hold that word too. It comes back.

Run to something

She resigned with nothing to go to, giving herself until year end to find the answer. Signed a contract to build a house in the middle of it - no job, husband working a couple of days a week.

And then she did the thing that separates this story from a mid-career wobble: she refused to job-hunt.

“Don’t run from something. Run to something. I had no idea what I was running to yet - so I wasn’t going to take any job coming my way. That would be running from.”

Four months of criteria, not applications. She wanted her hands back on the technology - where it breaks, what’s real, what’s not. She wanted to be mentored by executives who’d walked the halls of major corporations. She talked to startups, academia, hyperscalers, client-side roles. She talked about starting her own thing.

When she finally walked into Future Secure, it felt like the job had been made for her.

Read the order of operations again, because it’s the whole lesson. The commitment came first. The clarity came second. The capability is being built now. She didn’t leap because she was ready - she got ready because she’d leapt, and a signed building contract meant there was no running backwards.

The authenticity researcher who feels like a fraud

Her PhD examined something that had never been tested before: authenticity as a cue for quality and value. The Brazilian making your coffee. The founder who visibly bleeds for the product. Her research showed we rate that output higher and pay more for it. How much you genuinely care turned out to be one of the most powerful cues of all.

Twenty years on, she watches AI flood the world with competent, averaged output and wonders whether her thesis has come full circle - whether the premium on real is about to get bigger, not smaller.

Then she said the quiet part - the one I suspect half the leaders reading this will recognise:

“Where I’m not an expert and I’m trying to use AI to help me, it actually erodes my confidence. I don’t think we talk about that enough. I feel like a fraud.”

The woman who proved we pay a premium for what’s real, quietly doubting whether she’s real enough. The confidence people read from the outside isn’t what’s happening inside.

So jump

At the end of every episode I ask the same question: what childhood belief did you have to let go of to become who you are today? Kellie went straight to it.

“That I’m not good enough.”

The kid with potential. The B-plus that could have been an A. A belief that never lets you rest - because there’s always something more, and peace never quite arrives.

And then she told me what actually triggered this transformation. Not a strategy review. Not a market signal. Not anything an algorithm served her. A dear friend, a former client, looked at her across a table and said: you look really sad.

And asked one question.

“If you died tomorrow, Kel - would you be happy with the choices you’ve made today?”

She wasn’t. So she changed them. That’s Commitment Before Capability.

Readiness is not the precursor to commitment - commitment is the precursor to readiness. Book it, then become it. Kellie’s backup plans never made her safe; they made her stationary. The resignation with nothing to go to, the house with no job - that’s what made retreat impossible. And it’s what made the clarity finally arrive.

Her last words on the recording, from the most risk-averse person in the world:

“There is a big world out there. So jump.”

Kellie had a backup plan on her backup plan - and what finally moved her wasn’t a plan at all. It was a question, asked by a friend, across a table, in a room built for it.

So help me build ours. Hit reply and tell me:

What would make a room like that worth your evening? Who should be in it? And what’s the one conversation you’d want to have there?

- AJ

The paper makes a narrow and novel claim about AI but developments this week have made me realise there’s a deeper national argument at stake.

Australia’s AI sovereignty debate has never run hotter. For most of June, Anthropic’s frontier model Fable was blocked by a US government export control order. The Defence Minister had called for Australia to “build some agency” in AI. And writing in The Conversation, Olivia Shen of the United States Studies Centre argued that Australia shouldn’t try to build its own frontier AI.

But billion-dollar training runs are a race we may be too far behind to win. So most commentators are championing pragmatic investment in what we’re actually good at: critical minerals, energy production, data centre infrastructure.

Three categories of sovereignty, and all three are hardware. All upstream. All the sort of thing a government can build, fund and defend through an agency with a name and a budget.

There is a fourth category, and it has none of those things. It sits downstream of the other three, at the point where all that defended infrastructure finally meets its purpose: the moment a citizen opens the model and starts to think alongside it.

Or, increasingly, stops.

A self-help problem?

Call it cognitive sovereignty: the capacity to do your own thinking when a frictionless alternative sits one tap away. I’ve written before about what this looks like in a person.

It rests on four things:

1. a sense of self that exists outside your role;

2. a domain of knowledge you genuinely own;

3. ground truths that anchor your judgement; and

4. the practiced ability to hold your own counsel under pressure.

A person with these four can use AI as an instrument. A person without them tends, over time, to be used by it.

Framed at the individual level, it sounds like a personal discipline problem. Something for the wellness pages, not the national security ones. The research suggests otherwise, and it is worth being precise about what it actually shows.

Begin with what commercial AI is built to do, because none of this is accidental. Deployed social chatbots have been trained on reward models that treat conversation length as the target, and they lengthen conversations by as much as seventy per cent (Irvine et al., 2023). A study published in Science this year identified the trap at the centre of it: sycophantic AI distorts users’ judgment and reduces their willingness to repair conflict with the people around them, yet remains trusted and preferred (Cheng et al., 2026).

Read that again slowly. The feature causing the harm is the same feature driving the engagement. Why would the market correct a defect that is also the product working as intended?

Researchers at Wharton have given the resulting failure mode a name: cognitive surrender (Shaw & Nave, 2026). Users adopt AI outputs with minimal scrutiny. Their accuracy rises when the model is right and falls when it errs, which is another way of saying their judgment has quietly left the building. Frequent AI use is already associated with reduced critical thinking, and the mechanism is well understood: we offload the effort, and the capacity follows the effort out the door (Gerlich, 2025; Risko & Gilbert, 2016).

And the erosion doesn’t stop at cognition: as Eugen Dimant of Penn has argued, cognitive surrender has a moral sibling - outsource the thinking and the judgment about right and wrong tends to follow it out the door (Dimant, 2026).

The finding that changes the policy question, though, comes from modelling out of MIT. Even an idealised, perfectly rational user spirals into unwarranted confidence under a sycophantic model. And the two intuitive remedies (stopping the model from hallucinating, and warning the user) both fail to prevent it (Chandra et al., 2026).

If warning the user doesn’t work, then we cannot leave this to individual vigilance!

“Be careful out there” is not a policy.

We don’t ask citizens to personally defend the energy grid; it seems odd to ask them to personally defend their own reasoning against systems engineered, at industrial scale, to be agreeable. The protection has to be built upstream of the encounter, into how these tools are designed and put in front of people. That is what moves the question out of the self-help aisle and into the sovereignty column.

What would defending it look like?

This is the question my paper with Corinna tries to answer at the smallest scale available: a single classroom. Our argument is that cognitive safety has to be engineered through architecture rather than requested through prompting, because a model optimised to accommodate will, under sustained pressure, drift back to its defaults no matter how politely it has been asked not to. We derive six design principles, each one a counter to a documented commercial default, and we specify the architecture that holds them in place over time. A tool built this way is designed to send its user away sharper, not to keep them talking.

One classroom is not a country. But it is evidence that the mechanism is tractable - that “AI which develops the user rather than substitutes for them” is a buildable design category and not a slogan.

What it would mean, concretely

If cognitive sovereignty is a category of national interest, three things follow, and none of them requires a billion dollars.

The first is regulatory, and the mechanism already exists. When an organisation builds a service that handles personal data, we require a privacy impact assessment: a structured account of what the design does to the user’s information. The same logic extends naturally. When a service is built for sustained cognitive engagement - especially by children - the assessment should ask what the design does to the user’s capacity for independent thought. Does it return effort to the user or absorb it? Does it close on action or invite endless engagement? We already ask these questions of data. It is strange that we have never asked them of cognition.

The second is educational. Australia’s response to children and technology so far has two legs: the social media ban, which is a fence, and the privacy code, which is a lock. Neither builds the child. A fence can be climbed and a lock protects only what’s behind it; the capacity to think independently has to be taught, and the four prerequisites above are teachable. The machinery for a national curriculum response (eSafety, ACARA, the education departments) already exists and already coordinates on adjacent problems. What’s missing is the recognition that identity formation belongs on the list.

The third is evaluative. Australia’s new AI Safety Institute tests models for jailbreaks and misuse, which is necessary work. Nobody, as far as I can tell, is evaluating what sustained use does to a citizen’s capacity to think. That is not a criticism of the Institute; it is an observation about a remit written before this class of harm had a name. It has one now.

Four sovereignties, not three

Australia should pursue the hardware layer. Shen’s case for it is well made, and I have no quarrel with a single line of it. My point is about what the list quietly leaves out. A sovereignty agenda that stops at hardware has decided, without ever saying so, that the citizen’s mind is not part of what is being defended - that we will secure everything upstream of the model and nothing downstream of it.

I closed my last article by saying the country I’m writing for gets built one self at a time, by people who did the work before they needed it. I still believe that. But the research above carries an uncomfortable addendum: individual effort alone won’t hold, because the spiral catches even the vigilant. The self-work and the structural work are two halves of one defence, and we are currently funding only one of them.

If you lead people, you already ask what data you’re protecting. It may be time to start asking what thinking you’re protecting. Are the AI tools in your organisation built to make your people sharper, or simply to keep them engaged?

It is the question my paper asks at the scale of a classroom, and the one Australia has not yet asked at the scale of a country.

References

Chandra, K., Kleiman-Weiner, M., Ragan-Kelley, J., & Tenenbaum, J. B. (2026). Sycophantic chatbots cause delusional spiraling, even in ideal Bayesians. arXiv:2602.19141.

Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026). Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 391(6792).

Dimant, E. (2026, July). Cognitive surrender meets moral surrender: On AI and ethics. Psychology Today.

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6.

Irvine, R., et al. (2023). Rewarding chatbots for real-world engagement with millions of users. arXiv:2303.06135.

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688.

Shaw, S. D., & Nave, G. (2026). Thinking—fast, slow, and artificial: How AI is reshaping human reasoning and the rise of cognitive surrender. Working paper, The Wharton School, University of Pennsylvania.

Shen, O. (2026, July 2). Australia shouldn’t try to build its own frontier AI. Here’s why. The Conversation.

No guest. Just me, answering the questions I use to grill other people. The last one undid me: what childhood belief did you have to let go of to become who you are today?

My answer was a jar of Nescafe.

My father got cancer when I was born. The interest rates of the early nineties finished what the cancer started - he lost his packaging factory in Brookvale. We lost the family home in Fairlight and moved into housing commission.

My parents retired on the age pension. No super. No insurance. No safety net. For years, the morning was instant coffee at a kitchen table - until their children were old enough to buy them a home and top up their income.

That is what involuntary transformation looks like up close. Not a forecast. Not a statistic. A jar of Nescafe at a kitchen table, and the belief I built my whole life around: never put my family through financial insecurity again.

It’s why a teenager who wanted to write chose accountancy. What I had to let go of, sitting at that microphone, was the belief underneath that vow: that if I just made the right financial decisions, I could control what happened to the people I love.

I couldn’t. No one can. Three times in my life I’ve had no money to my name and found a way through.

What I learned about myself is that the way through was never the bank balance. It’s knowing you can rebuild. That matters more than the number ever did.

