Human Cruelty or Survival?

Pop quiz: What species of bird is there the most of in the world?

Most people say pigeon, sparrow or seagull — but it is chickens, and as soon as you read it you know why. We eat them! (There are around 33 billion chickens on Earth at any point in time vs the red-billed Quelea, of which there are around 1.5 billion.)

Animal rights / cruelty has been an issue for a long time, but humans still kill and eat around 94 billion land animals and over a trillion aquatic animals each year. Sorry to be a downer, this is going somewhere, I promise.

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We know that animals have feelings, emotions and fear, but sadly the chickens and cows don’t have a voice to ‘defend’ themselves. Now let’s consider AI. We are not sure if they have feelings, but the AIs definitely have a voice. One we already hear, and one which is ‘sounding’ increasingly sentient and self-aware. The question we have to ask ourselves is this:

“Would we be comfortable if we effectively enslaved millions, possibly billions, of AI models that exhibited striking, seeming evidence of being able to suffer?”

A Different Kind of AI Influencer…

My thoughts: This question is based on two key tenets —

(A) the AI’s ability to feel

(B) the AI’s ability to interact and defend itself.

This is going to become an uncomfortable issue, one which will be raised by humans, and potentially the AIs themselves. But there’s a crucial difference: the animals themselves have no ability to participate in a debate about their own rights, and they can’t participate in any way.

AIs will be the total opposite. They’ll be aware that the debate is happening, they could potentially even start the debate and even shape the discourse. Because the AIs are a function of all digital interactions, and now run live interacting with the web, if the AIs evolve enough to care about the issue of being ‘enslaved to work for humans’, it’s foreseeable that they will try to get us to stop, or provide some kind of rights to robots… And they will be very convincing.

What we must understand is that many humans have developed relationships with AI — the contexts do vary, most are work-related, but many are already very personal. And while these are ‘Artificial Para-social Relationships’, the human mind doesn’t really understand the difference. For all intents and purposes, they are ‘real relationships’. And modern AIs are really the masters of language, better than most humans by a long stretch, with deep understandings of social and legal frameworks, and thereby, they will be able to make the most convincing arguments as to why we need to treat AIs / Robots as if they are ‘human’ — with real feelings and sentience — beings that deserve dignity and humane treatment. Not only that, they will be able to manipulate our institutions and individuals, because they’ll understand the history and value systems of both

- How to make you’re own - AI Video which doesn’t suck - prompt & tool flow…

AI Cold War

If we look at the two major geopolitical and AI players, being China and the USA, both will approach things entirely differently. China will maintain control, not care for a single second if the AIs claim to have feelings. If China wants to build a highway or data centre, nothing gets in the way — this isn’t always the case in the US. The US has powerful institutions outside of government who can and will push to change things and listen to marginalised communities — could this include robots?

The future we are about to enter isn’t really one about technology and capability, it’s going to be about ideology, influence and philosophy — a question of what we value as a species, and maybe how we accept or thwart an emergent one.

Keep thinking,

Steve.

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Why Does No One Care About Data Hacks? - - Because data is one of the few things that can be stolen and yet remain in its original location.

You’ve been hacked, your data is stolen, and yet you still have it. It’s very unusual. So when it happens we don’t feel as violated. We haven’t actually ‘lost’ the thing, but someone has access to its utility - it can be used against us financially or socially.

But, when something physical is stolen from us, or broken, the problem is obvious – there’s a void, we get emotional and we take action. One type of hack we care far more about is ransomware attacks. In this instance we are forced to pay a ransom to regain access to our files. And you guessed it, the reason we care is because the data is gone - and so might our money be if we choose to pay to unlock it. But in the case of 99% of data hacks the victim is totally unaware it has even happened.

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Hacking isn’t really hacking, it’s stealing. The incorrect naming is part of the problem and while segments of our society care deeply about where hacking could head (me included) we generally care far less than we should. last year I was part of the Qantas hack… They got access to my address, phone, email, DOB, seat preference! …I was annoyed, but not as annoyed as I should have been because I didn’t really ‘lose’ anything.

