The machine may possess the world’s information. But a human still has to decide what to ask.

The most valuable piece of real estate in artificial intelligence is the small, empty rectangle blinking beneath the words: How can I help?

This is the prompt box: part cockpit, part confession booth, part slot machine. Into it we type a few words and await the cognitive jackpot.

“Write a strategy.”

“Explain quantum physics.”

“Make this better.”

When the answer arrives sounding like a management consultant who has swallowed Wikipedia, we blame the machine.

Computing has long had an acronym for this: GIGO—Garbage In, Garbage Out. Feed a system bad data and it produces bad results. With generative AI, however, the danger is subtler. ChatGPT can transform a vague, biased or confused prompt into a beautifully formatted piece of nonsense.

The new GIGO is Garbage In, Gospel Out.

The machine’s fluency can disguise the human’s failure to think.

A prompt is not merely a question. It is cognitive architecture. It tells the AI where to look, which role to play, what constraints to respect, what success should resemble and, most importantly, which problem it is supposed to solve.

“Give me some marketing ideas” is not a prompt. It is a cry for help wearing WFH casual.

A better version supplies a destination: “Design ten low-cost word-of-mouth strategies to help an independent Australian author sell a humorous book about chickens to readers over 50. Rank them by cost, speed and likelihood of recommendation.”

The first request produces content. The second recruits intelligence.

This is the central insight of prompting: the quality of the output depends not only on the capability of the machine, but also on the quality of the human direction. A powerful AI given a weak prompt is like a Formula One car being navigated by someone shouting, “Go somewhere nice.”

Yet GIGO does not mean prompts must be long. Length is not intelligence. Some of the worst prompts are enormous bureaucratic casseroles containing twelve objectives, nine audiences, conflicting instructions and a garnish of jargon.

Good prompts contain distinctions.

What is the purpose? Who is the audience? What does the AI need to know? What should it avoid? What form should the answer take? How will we recognise a useful result?

These questions convert prompting from typing into thinking.

I have been interested in promptworthiness for more than 40 years, although the word originally had nothing to do with chatbots. I wanted to know why some content gets itself copied from brain to brain while other content—valuable, worthy and perfectly true—dies before lunch.

Why does one joke cross a continent while another expires at the dinner table? Why does a political slogan colonise millions of brains? Why can The Girl from Ipanema, born among Brazilian musicians in the 1950s, still drift through elevators while thousands of later songs have vanished into the digital compost heap?

The answer is fitness.

Content lives under relentless selection pressure. Billions of human brains face trillions of competing packets of information. Every book, headline, sermon, advertisement, rumour, video and meme wants the same scarce resource: attention.

The content that survives tends to possess three traits: fidelity, because it can be copied accurately; fecundity, because it can generate many copies; and longevity, because it remains active over time.

These are also the traits of a powerful AI prompt.

A good prompt preserves its meaning, generates multiple useful possibilities and becomes reusable. It gets copied, refined, recommended and repeated. Eventually, it may become part of an organisation’s operating system: the standard prompt for reviewing a proposal, investigating a mistake or testing a strategy.

One especially useful example consists of four words:

“Do a GBB on this.”

GBB means Good, Bad, Better.

Ask the AI to identify ten good points about an idea, ten bad points and ten ways to make it better. The instruction is simple, but its cognitive effect is substantial.

“Good” prevents premature dismissal.

“Bad” interrupts infatuation.

“Better” escapes the primitive courtroom of right versus wrong and moves the conversation towards design.

Suppose you give ChatGPT a business proposal and ask, “Is this a good idea?” You have invited the machine to guess which answer will please you. It may become an exceptionally articulate accomplice.

Instead, ask: “Do a GBB. Give me ten good features, ten weaknesses and ten practical improvements. Identify the assumption most likely to be wrong.”

Now the AI has a thinking structure. It must explore the idea rather than merely applaud or condemn it.

