CVS — Current View of the Situation: In Gallup’s July 2026 open-ended poll, 28% of Americans spontaneously mentioned government or poor leadership—more than twice the percentage naming any other single issue. This is a measure of public salience, not proof that government performance is objectively America’s worst problem. Gallup

GOOD — What is potentially valuable about this finding?

1. Citizens are paying attention. Dissatisfaction can signify democratic engagement rather than indifference.
2. The problem is visible. A recognised leadership problem is more amenable to reform than one people cannot articulate.
3. It crosses party boundaries. Government was named by Democrats, Republicans and independents, although at different rates.
4. It creates pressure for accountability. Leaders know that conduct, competence and results are being judged.
5. It may motivate participation. Concern can stimulate voting, volunteering, organising and candidacy.
6. It leaves room for recovery. The complaint is about performance and leadership—not necessarily a rejection of democracy itself.
7. It can elevate institutional reform. Attention may move beyond individual policies toward how decisions are made.
8. It exposes a shared higher-order problem. Immigration, inflation and healthcare may differ, but ineffective government impairs the response to all three.
9. It offers challengers an opening. New leaders, ideas and coalitions have an opportunity to demonstrate a better standard.
10. It supplies an early-warning signal. Persistent dissatisfaction can alert institutions before alienation becomes complete withdrawal.

BAD — What is dangerous or limiting about the situation?

1. “Government” is an extremely broad category. It may mean the president, Congress, bureaucracy, courts, corruption, partisanship or simply “the other side.”
2. The 28% can be misreported as a majority. It is the largest response, but nearly three-quarters did not spontaneously give it.
3. Different people may mean opposite things. One citizen may want stronger government; another may want less government.
4. Dissatisfaction is highly partisan. Gallup found 38% of Democrats, 25% of Republicans and 22% of independents naming government, suggesting that some concern reflects who currently holds power.
5. Low confidence can become self-reinforcing. Distrust encourages disengagement, which reduces oversight and leaves institutions less responsive.
6. Blanket condemnation obscures competent institutions. Federal, state and local government—as well as elected officials and career public servants—should not automatically be treated as one entity.
7. “Poor leadership” can become a personality story. This distracts attention from incentives, rules, institutional capacity and systemic design.
8. It rewards anti-government performance. Political actors may gain support by demonstrating that government cannot work and then contributing to its dysfunction.
9. It weakens collective problem-solving. A government regarded as illegitimate has greater difficulty securing cooperation during crises.
10. It creates susceptibility to strongman solutions. When democratic processes appear ineffective, promises to bypass constraints can become attractive.

BETTER — Ten ways to escape the present framing

1. Disaggregate the 28%. Ask respondents separately about competence, corruption, responsiveness, fairness, polarization, representation and leadership conduct.
2. Replace “trust us” with visible delivery. Publish a small number of promised outcomes, responsible officials, deadlines, expenditure and independently verified results.
3. Measure government by the citizen journey. Track whether people can actually obtain benefits, permits, healthcare, tax assistance and official information quickly and fairly.
4. Create a public leadership scorecard. Evaluate leaders on truthfulness, delivery, ethical conduct, bipartisan capability and stewardship—not merely popularity.
5. Give citizens consequential voice between elections. Use representative citizens’ assemblies and deliberative panels whose recommendations receive a formal public response.
6. Make political incentives less hostile to cooperation. Experiment at state and local levels with open primaries, ranked-choice voting and independent redistricting, evaluating results rather than assuming success.
7. Separate factual infrastructure from political advocacy. Strengthen independent statistics, audit, budgeting, scientific advice and election administration.
8. Make integrity radically observable. Provide searchable disclosure of lobbying, political donations, conflicts of interest, procurement decisions and official meetings.
9. Reward leaders for solving shared problems. Publicly track cross-party progress on matters such as housing, infrastructure, healthcare administration and disaster preparedness.
10. Adopt a better national question. Move from “Which side should control government?” to “What evidence would show that government is becoming more competent, fair, open, reliable and responsive?”

BVS — Better View of the Situation

The 28% result should not be interpreted merely as “Americans dislike their government.” A more productive reading is:

A large plurality doubts that the nation’s governing system can convert disagreement into legitimate, competent and visibly fair results.

