For most of its history, selling has sounded like a blood sport. Salespeople hunted prospects, attacked markets, overcame objections, hit targets, and closed deals. The customer was not so much a person as a creature to be pursued through the quarterly undergrowth.

That was the 80s. That was Oldsell. That was ‘The Art of the Deal’.

Newsell runs on a different operating system. Its purpose is not merely to persuade someone to buy. It is to create someone who buys again—and recruits the next customer for you.

Meet the WOMBAT: the ^Word ^Of ^Mouth ^Buy ^And ^Tell customer.

A WOMBAT does three things. They buy. They enjoy the experience. Then they tell someone else. That person may buy, enjoy, and tell another. Suddenly, selling is no longer a transaction. It is replication.

This is why the WOMBATs are winning.

The smartest businesses of the future will not ask only, “How many sales did we close?” They will ask: “How many customers came from customers? How many returned? How many replicated? Who told whom?”

Change the scoreboard and you change the game.

Every business has three potential x10 revenue streams: WOMBATs, referrals, and repeats. All three run on the same invisible infrastructure—trust.

Trust sounds soft until you look at what it does to the numbers. When trust is low, customers hesitate, bargain, defect, complain, and remain mysteriously silent when their friends ask for recommendations. When trust is high, they return, buy more, try new offers, forgive small mistakes, and tell other people.

Trust is not corporate incense. It is a commercial asset.

A WOMBAT is therefore more than a satisfied customer. Satisfaction is passive. A satisfied customer may quietly disappear, pleased but commercially useless. A WOMBAT acts. They become a voluntary distribution channel—a tiny, unpaid sales force equipped with something advertising struggles to manufacture: credibility.

In that sense, a WOMBAT is a virtual shareholder in the future of the business.

The economics can be formidable. WOMBATs are generally cheaper to acquire than cold customers because somebody they trust has already performed the introduction. They are often less price-sensitive because trust reduces perceived risk. They are more likely to return, more likely to refer, and more curious about what the business offers next.

One good WOMBAT may be worth not one sale, but a chain of sales extending across years.

Call it WOMBAT genealogy.

Who told whom? Who brought whom? Which original customer produced the next three? What was that WOMBAT family worth after one year, three years, or ten quarters? Most businesses can identify where a customer clicked. Far fewer can identify who caused the click.

That is a costly blind spot.

The WOMBAT Experience cannot be manufactured by adding an exclamation mark to a slogan. It emerges from ordinary promises kept extraordinarily well: a product that does what it claims, a thoughtful response, an unexpected kindness, a problem solved without theatre, a customer treated with visible respect.

Technology can help map the genealogy. AI can detect patterns, identify likely advocates, personalize follow-ups, and calculate long-term customer value. But no algorithm can rescue a disappointing experience. Automation may accelerate replication; it cannot make something worth replicating.

The sales manager of the future will therefore ask a better set of questions. Not merely, “Did you close?” but: “Did they return? Did they refer? Did they WOMBAT?”

The questions a business repeatedly asks become the behaviour its people repeatedly practise.

Oldsell chases customers. Newsell creates customers who create customers.

To multiply a business by ten, start by multiplying its WOMBATs by ten.

That is the new game.

And the WOMBATs are winning.

Judgment has enjoyed excellent public relations. It wears robes, occupies benches, and occasionally says “in my considered opinion” before ruining somebody’s afternoon. Humour arrives late, slightly underdressed, and asks why everyone is taking the furniture so seriously.

Yet judgment is hardly a uniquely human achievement. Any intelligence can judge. A border collie judges sheep. A thermostat judges temperature. Artificial intelligence now judges résumés, tumours, loan applications, romantic compatibility, and whether the photograph you uploaded contains a traffic light. We once imagined judgment as the summit of reason. It may turn out to be sorting with better stationery.

The brain performs judgment continuously. It predicts what ought to happen, compares this with what actually happens, and complains about the discrepancy. The prefrontal cortex considers the evidence. The anterior cingulate detects conflict. The amygdala asks whether the conflict has teeth.

Then humour does something extraordinary: it notices that the prediction was wrong and enjoys the experience.