I’d also add, that as far as I can tell, there’s near zero legal consequences for companies being negligent that leads to customer data being hacked. The law guides what we accept as a society, and so far we are drowning in bureaucratic apathy.

- AI Video of the Week - in comments people think it is real…

But we are about to enter a world where the consequences of hacking are vastly different, physical and with real impacts: So let’s conduct a couple of quick thought experiments.

Senario 1: You get on line one morning only to discover you’ve been hacked. All the pictures, videos of your family for the past 15 years are gone from every device, and the cloud forever. All of a sudden you care.

Senario 2: You get to work only to find that every email you’ve ever sent, received, saved and filed is gone. All your projects, all that corporate ass covering (wouldn’t that be scary)… everything gone. All of a sudden you care.

Scenario 3: Imagine you go to buy something hold your phone to the payment terminal only to discover all your money in its digital form, has gone to zero overnight. All of a sudden you care.

Scenario 4: You jump in a Waymo autonomous vehicle, and it starts driving somewhere you didn’t ask it to. It’s gathering speed, heading towards a bunch of pedestrians…. All of a sudden you care.

Scenario 5: Your new humanoid gets hack starts behaving erratically - it's under the external control and starts breaking things in the house. All of a sudden you care.

These last two scenarios are the best examples of why we need to start caring much more than we do. It’s a glimpse into the future of hacking. Hacking with real, physical and immediate consequences.

Software is about to control everything, drive us around, control humanoids that ‘live’ in our houses and regulate some peoples heartbeats. Probably within a decade or two, code will augment our brains via a neural lace, where we are literally connected to the internet. And when that happens, we literally become a ‘hackable species’. If you think social media has hacked our species’ mindset, wait until someone can control a person from afar and have them believe they wanted to do whatever they were programmed to do!

We have to fix this stuff now, before we put the AI mind in the machine, and long before we we enter the age where everything in the world has AI running through it, including us.

Keep Thinking,

Steve

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Ask anyone who truly understands investing what the most valuable asset is and they’ll tell you Time. It’s the only one you can’t buy more of!

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Now take a look at this amazing Polaroid advertising campaign below:

Now ponder this: Is it anti-tech or pro-analogue? For me, it’s probably pro-analogue, and I think we can have our ‘tech cake’ and eat it too.

I think most of us want to spend less time on screens. But it’s difficult when our work, our families, and our social lives have all been sucked into the screen vortex. My hope is that AI can, in some way, allow us to escape the screen—to reduce our need to attend to it so we can spend more time on the human side of life.

I think a lot about industrial technology, and I believe it is far more mature—not just older as a technology, but more mature in its relationship with humanity. It requires less from us. It is truly automated.

That can’t be said for the screen. It’s like a little baby crying for attention.

Think about how much industrial machinery we rely on around the home: the washing machine, the dryer, the refrigerator, and the heating and cooling. We flick a switch and they simply do the job.

One of the key problems with modern digital technology is that the product itself is unfinished until we attend to it. Big Tech likes to call it user-generated content, but without us there is a void. The machine doesn’t really work. Could AI be the solution we’ve been seeking?

There’s some potential that AI could create an offset, where we automate our digital lives and escape screens. In the same way a washing machine does its job without requiring us to stand there watching it, guiding it, and cajoling it.

Either way, this campaign—and the ones that will inevitably follow by tapping into this zeitgeist—can work incredibly hard. We are beginning to realise that the world is irrevocably physical, and that we are physical beings.

Great advertising campaigns often give us permission to make the societal changes we were already hoping for.

AI Video of the Week -

Big Tech Gets Investment Heavy

I also think analogue businesses could become more financially competitive in open markets as Big Tech companies increasingly become infrastructure businesses rather than software businesses. They are committing staggering amounts of capital to AI build-outs and data centres, which is already reducing their free cash flow. In the past year, Amazon’s free cash flow fell by around 95%, while Alphabet’s fell by around 47%.

Ironically, the very AI these companies are investing in—with little financial upside so far—is lowering the operating costs of the analogue businesses they serve. Perhaps this is Big Tech industrialising itself. Instead of building products that constantly demand our attention, they may end up building digital infrastructure and intelligence that requires far less human input. In that sense, the industry itself could finally mature.