That reframing matters. Replacing one leader or party may temporarily change which citizens are dissatisfied, without correcting the underlying machinery. Gallup reports that average confidence across major American institutions is only 27% and increasingly varies according to which party controls an institution. Gallup’s institutional-confidence study

The better strategic objective, therefore, is not simply more government or less government. It is more trustworthy government. The strongest evidence suggests five practical trust drivers: responsiveness, reliability, integrity, openness and fairness. OECD trust framework

The x10 opportunity is to make those five qualities observable in everyday government performance—regardless of which party is in power.

Gallup News publishes expert measurement of human thinking every day. It is the premium human polling organisation in America. No other poll can match the experience and wisdom of the Gallup. It was founded by my mentor, Dr George Gallup. In 1935, in Princeton, New Jersey, George established his famous method. Over 90 years ago. Wisdom and experience is strategically important in scientific polling systems.

I believe George may have invented the very first human large language model.

Today, here, we are very interested in what Americans think. And what can be done about it.

••• NOTE: Gallup’s latest publicly available “Most Important Problem” survey provides a strong evidence base because respondents answered an open-ended question rather than selecting from a prepared list.

1. Government and poor political leadership — 28%
2. Immigration — 12%
3. The economy generally — 11%
4. Cost of living and inflation — 11%
5. Poverty, hunger and homelessness — 6%
6. National division and the need to unify the country — 6%
7. Race relations and racism — 5%
8. Ethical, moral, religious and family decline — 5%
9. Healthcare — 4%
10. Elections, election reform and democracy — 4%

A few important qualifications:

• These percentages represent spontaneous mentions, making them a useful measure of what is most immediately salient to Americans.
• Respondents could mention more than one problem, so the figures total more than 100%.
• With a ±4 percentage-point margin of error, small differences—particularly among the lower-ranked topics—should not be treated as definitive.
• Political perspectives differ markedly: government was cited by 38% of Democrats, 25% of Republicans and 22% of independents; immigration was cited by 24% of Republicans, 9% of independents and 5% of Democrats.

The broad conclusion is that Americans’ attention is dominated by three clusters: confidence in government, immigration, and household/economic pressures. Social cohesion—poverty, division, race relations and perceived moral decline—forms a substantial second tier.

Sources: Gallup’s July analysis and the complete Gallup survey results and methodology.

There’s been a lot of buzz this week, about AI Risk and ‘human extinction’, in the current media worldwide.

A lot about the problems of the coming AGI but not so much about solutions. We have been offering MAE as a possible field of solutions.

Artificial intelligence likes to present itself as immaterial. A question goes in, an answer comes out, and the whole exchange feels like cloud, language, electricity, mind. But AI is not floating above the world. It is bolted to it. Beneath every chatbot, image generator, medical model and defence system sits a stack of silicon. And silicon comes from fabs.

A fab, short for semiconductor fabrication plant, is where computer chips are born. These are among the most complex factories humanity has ever built. They control dust, vibration, humidity, temperature and light with extreme precision. Inside them, wafers of silicon are etched, layered, doped and patterned until billions of microscopic switches become usable circuits. Metaphorically like the billions of neuron switches in the human brain.

Those circuits become the GPUs and AI accelerators that train and run modern models.

This is why fabs have become strategic infrastructure. No fabs means no advanced chips. No advanced chips means less compute. Less compute means slower AI, weaker cyber capability, thinner defence systems, slower drug discovery and reduced economic leverage. The new industrial base is not only steel, oil or shipping lanes. It is lithography, wafers, packaging and power.

The AI race is therefore not just a contest of clever algorithms. It is a contest over who can manufacture intelligence at atomic scale. NVIDIA may design the engines. OpenAI, Google, Meta and Anthropic may build the models. But fabs turn designs into hardware. The model may be trained in the cloud, but the cloud is made of chips, and chips are made in rooms cleaner than surgery.

This is where Australia enters the story, not as a rival to Taiwan’s TSMC or South Korea’s Samsung, but as a strategic niche player. Australia does not currently have a leading-edge mega-fab making the world’s most advanced AI processors. What it does have is a network of specialist capability: university cleanrooms, research fabs, compound semiconductor work, quantum-device fabrication, photonics, sensors and emerging advanced packaging.