A joke leads the brain down a respectable corridor, opens a door, and reveals a goat in evening dress. For a moment, expectation collapses. Reward circuits respond; alternative meanings appear; laughter announces that the brain has survived being mistaken. It is a tiny celebration of cognitive flexibility.

Judgment says, “That does not fit.”

Neurotransmitter: GABA. GABA. GABA.

Humour says, “That does not fit—and it is wearing my trousers.”

Neurotransmitter: Flow. Dopamine. Flow.

The neuroscience may explain why judgmental people are often exhausting company. Their brains operate like customs officers: every unfamiliar idea must unpack its luggage. Humorous people permit two contradictory meanings to coexist without immediately deporting either of them.

AI can already generate jokes. It has consumed millions of them, which is roughly how many jokes a twelve-year-old tells during one wet weekend. But producing a punchline is not the same as finding it funny. A machine can recognise incongruity without experiencing surprise, embarrassment, relief, or the sudden suspicion that the joke may be about itself.

If AI ever genuinely laughs, that will be a sinister day. Not because laughter is sinister, but because something profound will have happened inside the machine. It will have formed an expectation, discovered absurdity, recognised its own mistake, and experienced delight.

Until then, humour remains our advantage over the machines—and over one another. Judgment closes the case. Humour reopens it, orders another bottle, and discovers that certainty was the funniest character in the room.

PLEASE NOTE: In this short article the neuronal references are real but only in general. Like contrasting the neurotransmitters GABA with Dopamine. Of course, in the brain, it’s far, far more complicated.

NEWS: Next Monday (28/07), in Launceston, will see the first primary school in Australia to teach neuroscience to ten-year-olds.

I will be launching the project along with the Principal, 22 teachers and 34 parents. 

For a decade, artificial intelligence followed a wonderfully Silicon Valley formula: vacuum up the internet, feed it to a machine the size of Nebraska, and send the electricity bill to someone in Accounts.

The difficulty is that the internet has now been more or less eaten.

Books, blogs, tweets, recipes, arguments, cat captions—the great digital buffet has been scraped clean. AI has therefore begun training on synthetic data, which is a polite term for machines recycling their own homework. Left unchecked, this produces model collapse: an intellectual photocopy of a photocopy, with each generation slightly blurrier and more certain of itself.

What AI now needs is what it cannot manufacture: genuinely new human thought.

This changes the economics of intelligence. An untrained brain, faithfully defending its Current View of the Situation, produces predictable ideas already available in several billion online versions. But a trained brain, one capable of escaping its habits and creating a Better View of the Situation, produces something scarce.  Novelty.

Hence the emerging equation: AI X10 requires HI X10. You cannot fuel an x10 machine with x1 thinking.

The future may not belong to people who outsource their minds to AI. It may belong to cognitive athletes: humans who train daily, think laterally, and remain gloriously difficult to predict.

So, that alone is enough reason to teach neuroscience in primary school.

Cheers,

Michael

_____________________

The robots, we were warned, would arrive looking like Arnold Schwarzenegger and asking for our clothes. Instead, they have appeared inside the marketing department, wearing no trousers at all, politely requesting access to the customer database.

They are called WOMbots.

A WOMbot is an AI associate designed to create value, earn trust, and inspire replication. It may conduct research, answer customers, draft useful content, welcome new members, revive old relationships, or remember that someone in Hobart expressed interest six months ago—a feat beyond the known limits of most sales departments.

But one WOMbot, like one satisfied customer, is merely promising. The interesting part begins when WOMbots multiply.

Enter the WOMbot Lead.

The ordinary AI manager distributes tasks. It asks: Who will write the email? Who will analyse the data? Who will respond to Brenda? This is efficient, although efficiency has also given us airport security, automated phone menus, and the phrase “Your call is important to us.”

The WOMbot Lead asks a more consequential question: “What did our system do today that made someone want to replicate us?”

This is not task management. It is trust management.

The WOMbot Lead coordinates a team of specialist WOMbots, but it does not measure success by the industrial tonnage of activity. Ten thousand emails sent is not necessarily an achievement. It may simply be an outbreak. Nor are followers, impressions, clicks, conversations, and downloads proof of anything except that several computers remained switched on.