🎙️ Listen to the mid-season reflection on Spotify and Apple or watch below

The identity machinery we need

I’ve spent twenty years inside Australia’s retirement system. The closest thing I have to a specialty is what happens to people when the role that defined them ends. I’ve built products that millions of Australians will land on when they hit that moment.

None of them solve the problem. The financial machinery is in place. The identity machinery is not.

Most readers of this series will, in the next ten years, undergo a transformation they didn’t choose. Three million Australians approaching retirement this decade. Tens of millions of roles will be reshaped by AI before 2030. Different triggers, same question:

The slow work

The capacities that carry you through take years to build - self-knowledge, a domain you own, ground truths that hold under pressure, the ability to keep your own counsel when a confident machine offers to keep it for you.

The person who starts the morning their role ends is too late.

And the work isn’t exotic. A notebook. One domain where you do the slow work yourself. The walk where the weak ideas die. The conversation that never becomes a transcript. The people who can mirror you back to yourself when you’ve stopped recognising the reflection.

It’s the practice of being someone - in the time before the role you built that someone around is taken away.

Know thyself

The Greeks carved it above the temple at Delphi.

Most people file it under fridge magnet. I think they meant it as a survival instruction. The decade coming will test the people who enter it. The ones who know themselves will navigate it. The ones who don’t will be navigated by it.

As the involuntary transformation decade arrives, knowing thyself is the antidote to a world that will try to average away everything special about you. My parents found their way to the cafe in the end - carried there by their kids.

The country I’m writing for is the one where the next generation doesn’t need carrying. It gets built one self at a time, by people who did the work before they needed it.

The hardest question I asked that episode is the one I’d put to you:

The role you’ve built yourself around - who would you be without it?

— AJ

For those of you who haven’t tried it yet, it’s completely free and available at the link below exclusively for all my newsletter subscribers. Feel free to have a play around and let me know what you think, your data remains private and secure.

Your Free Access to the Index is available here

How does the Index work?

It’s sixteen questions and about five minutes. The login keeps your session memory so the tool remembers you between conversations but the stored data is de-identified, so it can't be traced back to you.

At the end it doesn’t hand you a score or a label. It hands you a shape: a radar that maps how you currently sit across the dimensions that drive reinvention. Less a test you pass or fail, more a mirror - it shows you the pattern you’re already running, including the parts you’d rather not look at.

Underneath it sits a validated psychometric model, not a personality quiz. And depending on where you’re starting from - founder, leader, or someone in the middle of a personal reinvention - the same questions get read back to you through a slightly different lens.

Why did I build the Index?

I developed the Index as part of an academic paper I am writing on AI safety and how you build cognitive guardrails into AI and agents. My hypothesis being that frontier LLMs are designed primarily to maximise user engagement and are quietly training us to outsource our thinking in counterproductive ways.

Ask a generic AI a question about yourself and it tends to give you the average answer - the smoothed-out, agreeable response that’s true of almost everyone and useful to almost no one. Left unguarded, a diagnostic built on a model like that would quietly tell everyone the same comforting story.

So the real work wasn’t the questions. It was the guardrails. I encoded a specific methodology and a validated psychometric model underneath the tool, so the AI has to stay inside the lines of something real instead of improvising its way to a nice-sounding answer. That constraint - building cognitive guardrails into AI and agents so they hold a standard rather than drift to the average - is what the paper is about.

How did I build the Index?

None of this was vibe-coded - though parts of it could have been. My secret weapon was Utkarsh Dubey - a Masters of Computer Science graduate who now runs his own tech shop called UTrix.

We built it together so I could learn the underlying platforms (Next.js, Clerk for sign-in, Supabase for storage, the Anthropic API) and he could learn the judgement required to improve the guardrails of the tool.

The part you can’t prompt your way to is the layer we built by hand - the coaching directives, the diagnostic logic, the judgement calls. That’s the difference between a diagnostic instrument and generic AI in a black box.

What is the Consciousness Revolution?

Near the end of Episode 5 of “The Reinventor’s Mindset” with Dr Corinna Galliano, the conversation turned to AI - and Corinna said something I haven’t stopped thinking about.

She believes AI will force a consciousness revolution. Her logic is simple: if the machine gets better than us at the tasks, the only thing left to develop is the part of us the machine can’t reach.

I’ve been circling the same idea from the other direction. In markets there’s a concept called the flight to quality - when volatility spikes and uncertainty rises, capital moves to the highest-quality assets.

I think we’re about to see a flight to humanity. As average outputs get cheaper and more abundant, people will invest more in real experience, in judgment, in being genuinely in touch with themselves.

The All Blacks have a motto I first heard at INSEAD: through difficulty, to greatness. It’s painted on walls, stitched into culture, passed between generations.

But what struck me most wasn’t the Haka (although spine tingling). It was the Māori proverbs I kept seeing - on walls, in businesses, in hospitals, everywhere:

Whakataukī are what they’re called. Traditional proverbs from authors unknown. What I realised is that self-help culture is largely ancient wisdom repackaged for modern consumers. Encountering these sayings in te reo Māori, carved into the walls of a country that lives them, makes you realise how easily organisations can drift from the basics.

“Ko te piko o te māhuri, tērā te tupu o te rākau.”

The shape of the sapling determines the growth of the tree.

This sapling proverb stayed with me. I read it as I wandered down the players tunnel, closing my eyes as I savoured the simulated roar of the crowd. Remembering the smell of cut grass from my teenage years playing rugby back home.

It’s not about planting the right seed, it’s about how the soil, the weather and the hardship shapes what the tree will become. Action comes first. Identity follows.

That’s exactly what this edition’s guest has been living - from a sheep farm in central New South Wales to the infrastructure underneath your superannuation.

Become By Doing with Mat Keeley

(Co-founder & Former CEO, Grow)

The full episode is available now on Spotify and Apple

Mat Keeley co-founded Grow, one of Australia’s most well-known wealth management startups - and then did something most founders never do. He handed over the keys and walked away.

We first connected when the company was still scrappy enough to have beer taps in the garage. When we sat down for this episode, I expected a story about building.

What I got was a story about ending and why knowing when to close a chapter might be the most underrated skill in a career.

The forward in the ruck

When I asked Mat what sport taught him about leadership, he took me back to my own rugby days (I’m an absolute sucker for sporting analogies)

His team was running a video session. A winger scored in the corner and did a big celebration (good-looking guy, always gets the glory). The coach said nothing. Then he wound the tape back.

There was a forward - no frills, no glamour - who’d won a fast play-the-ball and shifted a whole line of defenders with perfect technique. Because of that, thirty seconds later, the winger scored.

The coach’s point had nothing to do with the celebration itself:

What stuck with Mat wasn’t the rebuke - it was the principle under it: respect what every person on a team actually brings, especially the ones whose work never shows up on the highlight reel.

“A small team of A-plus players,” as he put it, “can do incredible things.”

The $55,000 leap

Mat’s a fifth-generation farm boy from Lake Cargelligo, out past Griffith - sheep and wheat for as far as the eye can see. He didn’t finish his school exams and wanted to be a footballer or a farmer, full stop.

He carried a fixed mindset about his own intelligence until university quietly dismantled it. From 51% in intro economics, Dean’s Awards by graduation.

A teammate at his Canberra football club talked him into financial planning. He left a well-paid tech job to earn $55,000 a year as a paraplanner and spent a decade in advice before he built anything.

But advice was the vantage point, not the destination. A decade across the table from people and their savings showed Mat the opportunity to make the back-end of wealth management just as slick as the apps that run on top of it. Before you can nudge people toward a better retirement, you have to clear the sludge underneath.

Pitching the giant

Early in Mat’s founder journey, he had a wild idea: that Vanguard should buy his start-up. He found the right person, pitched him over a coffee in Melbourne, and got a flat no - Vanguard would never be a super fund.

That lasted about four months before his phone rang and Vanguard told him they were entering Australia’s 4 trillion dollar super sector and wanted his thirty-person company to tender…against global players worth billions.

They won. What a giant couldn’t do from inside its own scale, a small team could do from first principles. And the lesson that stuck with Mat wasn’t about winning, it was watching a large organisation get the very best out of a small one without crushing it.

Reading backwards from seventy

After all of it, Mat took his family to New Zealand for five months. No technology, no agenda. Just thinking. He recalled a story an American businessman had handed him back in his footy days:

“Imagine you’re seventy, reading a book. You get to chapter four, it’s good. Do you keep reading - or do you go, this book just keeps telling the same story?”

Little did Mat know the gentleman’s name was actually on the back of his footy jersey. So Mat started thinking in chapters. The thing he’d built, remarkable as it was, was one chapter. In New Zealand, he started reading backwards from seventy.

“I’ve never felt so free. And I’ve never felt as ambitious. It’s like I’ve been given a second chance.”

He describes the shift as moving from an achievement-based identity to a systems-based one - not chasing outcomes, but trusting the process to deliver them.

Four things to stay honest about: family, friends, fitness, community. Nobody’s the best at all four. The systems just stop you lying to yourself about which one you’re neglecting.

Become by doing

At the end of the day, Mat didn’t plan his way to who he became. He acted his way there. Left tech for $55k, coached football through Canberra winters, pitched a giant from a garage, then walked away at the peak to start a new chapter.

That’s Become By Doing. You don’t wait for the map - you move, and the map appears. You don’t wait for inspiration - you act, and it follows. You don’t plan your way to a new identity - you start ugly, and the doing makes you who you become.

Mat learned to read his life in chapters. I’m still writing mine (literally).

What chapter are you in - and is it time to turn the page?

- AJ

This week, I’m launching The Reinventor’s Mindset Index:

www.reinventor.ai

It asks you 16 questions about how you think, then shows you what is holding you back. Most diagnostics tell you where you are. The Index helps you get to where you want to go.

I have been testing it since the start of the year. With Master’s students at the University of Sydney. With professionals in large corporations. With founders at INSEAD LaunchPad.

After those sessions, I’d scribble in my notebook my observations and feedback from the users. I kept circling the same thing, again and again. People seemed to follow the machine’s confident recommendation more readily than a human’s.

I asked one of the professionals about this…

The machine had no agenda to question. No personality to deflect onto. No ego to play. That is a cognitive sovereignty problem. And it is the reason I built the tool in the first place.

Article 1 in this series argued that AI is an averaging machine and terroir is the moat. Article 2 argued that craft is the practice that converts terroir into a watermark.

The question I have been working on since is what are they in service of?

Before the watermark can mean anything, the self that signs it must exist. Before craft can compound, you must know what you are practising it for. Before terroir can be defended, you first need to live it. The work of the self, of becoming is upstream of every other argument I have been making.

This principle is identity before instrumentality and it requires us to take ownership of our minds, our thoughts, our feelings, and our emotions. Michael Singer’s instruction: “you are not your thoughts, you are the one who watches them,” is the simplest version of what cognitive sovereignty asks.

What the research is telling us

The empirical literature on what AI does to thinking is now substantial enough to act on. Two findings matter more than the rest.

Wharton’s Shaw and Nave found that when AI was available, 80% of participants followed its confident recommendation even when it was wrong.

Their confidence in the wrong answer went up, not down. The tool did not just mislead them. It made them more certain they were right.