••• Click image and give your prompt to get an instant GBB •••

GBB also improves the human prompter. It reminds us that we do not enter the prompt box as neutral investigators. We arrive carrying loyalties, fears, sunk costs and preferred conclusions. A biased prompt can quietly turn ChatGPT into an in-house barrister for a bad idea.

“Explain why my strategy will succeed” is not research. It is intellectual room service.

Better prompting asks the AI to challenge the premise, find contrary evidence, compare alternatives and label fact, inference and speculation. The goal is not to eliminate bias entirely—a project roughly comparable to eliminating weather—but to make it visible enough to inspect.

ChatGPT does not simply learn permanently from every weak prompt typed into it. The prompt primarily supplies the immediate context for the response. But that makes input no less important. The prompt determines which part of the machine’s enormous possibility space appears on the screen.

AI does not abolish the need for thinking. It exposes it.

Knowledge was once power. Then search engines made knowledge searchable. Now AI can explain, compare, draft and synthesise it in seconds. The scarce skill is shifting from possessing answers to framing worthwhile questions.

Everyone now has access to something resembling an intellectual orchestra. But the music still depends on the conductor.

The prompt is the baton.

And GIGO’s final lesson is not that machines are stupid. It is that machines can make our stupidity sound astonishingly intelligent.

So, before accepting the next polished answer, pause and issue one more command:

Do a GBB!

The future will belong not to humans alone or AI alone, but to humans who can prompt AI to investigate what matters, challenge what is assumed and produce something worth replicating.

The question is no longer merely whether artificial intelligence is becoming smarter.

It is whether its human prompters are becoming more promptworthy.

For most of its career, neuroscience has been called to the scene after something has gone wrong. Depression. Addiction. Dementia. Trauma. Neuroscientists arrive with scanners, electrodes and expressions of professional concern, rather like detectives in a television series where the victim is still alive but has misplaced the car keys.

This work is essential. But it has produced a curious imbalance. We know a great deal about the malfunctioning brain and rather less about the flourishing one.

Hence a new field waiting to be properly named: wellbeing neuroscience.

This is not disease neuroscience. Nor is it positive thinking wearing a white laboratory coat. It is the study of how gratitude, meaning, connection, curiosity, purpose and flow are produced—not simply “in the mind,” wherever that is supposed to be, but by the brain and body working together.

Its central question is beautifully direct:

What is the brain, as an organ of the body, doing when a human being is living well?

The old model pictured mental health as a railway line. At one end was illness; at the other, happiness. If the psychiatric train moved far enough away from depression, it would eventually pull into Flourishing Station, where everyone apparently kept gratitude journals and slept eight uninterrupted hours.

The newer two-continua model says that mental illness and wellbeing are different, though related, dimensions. A person can have no diagnosable illness and still be lonely, disengaged and without purpose. Another person may live with anxiety, depression or OCD while also enjoying friendship, meaningful work, humour and hope.

Removing misery does not automatically install joy. Treating panic does not create purpose. The absence of illness is not the presence of wellbeing, any more than the absence of termites is interior design.

This matters because wellbeing has different neural targets. The brain has systems involved in detecting threat, pursuing reward, controlling attention, regulating behaviour, constructing a sense of self and connecting with other people. Flourishing does not occur when one of these systems wins. It occurs when they work together with something approaching diplomatic competence.

Popular neuroscience likes to assign every emotion a postcode. Fear lives in the amygdala. Self-control occupies the prefrontal cortex. Joy is supplied by dopamine. Trust is delivered by oxytocin, presumably in an environmentally responsible bottle.

The actual brain is less tidy. The amygdala helps detect significance, uncertainty and possible threat; it is not merely a fear button. Dopamine contributes to motivation and learning, not instant happiness. Oxytocin can encourage attachment, but its effects depend upon the person, the group and the situation. It may help us trust “our people” while becoming more suspicious of everyone else.

The brain is not a vending machine. It is more like an orchestra whose musicians have strong opinions about the tempo.

We experience its emotional music as instructions:

Be angry. Be fearful. Be determined. Be ashamed. Be joyful. Be sad. Be anticipating. Be surprised. Be trusting. Be disgusted.