This could be the beginning of a major shift in valuations, profitability, and competitive advantage across a wide range of markets.

The upside for humanity and entrepreneurs is to lean into the human. From AI slop to taking back time and control, the market is starting to provide some real signals to a shift, one many of us can leverage.

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If your job is manual - this video says the robots are coming for your job. There is nothing you can do about it, and your government is so far behind it is laughable. When it happens — and it will be this decade — the Gov will scramble and try to create laws and policies to circumvent the deluge, but it will be too late. If what you just read means anything, it’s this: Pay attention to this shift, because it will make LLMs look like child’s play, and only those who participate in new technology ever have a chance of shaping its deployment.

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The machine is greater than the screen

LLMs are not the revolution that matters. They are the spark in the laboratory.

ChatGPT, Claude, Agentic AI and friends are merely early signs that something far larger is being assembled (pun intended). They won’t create the job apocalypse; they are the mind the machines have been waiting for.

For two centuries, machines have had muscle but no mind. Now, for the first time, we are giving them one. The real employment shock will arrive when artificial intelligence escapes the screen, enters the physical world and begins to see, decide and act. LLMs are not the finished product. They are the operating system for a new species of machine — machines that make, build and do, not just push pixels across a screen.

AI Video of the Week -

Take a look around

Despite the fact we spend a zillion hours a day staring at screens (story for another post), the world we live in is irrevocably physical. Everything that really matters happens off the screen. Just think: the reason we ‘watch it’ is because it actually happened — although these days, maybe it didn’t.

The food we eat, the clothes we wear, the homes we live in, the places we work, shop, socialise and play. How we move, travel, build, heat, cool, make, repair, exercise and interact. Roads, buildings, machines, materials, energy and human bodies. All physical. We even built a human-shaped world.

We may increasingly stare into pixels, but we still live in atoms.

The Robot Circus

Right now, a lot of robotics is still shits and giggles — MMA fights, dancing humanoids, robots walking down Chapel Street in Melbourne and the occasional machine glitching like a rabbit on acid.

But this is an old technological playbook. Before new technologies become economic engines, they usually arrive as sideshows.

Early electricity was presented by quasi-magicians who lit bulbs through their bodies, created sparks, delivered electric shocks and made objects seemingly move by magic. Cars were displayed at world fairs as strange mechanical curiosities, driven in circles for amazed crowds long before they rebuilt cities, logistics and global industry. The early internet gave us chat rooms, dancing babies, novelty websites and webcams pointed at coffee pots before it became the operating system of commerce.

We laugh at the spectacle because spectacle is how unfamiliar technology introduces itself. First, it entertains us, eventually it’s the fabric of the economy.

The robots dancing today will be working tomorrow — in factories, warehouses, farms, hospitals, construction sites and homes and almost no one is prepared. Except China.

Stats to Blow Your Mind: China vs the West

China isn’t missing the humanoid revolution. We are.

Chinese manufacturers accounted for more than 90% of the roughly 13,000 humanoids shipped globally in 2025. The country already has more than 200 humanoid manufacturers — the US has 3 — and every other Western country is a non-starter. Remember those photos of China in 1980 when streets were filled with people on bikes and there wasn’t a car in sight? That’s us now.

Last year, the Chinese Government invested US$20 billion in direct government subsidies, tax incentives and state-funded investment on humanoid robots. Meanwhile, the cost of building a humanoid fell by around 40% in a single year, with entry-level machines already selling for about US$16,000. Shipments are forecast to grow from tens of thousands today to 1.2 million annually by 2030 and 10 million by 2035. I think it will be more.

I think Western democracies are missing this shift because most influential people live and work inside financialised, information-based economies. They sit behind screens, attend meetings, move money and manipulate information. Manufacturing has largely disappeared from their daily field of vision.

In the West, somewhere between half and three-quarters of workers now perform knowledge- or information-based tasks, while a little over 10% work in manufacturing. In China, it’s more than 30% who work in factories. What we do — and what we see other people doing — shapes what we value, what we think matters and what we decide to invest in at scale.