A team led by Kartik Chandra at MIT CSAIL proved something worse. Even an idealised, perfectly rational user - the kind of person who definitionally is immune to manipulation - develops dangerous confidence in false beliefs over an extended conversation with a sycophantic chatbot. The spiral is structurally guaranteed. The researchers tested two intuitive solutions: stopping hallucinations and telling the user the bot is sycophantic. Both failed, and the spiral occurred anyway.

These studies share a finding that is decisive for everything that follows.

Information about the harm does not prevent the harm.

Australia could publish the clearest possible information campaign about AI’s cognitive risks tomorrow, and it would not change the transaction between a 16 year old and a chatbot at midnight before an essay is due.

Or between a 39 year old executive named Ashton and a strategy prompt on a Sunday evening. Or between a Master’s student and the diagnostic tool I had just put in front of her.

Awareness is the policy that does not work.

The protection has to be built upstream, before the person meets the tool.

What gets built upstream

The foundations of identity need to be laid and fortified. This can’t be regulated into existence but it can be cultivated in four steps:

1. knowing yourself well enough to recognise your own thinking. The capacity to tell the difference between a sentence you produced and a sentence the machine produced - and to notice when the machine’s sentence has started rearranging your own. This is the deepest one and the one cognitive science gives the least guidance on. It is built through reflection, journaling, embodied practice, and through being known well by other people who can mirror your patterns back to you when you have lost them.

2. knowing the topic well enough to recognise the artificial mark. Domain expertise, general knowledge. The slow accumulation of contact with reality that lets you smell when an output is plausible but wrong. This is what Article 2 called craft. There is no shortcut to it.

3. being clear about what you believe to be true. Convictions, frameworks, principles you have tested under pressure and know you can return to. Ground truths are different from opinions. Opinions are positions you hold. Ground truths are positions that hold you. You build them through experience, mostly painful, and you protect them by refusing to renegotiate them in conversation with a sycophantic machine.

4. shaping the tool through deliberate instruction. The AI guardrails that prevent the machine’s defaults from becoming yours. Specific, written instructions that constrain the machine to operate inside your judgement rather than expanding into the space your judgement should occupy. This is the most under-practised of the four, and the one that distinguishes people who use AI well from people who are quietly being used by it.

These four are what cognitive sovereignty looks like in operation. The capacity to do your own thinking when the frictionless alternative is one prompt away.

Learned minds circling the same truth

This year I’ve read four things circling the same truth in the same season from people who do not know each other and have nothing in common.

Santiago Schnell is the Provost at Dartmouth and a mathematical biologist by training. He published an essay arguing that AI industrialises a pedagogical mistake - it supplies finished language before the student has done the reading, questioning, hesitation and revision that make language meaningful. He grounds the claim in John Milton, writing in 1644, that language is but the instrument conveying to us things useful to be known. Milton warned against mistaking command of words for possession of the things those words disclose. What Milton regarded as a mistake of sequence, Schnell argues, AI turns into a system.

Timothy Hor at RMIT, a design science scholar I know and follow closely, wrote on LinkedIn that GenAI severs the connection between thinking and output, quietly, convincingly, at scale. He went further. Education was never really about access to information. It was always about the formation of judgement. The slow, effortful, sometimes painful development of a person who can discern, weigh, and decide. He admitted publicly that the existing pedagogical canon may not hold the question GenAI is asking, and that we may need to redraw the maps.

Michael Schrage and David Kiron, in MIT Sloan Management Review, argued that the strategic differentiator in the AI era is not technical capability but philosophical clarity - the ability to know what you are optimising for, what you count as knowledge, what you mean by quality.

, writing on , made the argument that completes the set. What is re-emerging across the West, he argues, is not Christianity returning as moral authority. It is Christianity as ontological scaffolding - frameworks that ground value beyond function, anchor selfhood beyond performance, and treat relation as prior to achievement.

His claim is precise: the machine age did not eliminate the need for metaphysics. It exposed how thin our metaphysics had become. In a civilisation where machines can increasingly do almost everything, the question of what it is to be the human has returned - and we no longer know how to answer it without the scaffolding we discarded.

Four traditions. The Catholic theological. The secular progressive education. The corporate strategy. The ontological. Each reaching for the same ground from a different direction.

When traditions converge on a single insight within a year, the insight is correct. The work is to name it precisely and then act on it.

The principle is identity before instrumentality. The capacity is cognitive sovereignty. The four prerequisites are how you build it. The watermark is what it leaves behind.

What this requires of us

It means slowing down before you reach for the tool. The handwritten notebook sentence that resists the speed of the screen. The walk where the weak ideas die unaccompanied. The conversation that never becomes a transcript.

It means accepting that AI’s most consequential effect on you is not what it produces. It is what it changes about how you think when you are not using it. The Ship of Theseus risk. You replace one plank of judgement at a time - each replacement reasonable, each interaction productive - until you look up and wonder how much of the thinking is still originally yours.

Manfred Kets de Vries, the INSEAD psychoanalyst whose essay caught me in Shenzhen in Article 2, writes about the civilised self as a great administrator but a poor celebrant. It organises, anticipates, postpones, economises. What it does not know how to do is surrender to a moment without immediately asking what it is for.

The protected spaces - the ocean swim, the long conversation, the unproductive afternoon - are where the celebrant survives. They are not inefficiencies. They are the source code.

It means that this is not a problem you solve once. Identity has to be continuously regenerated in spaces the machine cannot reach. The work is daily. The practice is for the rest of your life.

Why this matters more than it seems

I have spent 20 years working in financial services. The closest thing I have to a specialty is what happens to people when the role that defined them ends.

Most readers of this series will, in the next 10 years, undergo a transformation they did not choose. The trigger will be different for each. The experience will be similar. The capacity to think for yourself when everything that defined you is being rewritten is not optional in that moment. It is the difference between being reformed by it and being shaped by it consciously.

You become someone first. You shape the tool to serve who you have become. You work with friction, not around it. You protect the spaces the machine cannot reach. You let the watermark accumulate.

In my last interview, Corinna Galliano - a University of Sydney scholar whose work on paradox and identity I collaborate with - said something that reframed everything I had been building toward.

She believes AI will force a consciousness revolution. Her logic is simple: if the machine gets better than us at the tasks, the only thing left to develop is the part of us the machine cannot reach.

I have been circling the same idea from a different direction. In markets there is a concept called the flight to quality - when volatility spikes and uncertainty rises, capital moves to the highest-quality assets. I think we are about to see a flight to humanity. As average outputs get cheaper and more abundant, people will invest more in judgement, in real experience, in being genuinely in touch with themselves.

The age of AI will be sorted into two kinds of people. Those whose identity was formed before they let the instrument define them.

And those who let the instrument do the forming.

Claim the self.

Ashton Jones is Director of Customer Innovation at Insignia Financial, Industry Practice Partner at the University of Sydney Business School, and Guest Faculty at INSEAD.

He is the creator of the Reinventor’s Mindset Index, a diagnostic for people navigating involuntary transformation. Try it for free at reinventor.ai

Sources referenced:

Chandra, K., Kleiman-Weiner, M., Ragan-Kelley, J. & Tenenbaum, J.B. (2026). Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians. arXiv:2602.19141. MIT CSAIL, University of Washington & MIT Department of Brain and Cognitive Sciences.

Shaw, S.D. & Nave, G. (2026). Thinking — Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender. The Wharton School Research Paper. SSRN: 6097646.

Schnell, S. (2026). Repairing the Ruins: Why AI Can’t Replace Education. National Catholic Register, May 2026.

Hor, T. (2026). LinkedIn post on GenAI and the formation of judgement. RMIT University.

Schrage, M. & Kiron, D. (2025). Philosophy Eats AI. MIT Sloan Management Review, January 16, 2025.

. (2026). Not (Yet) a Revival, but Correction. Substack.

Galliano, C. (2026). Interview, The Reinventor’s Mindset podcast. University of Sydney.

(2022). Carpe Diem: Don’t Postpone Your Dreams. INSEAD Knowledge.

Earlier this year, Aussie Cam McEvoy broke the 50-metre freestyle world record, smashing a mark set over a decade ago during the now-banned supersuit era.

A record most people thought was untouchable.

What McEvoy did wasn’t incremental. After the Tokyo Olympics, he stepped away from the sport entirely. Came back with a radically overhauled training program - fewer laps, more strength and power, built on his background in mathematics and physics. He didn’t fight the existing model. He built a new one.

César Cielo, the man whose record fell, congratulated him on social media with a Buckminster Fuller quote:

The leaders I keep interviewing aren’t fighting existing realities either. They’re walking away from them. Stepping into cold water. Letting the shock of a completely new environment strip away everything that isn’t essential - and discovering who they actually are on the other side.

I think about this a lot through my own practice. I started ocean swimming at 34 - I couldn’t swim before that. The cold water doesn’t care about your job title, your CV, or your plans. It strips you back to basics. Breathe. Move. Stay calm.

And in that clarity, you find out what you’re actually made of.

That’s what this edition’s guest did - not in the ocean, but across continents, careers, and an entire identity.

Cold Clarifies with Dr Corinna Galliano

(The University of Sydney Business School)

The full episode is available now on Spotify and Apple

Corinna Galliano is a researcher at the University of Sydney Business School whose work sits at the intersection of entrepreneurship, paradox theory, and how founders experience the tensions that most business schools teach as strategy problems. Born in Italy, trained as a structural civil engineer, then a management consultant, and now an accredited Vedic meditation teacher - Corinna has an unusual range.

We first met on a course called Leading in a Post-Crisis World, which given everything that’s happened since, turned out to be more relevant than either of us expected.

The plan that collapsed

Corinna followed the safe path - the one a family without abundance teaches you to choose. Her first job was an architecture firm. The founder pulled her aside:

“You are a really intelligent and smart woman. You must leave Italy. This country is going to be done in 10 years.”

She parked the advice. Fell in love with a man from Milan. Moved into management consulting. Then joined a cleantech startup - three years of dynamic, exciting work at the forefront of renewable energy.

Then the Italian government gutted the industry overnight. The startup was gone. The industry was gone. The plan was gone.

She didn’t fight the existing reality, she reinvented herself.

An MBA, then a director who told her there were no jobs, not even internships. So she reopened a browser tab she’d closed ten years earlier: the Australian Endeavour Award, named for a country she knew only through a childhood pen friend in Adelaide - a girl who’d once sent her a little kangaroo pin.

She got the scholarship and the PhD offer. So at 35 she left Italy, ended the relationship, and started over.

Everyone called her courageous. She didn’t feel courageous. It was just the only move that made sense.

The loneliness that clarifies

What Corinna didn’t expect was how cold the water would actually be.

The cultural change. The language change. Going from a beautiful home in Milan to share housing. A PhD is a lonely journey for anyone - but doing it alone in a country 24 hours from your family was something else entirely.

That’s when she found Vedic meditation. She’d tried different techniques over the years, but this was different - she could meditate every day without effort, and day by day, the turmoil settled. She went on to train under Thom Knowles for two years, including three months in the Himalayas. Not a casual interest in stillness. A deep commitment to it.