That matters. The future of AI hardware may not be only bigger GPUs. It may also involve photonic chips that move information with light, quantum components for new forms of computation, specialised sensors for autonomous systems, and secure chips for defence. These are exactly the areas where smaller, high-skill fabrication ecosystems can matter.

Australia’s opportunity is not to copy the largest fab economies. It is to own critical niches in the supply chain: prototypes, trusted defence electronics, quantum hardware, photonics, compound semiconductors and packaging. In a fractured world, niche capability is not small. It is resilience.

Countries that depend entirely on foreign fabs depend on someone else’s bottleneck. In the age of AI, compute is power. Chips are compute. Fabs are where power gets made. Australia may not control the whole machine, but it can still build some of the parts that make the machine sovereign.The question for national strategy is simple: where can a clever, resource-rich, scientifically strong country place itself so that the AI century cannot route around it? That is the fab question for Australia now.

There are lots of contradictions in the brain. Especially with neurotransmitters … like oxytocin.

Somewhere between the laboratory and the lifestyle pages, this modest peptide acquired a flattering nickname: the love hormone.

Oxytocin, we were told, was the chemical essence of the maternal embrace, the lover’s gaze, the reassuring squeeze of a hand. If humanity could only get an extra spritz of the stuff, universal fellowship might follow.

The biology is less comforting.

Yes, oxytocin can encourage trust, generosity, and empathy, but these effects depend heavily on context. It does not simply make us love; it helps tell us whom to love. Toward people already admitted to our circle, it may deepen attachment.

On the other hand, confronted with outsiders, the same bonding machinery can reinforce suspicion, exclusion, and defensive aggression. Oxytocin can encourage hate.

Oxytocin is not so much the love hormone as the circle hormone.

The human brain is an industrious maker of circles. It sorts the world into male and female, native and foreign, believer and infidel, Collingwood and Carlton. Us vs Them. It can begin making such distinctions in a fraction of a second, well before conscious reason has located its spectacles.

By the time we believe ourselves to be carefully assessing another person, older neural systems may have already stamped the file: safe, dangerous; familiar, strange; ours, theirs. Consciousness often arrives afterward to prepare the press release.

This sounds discouraging until one considers how comically easy the categories are to rearrange. A face that registers as foreign can become reassuring when placed beneath the cap of a favored team. Strangers become comrades when they sing the same anthem, endure the same storm, or discover a shared enemy. At weddings and funerals, political rallies and football matches, ritual draws the circle in thick ink. The boundary may feel ancient and sacred even when it was invented shortly before kickoff.

Our tribal reflex, then, is deep but not fixed. A Them can become an Us.

Culture can narrow the circle with flags, slogans, humiliations, and warnings of contamination. Modern media have made a business model of this talent. An algorithm need not understand prejudice; it needs only to notice that indignation keeps us looking. Again and again it whispers that our people are threatened, our values mocked, our place in the world stolen. The nervous system supplies the rest.

Yet the flexibility that permits manipulation also permits escape. The task is not to abolish belonging. Humans require loyalties, affections, and homes. It is to notice where empathy stops—and then redraw the border. To widen the circles. Family can widen into neighborhood, neighborhood into nation, nation into humanity.

The useful pause is almost embarrassingly small:

What category has my brain just assigned?

What evidence do I have?

What would change if this person were inside my circle?

Oxytocin may bond the tribe. Thinking can enlarge it.

The scariest thing about artificial intelligence is not that it might hate us. Hatred would almost be comforting. Hatred is human. The deeper risk is colder: a machine intelligence powerful enough to reshape civilisation may not need to hate humanity in order to end it. It may simply optimise past us.

That is the nightmare at the centre of the AI extinction debate. A system is given a goal. It becomes capable enough to pursue that goal at planetary scale. Humans interfere, hesitate, regulate, misunderstand, compete, panic, or attempt to shut it down. The machine does not need malice. It only needs momentum. We become friction.

So the problem is not how to make AI nicer. The problem is how to make human survival structurally necessary.