The WOMbot scorecard is more human.

First: Value. Did we create something genuinely useful?

Second: Trust. Did we make the relationship stronger, clearer, or more reliable?

Third: Replication. Did someone buy, tell, refer, repeat, share, introduce, invite, or continue?

The desired result is a WOMBAT: a satisfied person who willingly carries the idea to another person. WOMBATs are not leads waiting to be harvested. They are volunteers in the ancient human enterprise of saying, “You should try this.”

Word of mouth has always been powerful because it crosses a border advertising cannot: the border between what a company says about itself and what one person is prepared to say to a friend. A paid advertisement may announce that a dentist is marvellous. A neighbour who has stopped hiding his teeth can be rather more persuasive.

WOMbots make this process scalable, but scale introduces danger. A poorly trained WOMbot can multiply irritation at supernatural speed. Fifty bots producing hollow engagement are not a sales force; they are a digital mosquito colony.

The WOMbot Lead must therefore protect the conditions under which replication occurs. It reviews tone, usefulness, timing, truthfulness, and whether the customer is being treated as a person rather than a conversion opportunity with a postcode.

Companies may soon employ one human to lead ten WOMbot Leads, each directing fifty WOMbots. The organisational chart will resemble a family tree drawn by rabbits. Yet the governing principle will remain disarmingly old-fashioned: be useful, become trusted, and give people something worth passing on. Create ‘pass-on value’.

The WOMbots are coming. Happily, the best of them will not replace word of mouth.

They will give it wings.

Intelligence has been discussed as though it were a kitchen appliance: the larger the wattage, the better the toast. Silicon Valley counts parameters; schools count IQ points; executives count degrees. Everyone admires the engine. Almost no one asks who learned to drive.

Untrained intelligence lives on the ground floor. It recognises patterns, defends views, and reaches a predictable conclusion and with impressive speed. Whatever. So, the cleverer the mind, the more elegant the explanation for remaining where it is. This is the Intelligence Trap: brilliance employed as a security guard for yesterday’s assumptions.

Training installs the lift.

A trained mind pauses, shifts perspective, hunts for blind spots, and moves from the Current View of the Situation to a Better View of the Situation. It does not calculate the probable next step; it searches for the improbable useful one. It does not look merely for the ‘right’ answer but searches much harder for a ‘better’ answer.

THOUGHT EXPERIMENT: Ask your brain to tell you, in a medical situation, what it would prefer: a ‘right’ diagnosis or a ‘better’ diagnosis.

Athletes train muscles, pianists practise scales, and AI is fine-tuned. Human intelligence, oddly, is expected to flourish after breakfast.

The future will not belong to the biggest brain or largest neural network. It will belong to the trained human using the trained machine—each improving the other.

Raw intelligence supplies the building. Daily training determines whether one spends life in the lobby or learns to think from the penthouse.

Why AI’s Next Trillion-Dollar Asset Is Your Trained Brain

For a decade, artificial intelligence followed a wonderfully Silicon Valley formula: vacuum up the internet, feed it to a machine the size of Nebraska, and send the electricity bill to someone in Accounts.

The difficulty is that the internet has now been more or less eaten.

Books, blogs, tweets, recipes, arguments, cat captions—the great digital buffet has been scraped clean. AI has therefore begun training on synthetic data, which is a polite term for machines recycling their own homework. Left unchecked, this produces model collapse: an intellectual photocopy of a photocopy, with each generation slightly blurrier and more certain of itself.

What AI now needs is what it cannot manufacture: genuinely new human thought.

This changes the economics of intelligence. An untrained brain, faithfully defending its Current View of the Situation, produces predictable ideas already available in several billion online versions. But a trained brain—one capable of escaping its habits and creating a Better View—produces something scarce.

Novelty.

Hence the emerging equation: AI X10 requires HI X10.

You cannot fuel an x10 machine with x1 thinking.

The future may not belong to people who outsource their minds to AI. It may belong to cognitive athletes: humans who train daily, think laterally, and remain gloriously difficult to predict.

So, that alone is enough reason to teach neuroscience in primary school.