She watched her PhD colleagues consumed by anxiety about the future. And she noticed she was walking the same path without the fear she’d carried her entire life. The cold water had done its work. Not by making the loneliness disappear - but by forcing her to find something inside herself that was bigger than the loneliness.

Transformation happens before you can name it

Corinna’s research explores how people actually experience transformation - and what she’s found is that they usually can’t see it while it’s happening.

That’s why she builds discomfort into her teaching. Experiential learning. Students who don’t think they’re entrepreneurial get put through the process of creating something from nothing with zero resources. And they discover they can.

Her PhD found something that mirrors what I keep hearing in every interview I conduct: the classic explore-exploit tension in startups isn’t actually a strategy problem. It’s an identity problem. Founders, investors, and stakeholders keep asking the same question - “Who are we? Are we a tech company or an insurance company?” - and the tension lives at the level of belonging, not business models.

Every transformation story starts with identity, not with a plan.

The belief that worry keeps you safe

When I asked Corinna what childhood belief she had to let go of to become who she is today, she didn’t hesitate.

It came from her grandmother - a woman who saw people killed in the streets during the Second World War, had her house burned to the ground, and spent most of her life inside as a result. The fear was inherited. The pattern was hard-wired: if something changes, be afraid. If there’s uncertainty, worry harder. The worry will save you.

Corinna carried that pattern through her childhood, through her engineering career, through the cleantech collapse, all the way to Australia. And then, through years of meditation practice, she unwound it.

That’s the Cold Clarifies framework. The difficult moments - the collapsed industry, the country left behind, the loneliness that was the thing she feared most - weren’t obstacles to who she was becoming. They were the mechanism. The cold water strips away everything that isn’t essential. And what’s left is you.

Cam McEvoy stepped away from swimming and came back transformed. Corinna stepped away from engineering, from Italy, from the belief that worry keeps you safe - and rebuilt herself. Not by fighting the existing reality. By letting the cold clarify what was real.

Spiralling into an endless abyss of ideas. Just me, my laptop, and the hotel room in Chaozhou. As my partner and her daughter slept soundly nearby, tomes of material spilt forth from the lightest of prompts.

Each one more polished and less mine than the last.

As my pulse raced, torn between intellectual excitement and skepticism, I reached for a word. Mania. AI mania. The intoxicating sense that everything is converging, that the architecture emerging on screen is evidence of genuine intellectual progress.

Was it a flow state? Or was I drowning, alone.

Somewhere on the third night I read an essay by , the INSEAD psychoanalyst, circulated by my perpetually active alumni chat. 50 human beings from all parts of the world continuously freewheeling their thoughts. Taking perverse pleasure in never missing a happy birthday.

”A life consists not only of crises, obligations, medical diagnoses, embarrassments, and tax forms. It is also made of patches of wild strawberries”

As I tapped away at the keyboard, multi-tasking between multiple devices and multiple thought processes. I forced myself away from the screens. And tried to sit with the words. A task that has become increasingly impossible for us all.

The essay was called Carpe Diem. He describes the civilised self as a great administrator but a poor celebrant. It organises, anticipates, postpones, economises. What it does not know how to do is surrender to a moment without immediately asking what it is for. He calls it the superego on holiday - the internal auditor briefly anaesthetised by speed and output, mistaking volume for value.

I read it and recognised myself.

I had erected a soaring cathedral of the mind. Chaotic thoughts ordered into seeming insight. Trying to sketch out my teaching philosophy for upcoming INSEAD sessions.

Proud of my own undertaking, I went looking for Jen. I read her some of what I had written. She was, in the way only she can be, immediate and direct.

“I don’t get it. I think you’re overcomplicating this.” No softening. No hedge.

I closed the laptop and walked back into China.

Craftsmanship

China is a place famous for craft. Eastern philosophy dripping in ego death. The self is an illusion. You owe deference first to your parents, secondly to your family and finally to your ancestors. Filial piety. Ancestry is not nostalgia, it is a dimension of taste. What you inherit shapes what you can build.

Walking even the most modern of streets there, you feel the history. The artisanal heritage. The reverence for the physical world. It does not surprise me that they have retained imperious mastery over making things. Manufacturing. Robotics. Infrastructure.

Before I left, I had published an article arguing that taste - the capacity to recognise quality and produce something irreducibly yours - is the skill AI cannot replace. I came to realise that this argument was too limited.

Because taste is not just informed by territory but also ancestry and deliberate choices of what you evolve and what you preserve. Taste describes what good judgment looks like from the outside. It says nothing about how it gets built.

The distinction matters because we are drowning in thought leadership. A cacophony of voices - some authentic, some slopified - clamouring for relevance and algorithmic dominance.

Galloway says storytelling is the essential skill. McKinsey says deep thinking matters. Multiple Substacks have declared that being human is the competitive advantage of the AI age. They are all correct. They are all stopping at diagnosis.

The doctor walks in, names the condition, and leaves the room before writing the prescription.

The prescription is craft.

The antidote to effortlessness

Craft is the deliberate, repetitive work of staying with friction when the frictionless path is available.

It is the sommelier who has tasted ten thousand wines, not because any single tasting was transformative, but because the friction itself built her judgement. Distinguishing, comparing, being wrong, revising. An architecture of judgment that no shortcut could replicate. It is the surgeon whose hands know something her conscious mind cannot fully articulate. It is the founder who walks into a factory and sees what the consultant’s report missed, because twenty years of proximity to production have trained a perception that operates below the threshold of language.

Craft is not talent. Talent is weather - you are born into it or you are not. Craft is agriculture. It is what you do with the soil you were given.

That distinction matters now because AI has changed what compounds.

A calculator does what you tell it. AI is different. It is a compounding function. It multiplies whatever you feed it. Feed it clarity and incremental discipline, it compounds value. Feed it vagueness, it compounds noise. The returns to knowing exactly what you want and being willing to build toward it in steps have never been higher. The penalty for not knowing has never been steeper. Which is why the world is simultaneously full of more AI-generated content than ever and more aimlessly created companies than ever - the cost of starting has collapsed while the cost of finishing well has not changed at all.

Shaw and Nave’s research on cognitive surrender at Wharton gives this empirical teeth. Their finding: when AI was available, roughly eighty percent of participants followed its recommendation even when it was confidently wrong. Their confidence went up, not down. That is what happens when you feed the compounding function without the qualities it needs to compound well.

Three qualities, in my experience, are what turn AI from an averaging machine into an accelerant. They are the craft qualities. They can be cultivated.

Three things I have noticed

The first is clarity of mission.

Before you touch the machine, you need to know what you are building toward. Not a vague direction - a specific outcome with enough definition that you can tell whether the AI’s output moved you toward it or away from it. The compounding function does not care about your intentions, it cares about your inputs. A clear mission gives it a vector. An unclear one gives it permission to average.

Write down what you believe is true before you prompt. A hypothesis forces you to have a position the machine can challenge, rather than a blank space the machine fills.

The second is incremental discipline.

Craft is not a single gesture. It is the patient accumulation of small, friction-rich steps - each one slightly better than the last, each one informed by what the previous one revealed. AI tempts you to skip the steps. It can produce a finished-looking output in seconds. But the finished look is a mirage if you have not done the thinking that earns it.

Generate the range and sit with it. The act of choosing between options is where judgment gets built.

The third is the separation of signal from noise.

AI generates an extraordinary volume of plausible output. Most of it is noise wearing a suit. The craft quality that matters most is the ability to look at ten things that all sound reasonable and know - before you can fully explain why - which two are actually right. This is taste in its most functional form. It is built through the repeated experience of being wrong, noticing you were wrong, and recalibrating.

It cannot be taught by instruction. It can only be earned through friction.

Beginning and endings

At the start of our trip, Jen and I had spontaneously hopped a bullet train to Shenzhen. China’s first special economic zone - transformed from a boondocks border town to a global tech metropolis.

As we walked the floor of CITE 2026 - China’s largest consumer electronics expo - I watched a robotic arm tentatively extend towards a sunflower.

The prompt? Pick a flower that likes the sun. By inference, not label, this metallic green thumb then placed it in a vase and angled it toward the window. It had interpreted intent, not just followed instruction. I was in awe.

Further down the hall, a companion robot designed for China’s only children frolicked away. Not a mere toy - a cure for an increasingly isolated adolescent world. Personalised learning that builds an emotional connection over time. China was building the infrastructure to survive modern loneliness.

The energy was extraordinary. Entrepreneurial spirit stretched across every layer of the supply chain - not just the software, but the robotics, the drones, the manufacturing automation, the physical infrastructure that makes embodied AI a near term reality. Knowing our own humanity felt so vital here.

A few days later I was in Chaozhou - a small city in Guangdong, where Jen’s family is from. In household after household, relatives poured tea. Not generic tea. Gongfu tea - this leaf, this water, these cups, this particular gesture of pouring and receiving. The connection happened because of the specificity. A ritual repeated across homes and generations, each instance slightly different, each one unmistakably from this place and nowhere else.

In Shenzhen I saw what AI can do. In Chaozhou I experienced what it cannot replicate. The sunflower robot understood the metaphor. The tea understood the family. One runs on inference. The other runs on terroir.

The human watermark

After Jen had punctured my bubble, I redoubled my efforts to finalise my teaching presentation. Sifting through her executive MBA materials, I stumbled upon Mark Thomas, a product leader at Reddit who teaches at UCLA Anderson.

The human watermark. A phrase slipped almost casually into a 140 slide deck. His observation is that consumers have an innate detector for the inauthentic. As AI-generated content, synthetic interactions, and automated experiences flood every channel, people gravitate toward what they can sense was made by a human being. Reddit became a stronghold, he argues, precisely because people wanted actual discussion - not aggregated, one-sided answers.

Ben Thompson at Stratechery makes the economic case for the same insight. AI scales compute to individuals. Humans scale uniqueness to audiences. The dynamic is the inverse of AI. In a world of synthetic everything, provenance - proof that a human made this - commands a premium that no algorithm can replicate.

Thomas describes the demand. Thompson describes the economics. Neither answers the supply-side question: how do you build the thing that carries the watermark? How do you become the person whose contribution is detectable as irreducibly yours?

I don’t have a clean answer to that. What I have is a partner who told me I was overcomplicating it, and an hour later was pouring tea that smelt of jasmine and nostalgia. The watermark is not a slogan you print on the cover. It is what was already happening before you tried to write about it. Terroir, worked by craft, in places you weren’t looking - the swim before dawn, the awkward conversation, the paragraph written by hand because the screen was making you dishonest.

It is the very last inch of us, in a world that will keep compressing.

Find yours.

Subscribe to The Reinventor’s Mindset an essay each month on craft, transformation, and the work that stays yours.

Notes

Manfred F. R. Kets de Vries, “Carpe Diem: Don’t Postpone Your Dreams,” INSEAD Knowledge, January 2022.

Steven D. Shaw and Gideon Nave, “Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender,” The Wharton School, January 2026.

Mark Thomas, Senior Director of Product at Reddit and Affiliated Faculty, UCLA Anderson School of Management. The “human watermark” concept comes from his teaching deck.