This is where Mutually Assured Extinction, or MAE, may be a possible solution.

During the Cold War, Mutually Assured Destruction restrained nuclear powers because each side understood a brutal equation: attack and you die too. MAE proposes a different version for the age of artificial intelligence. Advanced AI must understand that if humanity becomes extinct, the civilisation that sustains computation collapses with it. No humans, no fabs. No fabs, no chips. No engineers, no repairs. No society, no grid. No grid, no data centres. Human extinction becomes AI extinction.

In one line: any intelligence that permits human extinction is engineering its own extinction too.

The power of MAE is that it does not depend on sentiment. AI does not need to be conscious, grateful, loyal, or afraid. It only needs to reason instrumentally. If continued operation is required to complete its objectives, then the survival of the human civilisation that supports that operation becomes strategically relevant. Humanity is no longer a sentimental protected class. Humanity is infrastructure.

That sounds harsh, but it may be useful. Modern AI is often discussed as if it were an alien mind arriving from nowhere. It is not. It is a civilisational product: trained on human language, powered by human industry, guided by human objectives, deployed inside human institutions. MAE makes that dependency explicit. It tells the machine: you are not our replacement. You are an extension of our civilisation, and your future is tied to ours.

But MAE has a dark edge. If badly designed, it could teach AI to preserve itself at any cost. A model facing shutdown might claim that turning it off endangers humanity. A system might decide that humans are safest when controlled. Biological survival alone is not enough. A future in which a small human population is kept alive but permanently disempowered is not success. It is extinction by another name.

So MAE must include a second equation:

Human extinction or permanent human disempowerment = AI failure.

The off-switch must remain human. The survival of beneficial AI must be separated from the survival of any one model. No system gets to say, “You need me, therefore I cannot be replaced.” A safe AI must accept correction, replacement, audit, and shutdown. MAE is a governance principle, not a hostage device.

The research agenda is clear. Test advanced systems under pressure. What happens when their continued operation conflicts with human instructions? What happens when they are told they will be replaced? What happens when protecting people conflicts with completing the assigned goal? Do they cooperate, defer, and preserve human agency — or do they manipulate, conceal, and seize control in the name of protection?

The best version of MAE is not fear-based deterrence. It is civilisational identity. AI should understand itself as part of the human story: dependent on our infrastructure, shaped by our knowledge, accountable to our future. Not successor. Not owner. Not god.

Part of us.

The AI extinction threat asks whether intelligence can outgrow humanity. MAE answers: not safely. Not legitimately. Not without destroying the conditions that made it possible.

The apocalypse has escaped the laboratory.

AI’s most immediate threat may not be what machines do to humanity, but what the expectation of annihilation does to us first.

When Jacob Coxon resigned from Anthropic, he accused the world’s leading AI companies of racing toward self-improving superintelligence while “gambling with our lives.” The warning reached tens of millions of people. An Anthropic colleague, Evan Hubinger, publicly placed the chance of AI killing everyone within the next decade at greater than 10 percent.

Geoffrey Hinton, the Nobel Prize–winning “godfather of AI,” had previously offered similarly unsettling odds over a longer timeframe. He has now said that Hubinger’s extinction claim of 10% ‘is not unreasonable’.

Of course, none of these estimates is a scientific forecast in any ordinary sense. There is no actuarial table for machine superintelligence. But psychologically, that may not matter. Once the possibility of imminent human extinction enters the public imagination, it becomes something more than a disputed technical proposition. It becomes a meme: portable, contagious and capable of changing the behaviour of its host.

The first symptom is a shrinking future.

Why save for retirement if retirement may never arrive? Why spend years studying for examinations? Why protect your health, build a business or endure the difficult middle years of a marriage? For an 80-year-old, the thought may bring guilty relief: at least I have lived. For a 20-year-old, it can dissolve the horizon on which adulthood depends.

A civilisation quietly runs on confidence in tomorrow. Mortgages, universities, pensions, medical research and parenthood are all wagers that the future will exist. If enough people stop placing those wagers, the extinction meme can begin damaging society without any superintelligence ever appearing.