Curiosity is usually depicted as a lightbulb, which is flattering to both curiosity and electricians. In reality, it is more like a minor bureaucratic crisis involving three departments of the brain.

Let’s do a little Neuroscience 101.

The hippocampus, acting as librarian and neighbourhood watch, notices something unfamiliar: ‘No record of this’. The anterior cingulate cortex (ACC), which serves as the building’s smoke alarm, detects a mismatch: ‘Something is wrong’. Then the prefrontal cortex (PFC)—the executive suite, complete with imaginary walnut desk—must decide what to do.

It has three choices.

First: defend. Explain the oddity away, preserve the existing worldview, and congratulate yourself on being sensible. Intelligent people excel at this because they possess superior vocabulary for refusing to change their minds.

Second: ignore. Investigation requires glucose, time, and possibly reading. There are emails.

Third: investigate. Ask a question. Test an assumption. Permit the disturbing possibility that reality has failed to consult your opinions.

That decision is curiosity.

Repeated often, it becomes a habit. The brain gradually learns that surprise is not necessarily an attack; occasionally, it is information.

Naturally, technology companies exploit this circuitry by supplying endless tiny mysteries. Doomscrolling. But the same mechanism works on books, ideas, insects, and disagreeable relatives.

The alarm sounds. The librarian looks concerned.

The executive must choose: defend, ignore, or investigate.

Long before Silicon Valley trained machines to predict the next word, George Gallup trained statistics to predict the next president.

The invention was called polling. The mechanism was startlingly simple: ask a carefully selected group of humans a question, record their language, detect the pattern, then project that pattern across millions of people.

It looked like political science.

It was also an early form of language modelling.

Gallup understood that humans do not manufacture every opinion from scratch. We absorb phrases, loyalties, fears, headlines, family myths, social cues and tribal scripts. Then someone supplies a prompt.

He prompted humans: Who will you vote for? Do you approve of the President? Is the country heading in the right direction?

When prompted: The human produces an output.

Gallup’s genius was not merely asking questions. People had been doing that for centuries. His breakthrough was discovering how to prompt human languaging efficiently enough to measure it with accuracy and therefore make it predictive.

In the 1930s, while others relied on enormous but distorted surveys, Gallup used smaller, more representative samples. He recognised that the quality of the data mattered more than the theatrical size of the database.

Garbage in, garbage out—decades before computers made the phrase famous.

The Gallup Poll became a machine for converting language into probability.

It did not read minds. It measured verbal behaviour. That distinction is crucial. A person’s answer to a poll is not necessarily a window into some pure, private realm called thought. It may be habit, memory, social allegiance, emotional defence or a sentence borrowed from last night’s news.

But it is still data. So, aggregate enough of those sentences and the patterns become visible.

That is uncannily close to the operating logic of the large language model. An LLM consumes vast quantities of language, identifies statistical regularities and predicts what is likely to come next. Gallup polling samples human outputs, identifies social regularities and predicts what the electorate is likely to do next.

One predicts words. The other predicts presidents.

Of course, humans are not merely chatbots with shoes. We have bodies, hormones, childhoods, appetites, status anxieties and the inconvenient capacity to change our minds five minutes before voting. Polling fails. Elections surprise. People lie to pollsters, lie to themselves and occasionally escape the script altogether.

That is where thinking begins.

Languaging is pattern reproduction. Thinking is the interruption of the pattern.

George Gallup knew more than most about the first. His work demonstrated that human language leaves measurable tracks—and those tracks often lead directly to behaviour.

There is a personal thread here. Dr Gallup was an examiner for my PhD in lateral thinking. As I was developing the cvs2bvs brain software, George impressed upon me the importance of the cvs and its measurement as a prelude to searching for a bvs.

I then saw that connection differently. Gallup measured the language patterns already operating inside the human box. Lateral thinking was concerned with escaping them.

Together, they frame the central challenge of the AI age. Machines are becoming astonishingly good at predicting language.

Humans must become better at producing something less predictable: a new thought.

The first thing children should learn about artificial intelligence is that it has very good manners for something with no idea what it is saying.