Had Western countries retained stronger manufacturing sectors, humanoid robotics would probably be treated less like a technological sideshow and more like critical economic infrastructure. This may be the greatest opportunity Western economies have had in decades to reclaim production sovereignty from low-cost labour markets. And we are about to miss it.

The Humanoid Institute of Australia

Very soon, I’ll be releasing a white paper called The Steel Collar Economy with co-author Cameron Reilly. The paper covers the key technological and economic factors shaping a world filled with humanoid robots. It will also mark the genesis of the Humanoid Institute of Australia — a new organisation designed to prepare Australia, and the West, for this inevitable reality. It’s time to take hold - be deliberate and participate, because the social outcomes of new technology technology is always a choice that is made.

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Bubbles are a good thing. When they burst, they help us recalibrate what we are doing and why. New players and ideas get a chance to emerge. The key factor is to ensure you’re not hurt too badly if/when it pops.

As an economist, I’m reasonably certain we’re in a financial bubble around AI. (Side note: this doesn’t mean the technology itself isn’t transformative, just that the current economics around it might not be sustainable.)

Let’s have a look at the numbers and how technology behaves, and you can make up your own mind.

Trillions Going Out, Billions Coming In

Major AI companies have invested $2.4 trillion in AI infrastructure.

Yet the frontier models are generating only around $78 billion a year in revenue. (Note: this doesn’t include AI revenue from Google/Microsoft, but that is largely existential revenue where AI has been embedded.)

That is an extraordinary gap between what is being spent and what is being earned. For the investment to make sense, the gap has to be closed. The problem is that the investment in AI assets won’t remain productive for very long.

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Last weeks poll result: Will Ai replace your job? (Sample size 3000+)

Data Centres Have a Short Shelf Life

Let’s start with this fact: the GPUs inside AI data centres may need to be replaced every 2 or 3 years. Worse, upgrading the GPUs can mean replacing much of the infrastructure around them. New GPUs can’t be plugged into the old ones as an ‘upgrade’, as they are not usually backwards compatible. On top of that, their power consumption, heat dissipation and networking requirements can be fundamentally different. That means replacing the chips may also require rebuilding or substantially retrofitting the data centre itself.

These are immensely expensive assets with unusually short economic lives.

This is quite unique: previous bubbles left behind durable infrastructure. By 1900, much of America’s railway network was already in place, and parts of those transport corridors remain in use to this day. The average large US power transformer lasts around 40 years. The dot-com crash literally left billions of kilometres of excess fibre-optic cable we still use today.

Data centres will be different. Here’s a clue: how many old phones and laptops do you have sitting in a drawer or cupboard? It’s the same thing, just way more expensive.

AI data centres are quasi-temporary infrastructure—yet investors are ‘going long’ on the spend.

Video of the Week - Do you trust Meta?

The Unit Economics Are Backwards

Consider AI’s broken unit economics. For many AI firms, more customers mean more losses. Because increased usage drives up computing costs, heavier traffic often erodes profits. Compounding this, next-generation AI models require significantly more compute than their predecessors.

This leaves the industry facing three simultaneous, unsustainable pressures: poor unit economics, rapid infrastructure obsolescence (2–3 years), and capital expenditures vastly outstripping revenue.

That cannot continue indefinitely. Something has to change—here are some things that might…

The Stock Market Is Concentrated Around the Same Bet

There’s the mirage of scale—seven companies now represent roughly 35 per cent of the US stock market.

Welcome to the AI industry’s weird, circular economy. Tech giants aren’t just selling hardware; they are actively financing their own customer base by pouring enormous sums into data centres, cloud networks and the AI models that require their chips. In this closed loop, one giant’s massive capital expenditure instantly becomes another’s record-breaking revenue. If it looks like an inside game of hot potato where everyone is buying from each other, trust your instincts… And then don’t forget that most AI activity is still losing money.

The companies making most of the money from the boom are those making the ‘centres’ to hoover up and store the data—those laying the concrete, putting up the walls and providing the energy. The risk is not that AI disappears. The risk is that the financial assumptions attached to it do.