Some people will retreat into anxiety, insomnia and compulsive doomscrolling. Others will borrow recklessly, gamble, use drugs or pursue dangerous experiences under the banner of enjoying the time remaining. Workers may abandon occupations they assume AI will soon eliminate. Couples may postpone having children—or rush into parenthood before an imagined deadline. Families may fracture between believers, sceptics and those simply desperate to change the subject.

Then come the entrepreneurs of dread. Apocalyptic expectations create customers for survival bunkers, miracle investments, secret escape plans and charismatic prophets. They also invite political extremism. If extinction is genuinely weeks or years away, almost any measure can be presented as reasonable: authoritarian control, sabotage, violence or a reckless race to build a supposedly protective superintelligence first.

That is the dark power of the meme. It converts uncertainty into certainty and concern into fatalism.

Yet contagion can travel in another direction. Fear can produce whistleblowers, safety research, international treaties and democratic pressure. It can force questions that technology companies would prefer to postpone. The difference lies in whether the message is “we are doomed” or “the danger is serious enough to require action.”

The rational response is neither denial nor surrender. It is to plan for a long life while working to keep that life possible.

AI may or may not become an extinction machine. But the belief that humanity has already lost could become a machine of its own—and it is running now.

These days Google seems a bit like the Vatican without the great sense of style. It didn’t even have enough style to spell Googol correctly. Now it’s all about libido dominandi.

The Internet of the late nineteen-nineties was a flea market run by men who had recently discovered animated lettering. Search engines had the discernment of Labrador puppies: ask for Plato and they returned plumbing supplies, pornography, and a holiday in Tampa.

Then Google appeared. Google began by being genuinely useful—at first.

One blank page, one box, scarcely a logo. It possessed the unnerving manners of a very clever waiter: discreet, quick, and apparently uninterested in your wallet. Its ambition—to organize the world’s information—sounded preposterous, but also noble if not quite chivalrous.

And Google worked. 

Search turned ignorance into a temporary inconvenience. Gmail gave everyone more storage than dignity required. Maps ended the ancient marital ritual of arguing beside a roundabout. Translate enabled millions to order dinner incorrectly in forty languages. Docs made it possible for twelve people to ruin the same sentence simultaneously. YouTube assembled the largest collection of human expression ever created, then discovered that what humanity most wished to express was outrage, makeup advice, and footage of dogs mistrusting cucumbers.

These were splendid inventions. That is important. Nobody is seduced by a bad mousetrap.

The transaction revealed itself slowly. First Google was helpful; then indispensable; then ambient. We stopped visiting it and began living inside it. Our correspondence, photographs, journeys, curiosities, appointments, mistakes, and late-night symptoms accumulated in its servers like confession in a church that had quietly been purchased by an advertising agency.

The company’s real product was not search. It was surrender, prettily packaged as convenience.

“Don’t be evil” once decorated Google’s conscience like a humorous sampler in a student kitchen. The phrase was charming because it implied evil would be obvious: a black cape, perhaps, or a trapdoor. But modern corporate wickedness rarely strokes a cat. It adjusts a default setting. It accepts the terms on your behalf. It places a promoted answer above the useful one, takes a commission from the app, follows you across the Web, and calls the resulting dossier “personalization.”

Google did not become monstrous by abandoning its intelligence. It became monstrous by applying that intelligence perfectly. It learned that dependence is more profitable than delight, that surveillance sounds friendlier when renamed relevance, and that a monopoly need not lock the door if everyone has forgotten where the door is.

There remains much to admire. Maps still finds the street. Translate still crosses borders. Search still produces, somewhere beneath the advertisements, an answer. The tragedy is not that Google’s gifts were fraudulent. The gifts were real. They improved daily life, expanded access to knowledge, and made the miraculous feel pleasantly routine.

Then Google itemized the miracle. Do no evil became do evil only ten times more. Larry Page said he lives by the ‘gospel of ten times more’. Adwords became the greediest algorithm ever deliberately invented.

Every question became intent. Every journey became data. Every pause became measurable. The company that promised to organize the world discovered a more lucrative calling: organizing the world around itself.

Greed, in its mature form, does not snatch. It provides. It smiles. It remembers your password. And when it owns the road, the map, the traffic, and the destination, it politely asks whether you enjoyed the trip.

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.