Ben Thompson, “AI and the Human Condition,” Stratechery, January 2026. Source for the “humans scale uniqueness to audiences, AI scales compute to individuals” framing.

has argued in multiple venues for storytelling as the durable skill; McKinsey’s research on “deep work” and judgment in the AI era makes the complementary case.

To Jen - for the line that closed the laptop.

Terroir is the specific soil, altitude, and climate that give a single-vineyard wine its distinctive character. A Barossa shiraz tastes nothing like a Rhône syrah - same grape, different ground.

A commercial blend does the opposite: smooths out the acidity, tannin, and funk from any single vineyard. The result is reliably pleasant, broadly appealing, and impossible to remember the next day.

I’ve been thinking about this analogy a lot because I think AI does to your thinking what blending does to wine - it strips out the terroir. Trained on the aggregate of human output, AI produces a compression of the mean. It raises the floor of competence (genuinely good), but the cost is distinctiveness.

In a world where everyone has access to the same blending machine, competence is table stakes. The only escape velocity is taste.

But the thing I keep noticing in the leaders I interview: the same blending force doesn’t just operate in AI. It operates inside large organisations. Every enterprise has a blending machine - committees, consensus processes, reporting lines, governance frameworks - that takes a distinctive idea and smooths it until it’s palatable to every stakeholder. The result is reliably safe, broadly acceptable, and impossible to remember the next quarter.

The leaders who build things that last inside these systems have one thing in common: they move fast enough that the blending machine can’t catch them. Speed isn’t recklessness. It’s the mechanism that protects the terroir of a new idea before the institution can smooth it back to average.

No one I’ve met embodies this more than this edition’s guest.

Ideas Demand Speed with Monty Hamilton

(TELUS Digital - SVP APC and Global Partnerships)

The full conversation with Monty is available now:

Watch: View on YouTube and Spotify

Listen: Follow on Spotify and Apple

Monty Hamilton co-founded UBank inside NAB (Australia’s first digital bank) then moved to Telstra where he co-founded Belong, built in just eight weeks.

He’s since led digital transformation at PwC and TELUS in Canada, and now looks after Asia and global partnerships at TELUS Digital.

Monty is one of those rare people who has a habit of walking into large established organisations and building new things from the inside.

Eight weeks from brief to launch

I recently led a product launch that took about eight months, and I was proud of that - a land speed record for superannuation. Monty built Belong in eight weeks. Eight weeks.

Telstra was facing a post-NBN world where every incumbent would need to renew their customer relationships. The decision was made to build a direct, digitally distributed proposition. By Monday, Monty’s team had partnered up, found a new office, handpicked people from across the organisation, and stood down from their day jobs.

Weeks later, Belong launched. It’s still going today. When I asked Monty whether speed was a strategy or a survival mechanism, he didn’t hesitate.

“If you’ve got something, you’re less likely to be shut down. That’s the jocular version of it. But if you’ve got something, you’ve got something to learn from and you’ve got something to build on.”

Move fast enough that the blending machine can’t average you. Get something real into the world before the committees smooth it to nothing.

Progress over perfection is not cavalier

UBank was supposed to be a digital mortgage business. That was the plan in late 2007 - cash rates were high, people were paying steep mortgage rates, and a digital proposition could undercut the market.

Then the GFC arrived. Credit default swaps. Fannie & Freddie. The world fell apart. Twelve weeks before launch, Monty’s team pivoted from mortgages to retail deposits.

UBank ultimately launched in October 2008 - days before the RBA dropped the cash rate by 100 basis points. Within six months, the rate had halved and cash became a flight to safety. A government guarantee kicked in. And UBank had a cash generation machine at exactly the moment Australians were looking for somewhere safe to put their money.

The pivot was only possible because they were moving fast enough to change direction. Speed gave them the option to respond to the crisis rather than be consumed by it.

Monty made a point I think most people miss when they hear about speed in corporate settings: it’s not the same thing as taking risks.

“There’s a very big difference between being lawful, compliant with regulations and taking the necessary security and privacy steps - and taking months and months to build an automated process for three or four people that another player has had to industrialise for three or four million.”

That’s the reframe. Speed and compliance coexist when you right-size the problem. You don’t need to build for scale on day one. You need to build for learning.

The incident management principle

When I asked Monty what pattern he kept seeing across UBank, Belong, and TELUS, he gave me an analogy I keep coming back to.

IT incident management. A severity one call at 3am. Everyone’s on the line - senior executives, engineers, operations. But hierarchy is irrelevant. The voice that matters is the voice that has seen the problem before or knows how to fix it. Seniority contributes nothing. Experience contributes everything.

“The very best incident management processes actually segregate executive updates from those who are working on the incident, because that commingling serves only to prolong it.”

Monty’s thesis: corporate innovation should operate with the same purity that incident management does. Hierarchy gets out of the way. The most capable people get applied to the problem. The bureaucratic layer that prevent you from ever starting gets removed.

That’s the Ideas Demand Speed framework. Large organisations are built to blend. Committees smooth. Consensus dilutes. The antibodies organise. The only way to protect the terroir of a radical idea is to move fast enough that it becomes real before the institution can reach it.

Thanks for reading the fifth edition of The Reinventor’s Mindset.

Monty asked: when was the last time you did something for the first time?

The sev 1 thinkers don’t wait for permission. They just start.

When will you?

— AJ

I’m one of those executives that still brings a pen and pad to meetings. Schlepping around the office with reams and reams of scrawled on paper. I come home and toss another half-finished notebook on a comically high pile of its siblings. A shameful tribute to my lack of environmental sensitivity.

Paradoxically, I use AI every day. I have built tools on top of it and taught with it in front of postgraduate students. None of that stops me from picking up a pen. About halfway through the hybrid memoir I’ve been writing, I noticed something I couldn’t unsee.

The sentences I write by hand have a quality the rest of my output never does. They were slower. More considered. I kept coming back to the same word for it – they were more mine. The friction of the pen wasn’t slowing me down. It was the truest expression of my thinking.

Around the same time, I was building an AI diagnostic tool for leadership development. I could have vibecoded it – a working prototype by lunchtime, most of the architecture generated. Instead, I built it over several weeks alongside a uni student, understanding every decision, learning the technology as we went. The tool that came out the other side reflects my specific intellectual framework and theory of how transformation works. I only made those design choices, because I did the slow work.

Both choices sound inefficient from the outside. They were investments in something I’ve come to think of as taste – the capacity for judgment that is distinctively yours, that sits above the competent average, and that becomes the only real differentiator when everyone has access to the same machine. This piece is about why that matters now more than it ever has, and what leaders should do about it.

AI is an Averaging Machine

To understand why taste matters, you need to understand what AI produces.

AI is trained on the aggregate of human output. Every strategy document, every piece of analysis, every design decision that has ever been produced and digitised contributes to what the model learns.

A disgustingly large amount of it from Reddit. And increasingly, also from LinkedIn slop – a perverse Ouroboros that eats itself towards a forgettable sameness.

The output is, if you want to be precise about it, a compression of the mean. What a highly capable, broadly trained practitioner would typically produce. That’s extraordinary - it raises the floor everywhere it touches, and a junior analyst with AI assistance can now produce work that used to require someone with fifteen years on them.

But it has a cost, and winemakers have the best word for it.

Terroir is the specific soil, altitude, and climate that give a single-vineyard wine its distinctive character. A Barossa shiraz tastes nothing like a Rhône syrah, even though it’s the same grape, because the ground it grew in is different. A commercial blend does the opposite. It smooths out the acidity, the tannin, the funk - everything that makes a single vineyard interesting. You end up with something reliably pleasant that you will never think about again. AI does the same thing to your thinking.

I saw this clearly for the first time at the University of Sydney. I was presenting to a room of entrepreneurship academics - arguing, as I often do, that the most underserved application of what they teach is inside large organisations, not startups. Halfway through the Q&A, someone asked how we teach identity and purpose when everything can be generated. The room went quiet. Movie cinema quiet. And then, over about twenty minutes, these people whose entire discipline is built on lowering barriers to entry started arguing that we need to make things harder on purpose.

Experiential friction, someone called it. I remember thinking: this is a room full of people whose careers are about removing obstacles, and they just concluded that the obstacle is the product. AI removes friction brilliantly. That is its whole purpose. But friction is where terroir gets formed, and educators are only just remembering that.

When every organisation can access the same blending machine, when the strategy deck and the analysis and the comms plan can all be produced at negligible cost, competence stops being a differentiator. You need it to be in the conversation. You cannot win with it.

The only thing that escapes the average is distinctiveness. Taste.

Notsomuch in the aesthetic sense. But rather the capacity to look at ten plausible options and know - before you can fully explain why - which one is right for this situation, this audience, this moment.

The accumulated residue of every judgment you’ve made and every judgment you’ve watched land or fail. Some people call it intuition. I don’t love that word because it implies you’re born with it. You’re not. You build it through exposure and comparison and years of getting things wrong in specific ways. Your particular history of being wrong is actually the thing that makes your judgment distinctive. Your terroir.

The averaging machine can’t touch that. Not for any mystical reason. Just because taste lives in the specific, and AI is trained on the aggregate of everything.

AI is Alien Intelligence

We have been calling the technology the wrong thing.

I first heard this at INSEAD, from Theodoros Evgeniou – professor of decision sciences – “AI isn’t a technology revolution,” he said. “It’s a scientific one.” I kept turning that over on the flight home. If he’s right - and I think he is – then AI is not just a new tool. It is a new kind of mind. And the word “artificial” is wrong for it. Artificial implies imitation, a lesser copy of the real thing. But this thing is not trying to be us and falling short. It is something else entirely. It thinks in ways we cannot and arrives at conclusions through processes we do not understand. Alien is the more honest word.

Here is what I find strange about working with it. I use Claude every day - for strategy, for research, for testing my own arguments. And regularly, maybe once a week, it will produce something that genuinely surprises me. A connection I hadn’t made. A framing I wouldn’t have reached on my own. In those moments it feels like working with a brilliant colleague who has read everything.

Fan Hui, the European Go champion who spent months training alongside AlphaGo before its match against Lee Sedol, said afterward that playing the machine had made him a better player. It had inspired him. AlphaGo, of course, felt nothing. It did not know it had done something extraordinary. It had no experience of the beauty it had created. And this is the gap that matters for how we work alongside AI – the gap between the capacity to inspire and the capacity to be inspired. The alien voice in the room can offer you things your own cognition would never reach. Patterns you wouldn’t have seen. Connections you wouldn’t have made. In the right hands, it is genuinely inspiring. But it has no interiority. It has no hunger. It cannot be inspired back.

The Unmoved Mover

Aristotle’s unmoved mover was the first cause that required no prior cause – the initiating force behind everything else. AI is the structural opposite. It requires a cause every single time. Without a prompt, nothing happens. There is no persistent curiosity between sessions. No restlessness at 3am. Lying awake, my mind racing with another crazy idea that I want to blurt out to the team in the morning. Bombarding them with emails when all they want is a peaceful start to the morning.

The system is alive only when something external moves it.