Each emotion has a survival history. Fear can protect us from danger. Anger mobilises energy against obstacles. Shame helps prevent social exile. Sadness signals loss and draws support. Disgust steers us away from contamination. Surprise interrupts the current prediction and says, in effect, “Well, that wasn’t in the brochure.”

Even so-called negative emotions can serve positive purposes. The problem is not that we experience fear, anger or sadness. The problem arises when an emotion misreads the situation, refuses to leave or starts answering questions nobody asked.

Wellbeing is not permanent happiness. A person who remained delighted during a funeral, a bushfire and a tax audit would not be flourishing. He would require assessment.

The aim is emotional flexibility: the ability to fear danger, survive sadness, revise anger, test trust, experience joy and remain open to surprise.

We cannot always tell the brain how to feel about the present moment. Telling an anxious person to calm down is like telling Melbourne weather to be more consistent. The instruction may be sensible, but it is addressed to a system with its own momentum.

Understanding neuroplasticity can, however, help change the brain that will encounter the next moment.

This is the idea behind the Limbic Games. A current view of a situation can be investigated in search of a better view. New information may shift anger toward surprise. Support may help sadness move toward trust. Preparation can prevent fear from becoming paralysis. A reliable relationship may allow guarded trust to become joy.

Books, lessons, coaches, therapy, friendship, music, religion, teamwork, AI and changes of environment all provide new evidence. Repeated experience alters what the brain predicts. Changed predictions can produce different future responses.

Training is the key. This is what we have learned from the rapid advancement of AI. Intelligence can be trained.

Circumstances change brains. Training is the deliberate design of circumstances.

The body is also involved, which may disappoint anyone who hoped wellbeing could be achieved entirely from an armchair. Heart-rate variability provides clues about autonomic flexibility. Cortisol helps track stress. Inflammation, sleep, movement and the gut-brain system all contribute to the organism’s state.

No single measure proves flourishing. A high heart-rate variability score does not mean one has achieved wisdom. It may simply mean the watch is pleased.

What wellbeing neuroscience needs is a bio-behavioural matrix combining experience, behaviour, relationships, sleep, physiology and environment. Brain scans may contribute, but only when they tell us something useful. A coloured patch on an fMRI image is not yet a meaningful life.

Language may be another wellbeing technology. Someone whose emotional vocabulary consists of good, bad and stressed is working with three emotional pixels. A richer vocabulary distinguishes irritation from humiliation, apprehension from danger, loneliness from sadness and relief from joy.

“I am furious” suggests one problem. “I feel excluded” suggests another. Naming an emotion does not abolish it, but it can change what the brain notices and what the person does next.

This is especially important in schools.

A classroom asks developing brains to perform adult feats of attention, restraint, flexibility and social judgment. The school environment frequently demands a mature prefrontal cortex from hardware that is still under construction.

When a child behaves badly, adults usually ask, “What is wrong with this child?” The Brain Detective asks better questions:

• What was the brain trying to do?
• What was the brain feeling?
• What was it worried about?
• What was it hoping for?
• What can it learn for next time?

Consider a 13-year-old girl who smokes after her father forbids it. The behaviour may be an attempt to assert autonomy, gain peer approval, imitate a high-status person, test a boundary, relieve stress or chase novelty.

None of these possibilities makes smoking acceptable. An explanation is not an excuse. It is evidence that might produce a better response than shouting “Because I said so” at a nervous system currently specialising in defiance.

The Brain Detective does not lower standards. It raises the quality of the adult response.

That is the promise of wellbeing neuroscience. Clinics could treat illness while also building connection and purpose. Workplaces could investigate whether their systems create chronic threat rather than distributing resilience brochures. Schools could teach children that emotions and decisions emerge from an organ in their bodies—their human brain.

Neuroscience has spent more than a century asking what goes wrong inside us.

The next great question is what helps us go well.

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 little more than label-slapping and mail-sorting.

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. It will have a sense of humour.

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 nuanced, contradictory and 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.