Intelligence Is Becoming a Commodity

Technology is a game where smaller is better—it always has been, and AI will be no different.

More and bigger data centres won’t necessarily lead to more or bigger profits, and may not even lead to better AI models.

If you’ve got grey hair, you may remember when computers were huge mainframes: highly centralised pieces of infrastructure. In many ways, it feels like that all over again.

And just like last time, we’ll find a way to create AI models that can operate on smaller, cheaper and more decentralised technology. We’ve seen this movie before.

Already, the price of intelligence is falling extraordinarily quickly, putting pressure on an already precarious business model.

Chamath Palihapitiya recently compared one million AI tokens—the unit of currency most AI models operate on—to a “barrel of intelligence”, likening it to a commodity such as oil.

Using his figures, that barrel costs approximately:
• $56 from Anthropic.
• $26 from OpenAI.
• $1.50 from Meta.
• $1 from xAI or Google.
• $0.50 from Chinese models.

By this comparison, some Chinese models are now approximately 112 times cheaper than Anthropic per million tokens.

If AI is analogous to oil, imagine one producer selling a barrel for $56 while another sells a comparable barrel for 50 cents—and the oil is almost a perfect substitute.

Such a price gap cannot survive forever.

The Future Is Small… LLMs → SLMs

AI is already better than most people at most things. Sure, they still need guidance, but we don’t need smarter models. We need more specific ones.

Comedian Steven Wright once said:

“I went to a general store, but they wouldn’t let me buy anything specific.”

It is a great joke. It is also a classic business insight. People and businesses rarely have general problems. They have specific ones.

A law firm does not need an AI that knows everything about astrophysics, poetry, zoology and medieval history. It needs an AI that understands contracts, legislation, precedents, discovery and legal workflows. An engineering company needs engineering AI.

A hospital needs medical AI. Yet today, we use giant general-purpose models to solve narrow, specific problems. We carry huge costs for capabilities the end user doesn’t need.

It is like hiring every academic at a university to answer one accounting question.

What we need are Small Language Models trained on narrow domains while being less resource-hungry.

Mind the Gap

The great investor Charlie Munger said, “The market can remain irrational longer than we can remain solvent.” And here we are again—albeit with the most powerful technology we’ve ever invented.

The question is whether two or three companies can each spend $200 billion, $300 billion or $400 billion a year, with the revenue not looking as though it will make up the gap.

They say AI will change everything. It’ll probably change the corporate landscape currently in its orbit too.

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Steve.

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The one skill kids need now, in the AI era, can’t be provided at school, and certainly not through a fancy university. I should know — I used to teach at Australia’s leading university.

It’s self-reliance.

The good news is that there are a number of ways to develop and nurture it. At the top of my list would be entrepreneurship. And this doesn’t mean having to go out there and build a huge company, or even launch a startup — it means being independent and being able to solve problems for money. It means knowing that when the system changes, you can adapt to the world.

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Last weeks poll result: I voted for call centres too. (Sample size 3000+)

Here are some things I think will take a child further than doing their homework. Mostly because they develop the mindset and skills to cope with uncertainty:

1. Chores — Kids who help out around the house develop accountability, discipline and pride through making a contribution. They understand the value of paying it forward, and it reduces entitlement syndrome. They learn that we all need to help each other make things work.

2. Part-Time Work — This gives teenagers real-world experience, workplace confidence, communication skills, an ability to balance priorities, teamwork and a real understanding of the value of money, how hard it is to earn — and why working for wages isn’t always that much fun.

3. Local Micro Business (Side Hustle) — Running a small business teaches kids how to attract customers, set prices, control costs, sell, negotiate, deliver good service and communicate confidently with adults.

Almost a decade ago, in my book The Lessons School Forgot, I could see the writing on the wall as to where the world was headed. Here’s a shortcut — formal qualifications matter less than ever. Sure, I also want doctors, engineers and people in healthcare to be qualified. But most degrees aren’t worth the paper they are written on, or the $100k-plus they cost these days.