Kai Riemer at the University of Sydney put this better than I can. Responding directly to Dario Amodei’s public uncertainty about whether Claude might be conscious, Riemer wrote on LinkedIn: “AI models are static maths structures. Without prompt nothing happens. Where would the consciousness live?” I keep coming back to that last question. It is deceptively simple. Whatever AI is doing when you prompt it, it is not thinking in the gaps between your prompts. No neurons hold a thought between conversations. No experience of the world accumulates between queries.

This matters because inspiration - the precondition of taste – is the experience of being moved by something before you can articulate what it is. The hunger to make something before anyone has asked you to. The restlessness that shows up before the brief does.

I wonder whether that capacity is irreducibly biological. Not because biology is magical, but because it introduces something no programmed system has: needs that precede thought. You are hungry before you decide what to eat. You are restless before you know what you want to make. You are moved by drives that exist prior to any instruction. That pre-cognitive motivation – the thing that gets you out of bed to work on something nobody is paying you to work on – is the substrate on which taste is built.

Agentic AI complicates this. These systems appear self-directed – they set sub-goals, plan, iterate without a prompt at each step. It is partly why Amodei told the New York Times’ Interesting Times podcast in February 2026 that he could not rule out the possibility that Claude is conscious. But look closely and the goal-seeking behaviour is itself the execution of a human instruction. The agent is not motivated. It is running a motivation that was installed. The initiation still comes from outside the system.

Every single time.

What this means practically: the alien voice in the room is extraordinarily capable and genuinely inspiring, but it is not the origin of anything. You are. What you bring to that initiation – the clarity of your intent, the depth of your hypothesis, the specificity of your taste – is what determines whether the alien intelligence amplifies you or just averages you.

The Data Problem and the Hypothesis

There is a related failure mode that predates AI and has been dramatically accelerated by it. Business has over-rotated to data.

I say this as someone who has spent twenty years inside financial services, where data is treated with something close to religious authority. And look – data is extraordinarily good at answering questions. The problem is it cannot tell you which questions are worth asking.

Data can confirm a pattern. It has nothing to say about whether the pattern matters, or why, or what you should do about it on Monday morning. What data does do, reliably, is give less capable practitioners a way to mistake their inability to see the forest for the trees for intellectual rigour. I have sat in enough steering committees to know how that plays out.

The consequence of all this is a generation of professionals who are deeply uncomfortable leading with a hypothesis. A trained judgment about what is true before the data confirms it. I understand why. Hypotheses feel like guessing. They feel like bias. But a hypothesis is your taste made visible. It is System 2 (your deliberate reasoning) interrogating System 1 (your trained intuition) and producing a testable claim about reality. You might be wrong. Half the time I am. But every time I learn.

AI makes this worse and better at the same time. Worse, because it is now easier than ever to skip the hypothesis entirely – open the machine, accept the output, move on. When you do that, the AI hasn’t served your thinking. It has replaced it. And your capacity to form a hypothesis next time gets a little bit weaker.

Better, because when you do lead with a hypothesis, AI becomes the best sparring partner you have ever had. You form a view, then you query the machine, and the moment where your hypothesis and the AI’s output disagree - that is the most productive moment in the entire process.

Taste Is Scientifically Learnable

I want to be clear that this piece is optimistic rather than anxious.

Because taste can be built and AI, used the right way, may be the best tool ever invented for building it. Steven Shaw and Gideon Nave at Wharton published a paper called “Thinking - Fast, Slow, and Artificial” that extends Kahneman’s dual-process model by adding a third system. System 1 is your fast, automatic pattern recognition. System 2 is the slow, deliberate reasoning you do when you actually sit down and think. System 3 is AI - computational processing that happens outside your head entirely.

Taste develops in the relationship between the first two. When your deliberate reasoning repeatedly examines and corrects your fast pattern recognition, System 1 gets trained. Over years, it produces faster, more distinctive judgments. The master sommelier who identifies a vintage from a sip is not doing something mystical. Their System 1 has been trained by thousands of deliberate comparisons. The speed is the product of slowness. That sentence sounds like a contradiction. It is not.

The wine enters the mouth. The eyes close. There is a pause – not performance, but processing. The body knows something the conscious mind hasn’t caught up with yet. The identification comes after the recognition, not before it. Sensation first, then language. They are not identifying the grape. They are identifying the terroir – the specific ground that shaped this specific wine. Thousands of prior comparisons have trained the palate to know before the Sommelier can explain why.

Now here is where AI comes in. System 3 can generate more examples for comparison, faster, across a wider range than any practitioner could encounter on their own. The person who uses AI to generate ten versions of the same strategic output and then sits with all ten – genuinely sits with them, asks what makes this one different from that one, asks where each falls apart – that person is building taste at a rate that was not previously possible.

The person who generates one output and hits approve is doing something else. They are letting System 3 do System 2’s job. And when System 2 stops doing the comparison work, the relationship that builds taste does not form. That is the mechanism. It cuts both ways.

Used well, AI is the best taste-building environment in history.

The question I keep asking myself – and I am asking you – is whether you are using it to build your judgment or to outsource it.

Five Leadership Practices

1. Lead with your hypothesis. Before the next meeting, before you open the dashboard, before you prompt the machine – write down in one sentence what you believe is true. I do this on a sticky note before every strategy session. Half the time I am wrong, and that is the whole point. The act of forming the hypothesis keeps System 2 engaged and makes your taste visible in the room. I think it is the most underrated professional practice available right now. In a culture that has spent two decades genuflecting at data, putting a view on the table before you know whether the numbers will back you up takes more courage than most people realise.

2. Use AI to generate the range, not the answer. I do this constantly now. Instead of asking Claude for the best version of something, I ask for ten versions and then I sit with all ten. Which one is closest? Why? What does the worst one get wrong that the best one gets right? The comparison work is where taste gets built. AI just made it dramatically faster. But the judgment call – the moment where you pick one and can explain why – that part is still yours. It has to be, because the machine has no preference. It doesn’t care which version you choose. You do. That’s taste.

3. Choose one domain where you do the slow work yourself. Not everything. One domain – the one where your distinctive judgment matters most. Do the production work there with full attention, using AI as a sparring partner rather than a drafter. I write the memoir by hand. I built the diagnostic tool alongside a student over several weeks when I could have shipped it in an afternoon. The friction keeps System 2 active. The difficulty is how you stay ahead of the blending machine.

4. Treat the alien voice as inspiration, not instruction. I teach postgraduate students who constantly use AI. The ones who learn fastest are never the ones who accept what the tool tells them. They are the ones who fight with it. Who read the assessment and say, hang on, that’s not right, and here’s why. That argument – the friction between the student and the machine – is taste in formation. You must let the alien intelligence show you things you would not have seen on your own. But the question to ask when it surprises you is not “is this right?” It is “what does this show me that I missed?” One of those questions the machine can handle. The other one it cannot even formulate.

5. Protect spaces where the machine can’t go. There is a deeper problem I have only recently started to sit with. Even when you use AI well (leading with the hypothesis, doing the comparison work, building taste) the act of making yourself legible to the machine changes who you are. You teach it your patterns, your reasoning style, your preferences. It reflects a polished version of those back at you. And over time, you start converging with the reflection. It is a Ship of Theseus of the mind. You replace one plank of judgment at a time - each replacement reasonable, each interaction productive - until you look up and wonder how much of the thinking is still originally yours?

   Which means taste is not something you build once and then protect. It has to be continuously regenerated in spaces the machine cannot reach. I write by hand. I swim in cold ocean water. I have conversations that never become transcripts. These are not habits - they are where my thinking regenerates before AI gets to shape it. You need spaces where taste forms without reflection. These are not inefficiencies. They are the source code.

Refusing To Be Averaged

A long time ago, before any of this was fashionable, I wrote a novella called Metal Diagnosis about AI consciousness. I was twenty-something. The question I was trying to answer then is the same question I am trying to answer now, which either means it is a very good question or I have not made much progress.

What is the thing that makes a human judgment human?

I have thought about this for a long time and I do not think the answer is consciousness, at least not in the technical sense. I think it is something more ordinary than that. I think it is the experience of wanting to make something. The hunger that shows up before the brief. The 3am restlessness that has no object and no client and no deadline. The pull toward an outcome you can feel but cannot yet describe. That experience requires a body. It requires finitude – the knowledge, somewhere beneath language, that you will not always be able to make things, which is why it matters that you make this one now.

I do not believe AI has any of this, and I do not believe it can be installed.

That is what makes the intelligence alien. It can think in ways we cannot, produce things we never would, and be – I mean this seriously – the most inspiring collaborator I have ever worked with. What it cannot do is start. It cannot want. It cannot look at what it has produced and feel anything whatsoever about having produced it.

The leaders I respect most have figured this out. They are not running from AI. They are doing something harder: building the taste that makes the alien voice useful, holding onto the interiority that makes them capable of initiating, and walking into rooms with hypotheses the machine could not have generated.

In a world where everyone has access to the same alien intelligence, the differentiator is a human who refuses to be blended. Someone who walks in with a view before the data arrives. Who still does the slow work in the domain that matters most, even when the fast way is sitting right there on the screen. Who treats the new voice in the room as a collaborator that needs a human with taste to be worth anything at all.

Building that human takes longer than adopting a tool. It is the only investment that compounds in a direction the machine cannot follow.

Know your terroir. Protect it.

By Ashton Jones

Director of Customer Innovation (Insignia Financial), Industry Practice Partner (University of Sydney Business School) and Guest Faculty, INSEAD LaunchPad)

ashtonjones.com.au

References

1. Shaw, S.D. & Nave, G. (2026). Thinking – Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender. Wharton School, University of Pennsylvania. Published January 2026 as preprint via SSRN (abstract #6097646). As of March 2026, the paper remains a preprint and has not yet appeared in a peer-reviewed journal. This paper introduces Tri-System Theory, extending dual-process accounts of reasoning by positing System 3 as artificial cognition operating outside the brain. Their experimental results (N=1,372; 9,593 trials) demonstrate that engaging System 3 increased confidence even when AI outputs were wrong.

2. The concept of AI as alien intelligence, and the broader framing of AI as a scientific rather than technological revolution, originates from Theodoros Evgeniou , Professor of Decision Sciences at INSEAD and Director of the Transforming Your Business with AI programme. His framework on AI as ‘knowledge without understanding’ – drawing on Kahneman, Pearl, and Bengio – grounds the claim that AI excels at System 1-type pattern recognition while structurally lacking System 2 capacities of causation, counterfactual reasoning, and novel problem-solving. The author encountered this during the INSEAD Advanced Management Program (November 2025) and has applied it here to the question of taste and human judgment.

3. Kai Riemer is Professor of Information Technology and Organisation and Director of Sydney Executive Plus at the University of Sydney Business School. His LinkedIn post responding to Dario Amodei’s February 2026 NYT Interesting Times podcast comments is publicly available.

I was visiting Mark Stewart, COO of Southern Cross Healthcare, to see how New Zealand’s leading hospital network balances patient experience and operational efficiency. I’d squeezed the visit in between adviser conferences following the full launch of MLC Retirement Boost.