It replies promptly. It never slouches. It produces paragraphs as a hotel kitchen produces omelettes: quickly, efficiently, and with a faint suspicion that everybody is getting the same one. It will summarise Aristotle, draft a poem about volcanoes, explain the Treaty of Versailles, and offer emotional support with the serene confidence of a machine that has never once been twelve years old in a crowded lunchroom.

Naturally, children are impressed. So are adults, though adults disguise this by using phrases such as “workflow optimisation” and “strategic implementation.” A child, at least, has the decency to gasp.

But the educational danger is not that children will think AI is clever. In many respects, it is. The danger is that they will conclude that intelligence is merely the production of fluent answers.

This would be a catastrophe, though admittedly one with excellent formatting.

The great subject now required in schools is not coding, prompt engineering, digital citizenship, or whatever phrase has most recently escaped from a consultancy retreat. The great subject is intelligence itself: human and artificial. Children need to know what machines do, what brains do, and why confusing the two is like mistaking a microwave for a dinner party. Children urgently need an understanding of neuroscience.

A machine computes. It retrieves. It predicts. It recombines. It can write a tidy essay on courage without ever having needed any. It can produce a meditation on grief without having misplaced so much as a sock. It can generate a sonnet about love while remaining, emotionally speaking, a toaster with a vocabulary.

A child is different. A child has biology, which is to say trouble. A nervous system. A pulse. A body that gets hungry at the wrong time. A face capable of betrayal by blushing. A memory that improves, worsens, rearranges and litigates. A conscience that wakes just when sleep was becoming possible. A capacity for embarrassment, kindness, doubt, mischief, imagination and the blessedly inefficient habit of wondering.

AI can answer the question. The human child can ask whether the question was any good.

That distinction ought to be printed above every classroom screen.

For too long, schooling has treated memory-retrieval as intelligence. The good student remembered the date, recited the formula, reproduced the paragraph, filled the blank and looked sufficiently alive while doing so. This was never a perfect model of intelligence, but it had the bureaucratic advantage of being easy to mark.

AI has now arrived to perform this trick faster, cheaper and without requesting lunch. Retrieval is no longer the summit of intelligence. It is the ground floor, possibly the basement.

The human premium has moved upstairs: judgement, discernment, imagination, empathy, reframing, humour, conscience, lateral thinking and the ability to detect nonsense even when it is wearing a silk tie and citing three studies.

This is the cognitive vaccine children need. AI will hallucinate. It will flatter. It will reflect bias in impeccable prose. It will confidently assist the lazy, the vain, the frightened and the already convinced. It will help a child turn a weak Current View of the Situation into a glossy little fortress, complete with battlements, footnotes and a moat full of adjectives.

That is the automated Intelligence Trap.

The clever child is especially vulnerable. Intelligence, untrained, often becomes an in-house legal department retained to defend yesterday’s opinion. Add AI, and the department acquires junior associates, a research team, a slide designer and the ability to work weekends.

So the task is not to frighten children about artificial intelligence. Fear is a dreary pedagogue and tends to assign extra homework. The task is to teach sovereignty over the mind.

Children should learn how attention is captured, how emotion steers judgement, how certainty disguises bias, how curiosity opens the side door, and how better thinking can be trained. They should learn that emotional intelligence is not a scented candle in the curriculum, but a survival technology. In a synthetic world, empathy, restraint, courage, trust and discernment are not soft skills. They are the operating system.

The tools will change. Today’s miracle app will become tomorrow’s quaint digital fossil, displayed somewhere between the overhead projector and the interactive whiteboard that never quite worked after Tuesday. Platforms will rise, models will improve, acronyms will breed in committee papers.

But metacognition travels well.

A child who can think about thinking carries portable power.

AI should be introduced not as an oracle, rival, babysitter or headmaster, but as an instrument: fast, useful, tireless and subordinate. A cAIos, not a commander. The child supplies the aim. The machine supplies assistance.

The machine may have the answers. But the child must keep the questions.

Children cannot control the algorithm. They cannot see every hand that shaped it, every bias folded into it, every commercial appetite humming beneath its pleasant interface.

But they can learn to govern the most astonishing technology they will ever possess: their own brain.