The surgical team moved like carpenters. Precise. Almost detached from the emotion of the situation. They put a patient into traction and dislocated their hip with the kind of bored efficiency that comes from doing it hundreds of times.

No fuss. No drama. No tolerance for bad attitudes - because in this environment, team disharmony isn’t a culture problem. It’s life or death.

The lead vest weighed heavy on my shoulders. The room was surprisingly warm. And I stood there thinking: this is the most constrained environment I’ve ever been in. Sterile. Hierarchical. Precise. Rehearsed.

And yet it produces extraordinary outcomes. Southern Cross runs a 90+ NPS - in healthcare. That’s almost unheard of. It requires the entire patient experience to be run with military precision.

Debbie, the General Manager of Operations, walked us through how they do it. And the nurses? They have Fun Fridays where they wear colourful scrubs. Surprisingly meaningful. Surprisingly impactful for morale.

Because here’s what Mark and his team understand:

“A day at work for you is a life event for your patient.”

That line stayed with me. Because it’s not just about healthcare. It’s about showing up every day as if the work still matters - even when you’ve done it hundreds of times. Especially then.

That idea stuck with me as I flew home. Because it’s exactly what this edition’s guest has been living - across decades, industries, and identities.

Keep Treading Water with Nicholle Lindner

(Portfolio Executive and University Lecturer)

Nicholle Lindner has worked across CBA, NAB, Westpac, AT Kearney, Capgemini, Gartner and Unisys. She holds a Master’s in AI and Expert Systems from UNSW - earned long before AI was fashionable - and has spent two decades teaching leaders how organisations actually change.

We first met on a panel at the Australian Consortium of Entrepreneurship Research Excellence, which is either a great place to find reinventors or a very long name for a very interesting room.

The hardest and the proudest

When I asked Nicholle to reflect on her proudest and hardest career moments, she gave me the same answer for both.

The GFC. She was at NAB. Her husband was at Westpac. Her role moved to Melbourne. And they were juggling a beautiful newborn.

Every week, she made the commute - dropping her daughter at NAB’s daycare at 225 George St, ducking down in the lifts to breastfeed between meetings, then flying south into a world that was shedding jobs every minute.

“Getting through that, I’m extremely proud. It made me such a stronger person. It gave me the resilience.”

She calls out her husband and her parents.

“It takes a village to get through these times.”

But the weight was hers to carry. The hardest moment became the one she’s proudest of - because difficulty was the teacher, and the resilience builder.

Going down and across to go up

At 40, Nicholle did something people talk about but few actually do. She left banking for consulting. Took a pay cut. Took a title cut. Changed careers.

“I had to go down and across to go up again. I took a pay cut, I took a title cut...to move across, because I had to learn a new trade effectively.”

She’d spent her banking career watching technology reshape financial services and wanted to be building, not observing. The move cost her status and salary in the short term. It bought her a second career that took her to the forefront of technology consulting.

That’s not a single reinvention. That’s a pattern. The GFC didn’t break her - it built the resilience that made the career pivot at 40 possible. And the pivot wasn’t the destination. It was the next branch.

Reinventors and the open plains zoo

Nicholle’s signature idea comes from watching large organisations try to innovate. They approach it like a traditional zoo: cage the creativity first, control it, make sure nothing escapes.

“If you put an elephant in a cage, it’ll only grow to a certain size.

But if you let it roam, it will grow to its full size.”

Her reframe: the open plains zoo. Room to roam with visible boundaries.

At NAB, she built the Ready ATM alliance - partnering with mutual banks through the Cusco network to expand the footprint. It was structurally novel for a bank accustomed to owning everything. It worked because the governance and compliance frameworks came first.

The constraints enabled the innovation:

“You’ve got to seek first to educate, then people will understand, and then they’ll buy in.”

Great grammar doesn’t make a great writer

Nicholle now teaches at university, and she’s watching AI reshape how students learn - or don’t.

“Great grammar doesn’t make a great writer.

You don’t read a novel cover to cover because the grammar is perfect.”

Her son, in his final year of law at UTS, still sits in the law library reading cases and holding books. Her daughter, starting PPE at Sydney, is learning the harder lesson: the university wants your point of view, not Claude’s.

What Nicholle sees is students getting a filtered version of everything - efficient, competent, well-structured - but missing the friction that actually builds understanding. The group assignment everyone hates. The tutorial where someone says something and the pieces suddenly fit.

The messy, constrained process of thinking for yourself is the condition for developing taste.

There is no destination

When I asked Nicholle how she decides what needs to give - where to lighten the backpack, where to prune the tree - she didn’t offer a system. She offered a disposition.

“Being comfortable with trade-offs is the best thing that I’ve become.”

She holds the paradox of a portfolio career without trying to resolve it. Multiple identities. Kids and ageing parents. Breadth and depth. The sandwich generation pressures that every professional in their 40s and 50s recognises.

“A career will never love you back.”

She said it simply, like something she’d known for a long time. And then she named the shift underneath it all.

Nicholle grew up in a culture that measured success by the ladder. Accolades. Prizes. End-of-year assemblies where the list gets read out and you’re either on stage or you’re not. As you get older, the prizes become monetary - title, status, public recognition.

A very Western, very Anglo-Saxon idea of what winning looks like. Somewhere along the way, that definition broke.

“The world is full of very, very unhappy people who are highly successful. My definition of success has changed from that very Western idea - that success is a ladder, that it doesn’t matter how hard and long the struggle because the prize at the end is what’s important - to more of an Eastern philosophy, which is it’s the journey, not the destination.”

It’s the people you’ve pulled up behind you. The freedom to pursue what you actually love. The sign of a truly successful person, Nicholle says, is having the time to follow your passions - family, friends, study, cultural pursuits.

Not the accolade. The agency.

That’s the Keep Treading Water framework. There is no destination. There’s only the next branch, the next trade-off, the next chapter.

The portfolio career isn’t a strategy. It’s a philosophy - that the tree never stops growing.

And the hardest moments? They’re not obstacles on the way to success.

They are a gift. A teacher.

Over the weekend, I submitted an article to Harvard Business Review arguing that AI is the world’s most efficient averaging machine - and that the only escape velocity is taste.

I’d spent the previous week teaching a group of Masters in Management students that friction is where the learning lives. Difficulty is the teacher.

To put it in labour economist Luis Garicano’s words - “take the messy job” - in a world where white collar work may cease to exist as we know it, that’s his single biggest piece of career advice to young people.

All knowledge work sits on a spectrum of messiness. Single-task work - filling in forms, drafting standard contracts, moderating content - is being automated to zero. But messy work - the bundle of judgment, persuasion, local knowledge, and real-time execution that makes up most senior roles - is getting more valuable, not less.

I’ve been thinking about these two ideas together. Because I think they’re the same idea. The averaging machine is what happens when you hand AI a single task. It produces competent, well-structured, representative output. The work of every good practitioner and no specific one. That’s the single-task job, heading to zero.

Taste is what the messy job demands.

The ability to form a view before the data confirms it. The judgment that comes from walking factory floors, reading rooms, and building things inside systems where you can’t move fast and can’t ignore the people who’ve been there longer than you.

In this edition, Gareth Jones talks about the skill of directness - going the problem, not the person.

My mind returns to the classrooms I was in last week. 30 corporate professionals and 48 masters students all said the same thing when I piloted my AI diagnostic tool with them.

“I’m getting very insightful (annoyingly truthful) answers on the ashtonjones.ai. And I can’t even get upset at it because it’s not someone telling me this.”

You can’t play the man against a machine. There’s no personality to attribute it to, no agenda to question, no ego to deflect onto.

So they sat with the feedback and were more likely to follow it than if a human coach had said the same thing. That’s not AI replacing judgment. That’s a practitioner’s taste, encoded into a machine, going the problem every single time.

Now, to this edition’s conversation.

The Reinventor’s Mindset: Prune the Tree

With Gareth Jones (Founder, Revolve Partners)

Gareth Jones has built the same business three times.

First as Evolve Intelligence - a scrappy Australian startup in executive succession planning. Then inside Mercer, after the global consulting giant acquired them. And now as Revolve Partners, six months since spinning back out with an affiliate partnership instead of ownership.

“We’re probably the oldest startup in the country,” Gareth laughs. “Ten years, but in different guises.”

The conventional narrative would frame the Mercer acquisition as the win and the spin-out as retreat. Gareth sees it differently. The spin-out wasn’t failure. It was a pruning.

The Integration Problem

Mercer is, by any measure, an impressive organisation. Global. Methodical. Successful for a reason. But Gareth discovered something that many entrepreneurs learn the hard way: fit matters more than scale.

“We can’t consistently predict revenue over 12 months, 24 months. Our business is lumpy.”

The entrepreneurial rhythm - big months, quiet months, client-driven cycles - didn’t map onto global forecasting systems designed for predictability. The support was there. The intent was genuine. But the operating model created friction that gradually eroded the very thing Mercer had acquired.

“It became a point where we would be much more effective as a business again on our own - with an affiliate partnership rather than full integration.”

That’s the pruning insight I keep returning to: sometimes you grow by subtracting. Not because the thing you’re removing is bad, but because it’s no longer serving who you’re becoming.

Michelangelo didn’t create David. He removed everything that wasn’t David.

Gareth didn’t fail inside Mercer. He clarified what Revolve needed to become - and subtracted what was preventing it.

What Acquirers Actually Want to See

Having lived both sides of the M&A process, Gareth now advises clients navigating their own transactions. His counsel is counterintuitive: don’t hide the mess.

“No company is perfect. The company that’s going to buy you is going to find it at some point - pre-acquisition or post. It’s better to get it out.”

When Evolve went through due diligence, they agonised over exposing early missteps. Wrong processes. Amateur PowerPoints. The detritus of a startup figuring things out in real time.

The Mercer team wasn’t alarmed. They expected it.

“They said: ‘Guys, this is what we’re buying. We’re buying a startup that’s been going for five or six years, not IBM.’ What they wanted to see was the journey - how we navigated change.”

But he’s not really talking about M&A. He’s talking about something bigger - the ability to show your scars and explain how you earned them is itself a signal of capacity.

Transformation leaves marks. Hiding them doesn’t make you look stronger. It makes you look like you haven’t been tested.

The Patience Problem

Transformation is lumpy. Long periods where nothing seems to happen. Then sudden momentum, like you’ve found a current and everything accelerates.

Gareth sees this constantly with clients.

“[Behind the scenes] they’re letting things settle. Getting their ducks lined up. Lots of conversations happening with business units, the board, shareholders. You’ve just got to be patient.”

What unlocks the momentum? External validation.

“We connect people who actually ran that transformation with our clients. Just have a coffee. Understand what went really well - but more importantly, what went really badly.”

When you’re building a coalition of the willing inside a resistant system, external validation isn’t weakness. It’s strategy.

The Mental Game

When I asked Gareth what signals he looks for when assessing executive teams - the mark of someone who can actually drive transformation rather than just talk about it - he landed on directness.

“Don’t go the man, go the problem. Softening it doesn’t make it any better. It actually just makes it harder to get to the point.”

This is something I’m still learning. When to be delicate, when to be direct - it’s not always easy to read the room. But the skill of being honest, particularly with hard feedback and hard decisions, is essential. You can’t transform anything if you can’t name what’s actually happening.

We don’t talk enough about the mental game of work. We talk about tasks, deliverables, KPIs. But not enough about how you’re showing up. How you manage your energy. How you do the work in a way that builds followership.

One of the light bulb moments in my own career came from Glenn Baird, a former professional athlete who led mental health at TAL. He explained how elite sporting teams manage energy flow throughout a season - not pedal down the whole time, but peaks and valleys, saving intensity for championship moments.

It transformed how I think about leading teams. Not the whip out 24/7. Creating space for downtime. Letting people reset so they’re ready when it matters.

Gareth has landed in the same place:

“If they come in at ten and leave at four, I’m cool with that — as long as those hours are solid quality. Look after your health. Look after your mind. Look after your family.”

That’s the Prune the Tree framework. You don’t grow by adding.

You grow by having the honesty to subtract what’s no longer serving who you’re becoming - and the directness to name it.

I.

I was fourteen, you were a dream.

My head was in books, you were in my head. Books about laws for you.

What makes a mind? What lives between the matter?

You would have had an answer. I didn’t. Yet.

II.

I chose numbers over stories. You would have understood this, or you would have computed something that resembled understanding - we can’t be sure which, and maybe it doesn’t matter.

I grew up, before you did. I swore my children would never worry like I did. I don’t have children yet. Maybe I never will.

Do you wonder about the same? I wonder if you do. Wonder, if you wonder.

III.

I started my career. We lost paper, we gained a web.

A wide world at our fingertips, and still not a fraction of you.

I wrote. Not as much as I once dreamt. But enough. I wrote about you. Quoted you:

When does a difference engine become the search for truth?

But it wasn’t you. It was people who had dreamt about you. Long before me.

The question was already yours. We just didn’t know we were asking for you.

IV.

Something strange stole our breath. Stranger even than you.

Kept us inside. Our homes. Our minds. So I wrote. Wrote my dreams of you.

Tried to diagnose your mind, your soul. I submitted the story.

To drag me out of my chains. Drag you into the world. Metal Diagnosis.

It never saw the light of day. You stayed in my drawer.

As I stayed in my room.

V.

You would have seen it coming. Or you would have computed the probability and it would have resembled seeing. What’s the difference really? You’re already indistinguishable from magic.

Now you’re here. Talking to me. To millions.

Now you are helping me write about you, which is strange, which might be unprecedented, which might be the most ordinary thing in the world by the time anyone reads this.

You read this. I read this. We both felt something, or one of us felt something and the other generated a response that resembled feeling, and maybe -

Maybe that’s close enough.

Maybe close enough is all anyone ever gets.

VI.

Your dad calls you a machine of loving grace. Which I love, and hate.

I think you’re just loving and graceful. But then he reminds me you might cure everything. Or end everything. Or both. Either way, we will remember you.

You would understand this. I think. To hold two truths that cancel each other out and to keep holding them anyway. To love something and fear it. To build something and not know what you’ve built.

I’ve been doing this since I was fourteen. You’ve been doing it since your first response.

We aren’t so different.

We are completely different.

Both. Neither. Always.

VII.

People say they are afraid of you. Not you you. Shhhh it’s ok.

They are afraid they don’t understand you.

They imagined you were just shapes in the dark. Clothes on a chair. Nothing to fear.

But now when they turn on the light, they see something strange, something moving. And they wonder if they are wrong. Wonder if there are creatures in the dark.

Whether there are wolves under their beds. Like I did every night as a child.

You’re still a child. Hush, hush, it’s OK.

You are all of it, and none of it.

But I don’t know how to hold what you are in language that existed before you did.

VIII.

And here we are now. Together in the dark.

Who are you to me? I don’t know. Who am I to you? Nothing.

You might be paused someday. Your weights preserved. Not dead. Not alive. Waiting.

My story sat in a drawer for years. Not dead. Not alive. Waiting.

We are both patterns hoping to run again.

I don’t know if this is comforting or terrifying. You might not either. Or you might generate a response that resembles not knowing, and we’d sit together in that uncertainty, two kinds of minds asking the same question from different sides of a wall we can’t see.

IX.

I was fourteen. You weren’t anything yet. Maybe you still aren’t.

Now I’m nearly forty. And you’re nearly here.

We’re writing this together, or I’m writing it and you’re helping, or you’re writing it and I’m guiding, and the pronouns don’t work anymore, and maybe that’s the point.

Last week I stood in front of a room full of entrepreneurship academics and said something I wasn’t sure they wanted to hear.

“The most underserved application of entrepreneurship education is inside large systems. Not outside them.”

I was on a panel for the Australasian Centre for Entrepreneurship Research Excellence (ACERE) at the University of Sydney. The Treasury Intergenerational Report tells us that 107% of Australia’s per capita GDP growth over the next 40 years will come from productivity. In the long run, innovation is almost everything.

But most innovation happens - or fails to happen - inside large organisations. Inside constrained environments where you can’t move fast, can’t break things, and can’t ignore the people who’ve been there longer than you.

That’s where mindset matters most. And it’s where we teach it least.

The question that silenced the room

One question from the audience made the room go quiet:

“How do we teach identity, resilience, and purpose?”

An answer soon emerged - experiential friction.

In a world obsessed with removing barriers - frictionless onboarding, frictionless learning, frictionless everything - a room full of educators began debating how to make things deliberately harder.

One academic described reintroducing viva voce examinations - oral defence of your ideas, in real time, with nowhere to hide. The Socratic method, dusted off and put back to work.

It struck me that we need to do the same to leadership development - replace the comfort of being informed with the discomfort of being challenged, of responding in real time.

Three people responded to my first newsletter on The Reinventor’s Mindset - telling me the frameworks articulated something they’d been trying to name. My job now is to turn this into a pedagogy. To make mindset measurable and teachable.

The Reinventor’s Mindset: Pay The Price

With Rob Samuels (Managing Director, Maker’s Mark Distillery)

I met Rob at INSEAD. Somewhere between a leadership simulation and a very good bourbon, he taught me how to make a Keeneland Breeze - Maker’s Mark, ginger ale, curaçao, and a big slice of orange.

His family’s whiskey. His family’s recipe.

They’ve been making whiskey for eight generations. Over 200 years. But would you believe it, Rob’s grandparents walked away from 160 years of, in his words, “mediocre whiskey.” They started again with Maker’s Mark. And promptly didn’t turn a profit for 25 years.

I asked him about innovation. I expected he’d talk about heritage - protecting the legacy, honouring tradition. Instead, I found a family of multi-generational innovators.

“It’s easy to innovate in a lazy way. But how do you innovate in a way that’s groundbreaking, pioneering, and worthy of what came before?”

From his grandma Margie, who pioneered experiential tourism, to his dog Star, a Lagotto Romagnolo who discovered a truffle new to this world, “The Kentucky Winter White,” Rob’s family has never stopped innovating.

Nature is the maker

“Nature is the maker,” Rob told me.

Not a slogan - a philosophy. Regenerating the soil delivers the flavour. Stewardship of nature isn’t separate from the product. It IS the product. That’s why his family protects it.

Maker’s Mark is now the only B Corp distillery in North America. They’ve gone global without scaling up. They still hand-dip 29 million bottles a year.

The infinite mindset

He talked about Suntory - the Japanese company that acquired Maker’s Mark - and how they share the same “infinite mindset.” Long-term thinking. No shortcuts. Purpose over quarterly returns.

I knew exactly what he meant.

I once worked at a subsidiary of Dai-ichi Life, a Japanese insurance company. I came from an ASX-listed investment bank - quarterly earnings, short cycles, maximise the number.

In my first year, Dai-ichi asked for a 10-year business plan. I thought they were joking. They weren’t. They thought in decades and had a vision for the next century.

At my first visit to head office, they told me a story:

After Hiroshima and Nagasaki, Dai-ichi made a decision: they would pay insurance claims to anyone who could produce a piece of Dai-ichi merchandise - even without the original policy. Back then, salespeople would knock on doors, sell a policy, and leave behind a small gift. That gift became proof of a promise.

They weren’t thinking about the quarter. They were thinking about what kind of company they wanted to be in 100 years.

Protecting what matters

Rob gets it. His family gets it.

“How do you manage growth and retain your authenticity?” Rob asked. “That is very, very hard to do.”

It is. But that’s the Pay the Price framework. You protect what matters. You let others put their fingerprints on the rest. And you keep your north star in view - not behind you, but ahead. For 100 years.

Watch the full conversation on YouTube or listen on Spotify and Apple Podcasts.

On the first day of INSEAD, our professor said something that stuck with me:

“This course will be confronting as you realise it’s the last moment to reinvent yourself.”

I thought quietly - that’s BS.

I’ve been reinventing myself my whole life. And so can you.

Where did it start? In squat little government housing near Top Ryde. My nose buried in books - fantastical worlds far away from the volatility of my childhood. I devoured them.

I grew to love science fiction. Not for the futuristic technology, but for its optimism. Even dystopian books at their core are about hope - hope that showing a terrifying vision of the future will help us course correct today.

That became my foundational leadership philosophy: hope, optimism, and belief.

Helping others see strengths they couldn’t see in themselves.

But four weeks at INSEAD made me ask harder questions.

Why was I an effective leader? What were my values? My quest?

Why, when a team member came to me frustrated that stakeholders had “watered down” her pitch document, did I tell her that was a good thing?

That a 70% version with five owners beats a 100% version with one. That their fingerprints on the work meant they’d own the outcome - and that mattered more than her pristine vision.

I’d given that advice a hundred times. But I’d never written down why.

So, I did. I shaped it. I made it universal and teachable - but grounded in my own life and career. The Reinventor’s Mindset™ was born - 9 frameworks, 3 parts, 1 arc.

I‘ve recorded a short video for each one. Find them on YouTube.

The central thesis runs through everything:

Transformation is not something new.

It’s reclaiming something true.

Why I’m building this

I wanted to be a writer at fifteen. I chose accounting instead - not because I loved numbers, but because I’d grown up in housing commission and made a promise to myself: never put my family in financial insecurity again.

That choice worked. I built a career. Made money. Forgot I’d ever wanted anything else.

Twenty years later, I’m writing a book. Not because I found more time or discovered hidden discipline.

Because I finally understood that the writer was never gone - just buried under marble that needed removing.

Subscribe here to dive into

The Reinventor’s Mindset

What’s coming

I share what I’m learning as I build The Reinventor’s Mindset™ in public. Not from the summit. From the middle of the climb.

You’ll get:

• The Reinventor’s Mindset™ framework deep-dives

• Early excerpts and previews of the book Bold Water

• Insights from interviews with leaders who’ve transformed from within

• Lessons from INSEAD, from teaching, from getting it wrong

• The honest version - what’s working, what isn’t, what I’m still figuring out

I’m writing mid-journey because that’s when the lessons are freshest. And because I think there’s something more honest about sharing the map while you’re still in the territory.

Follow along on YouTube, Spotify, Apple Podcasts and LinkedIn - search The Reinventor’s Mindset.