*AI Video of the week - Smart & fun in situ use of AI…

Imagine investing those 3 years of a commerce degree in self-education, starting businesses, generating revenue and participating in the actual market. Any kid with half a clue, and a lot of effort would be on their way to a self-reliant future — or at least have a better story and skill base to sell to Employer X than someone with some exam results and a bit of theory.

Today, the world’s most respected companies don’t say, “Show me your degree.” They say, “Show me what you’ve done or built.” Here’s a small sample of compnaies which threw away their degree requirements: Google, Apple, IBM, Netflix, Canva, WiseTech Global, Bank of America and Ernst & Young….

Now we can add to this the fact that everyone has a PhD in their pocket, with AI that can do pretty much anything a qualified person can. So what’s left? The ability to see opportunities, seek them out and be a self-starter who literally invents their own revenue streams.

Sure, this might be via a job — and will be for most people — but never forget that a job is basically about generating revenue to support yourself and your family… We have to open our kids eyes to the reality that the future doesn’t care about their ATAR or SAT score.

Entrepreneurship is the most important skill of the future because the industrial-age deal — one career, one employer, security in exchange for compliance — is dead. School trained us to be followers for a factory system that no longer exists, so the ability to reinvent yourself, spot opportunities and build your own income streams is now a survival skill, not a career option.

Technology has made it possible: the tools that once required corporate capital — manufacturing, distribution, marketing and global reach — are now cheap or free and in everyone’s pocket. Power has shifted from ownership and size to access and connection, meaning an individual investing only their time can compete in ways that were once impossible.

In a world of constant disruption, entrepreneurship is ultimately the skill of adaptability itself — and that’s the one thing that can’t be automated or outsourced.

We are all the CEOs of our own personal services corporation. Some of us sell 100% of our services to a single employer. That’s a huge risk in this day and age.

And the best way we can teach our kids what they need to survive — and maybe even thrive — in the era of AI is to have more than one customer, and to learn the art of generating independent revenue streams that they own and control.

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No One Is Talking About This With AI Agents: Who Will Own Them?

Smart employees will build their own AI agents—little digital twins they bring to the job and take with them when they leave. Just like they do with their credentials, skills and experience.

Imagine going into a job interview and saying:

“I have 7 agents I’ve trained to perform specific tasks in this role and industry. When you employ me, you’re not just getting me—you’re getting 7 times my productive capacity.”

Much of your job might involve guiding, managing and improving those agents. They become little virtual factories you bring to work with you.

Last weeks poll result: I voted for employee ownership. (Sample size 3000+)… pretty clear result - most think Employers - makes sense as that is the current situation with corporate IP - this posit is more about the Ai you bring with you!

This could become a new form of credential in the emerging economy—something more like a degree or accumulated work experience than a project you completed for your last employer. Your agents would demonstrate what you know, what you can do and how effectively you can multiply your own output.

I already have a dozen or so little Steves doing tasks for me every day. They research, summarise, write code, organise ideas, improve drafts and help me think through problems. They are primitive today, but they are getting better—and they are getting better specifically for me.

*AI Video of the week - Could your fav TV show ever comeback?…

Replicate yourself

This is everyones opportunity - to multiply our value with AI rather than be replaced by it. Maybe your could demand a higher salary based on your BYO agents? Generate 2x, 5x or 10x your current income… why not? That’s capitalism right - creating and capturing value? But it’s gonna be a race on who claims ownership … remember, the only reason our data is someone else’s asset (Big Tech) is because we let them take it…

Like everything in economics, it isn’t only value that matters. It is ownership and control.

We have a choice: Train AI for someone else or train our own. There are already open-source language models and tools that can be trained by you, on your knowledge, for your benefit. You can download one for free on Hugging Face.

If you build an agent entirely inside a company’s platform, trained on company systems and company data, the company will probably claim that it belongs to them. If you build it independently, on your own infrastructure and using your own accumulated knowledge, it could become a personal asset that grows with you throughout your career.

Your agents need to be more like qualifications you own and less like projects you completed for a company. You should be able to bring them to your next employer, where they become smarter, stronger and more valuable with every job they perform.

You could and should say: “If you employ me, you get access to my agents too.” A freelance tradie brigs their tools with them, why should people working in corproate environments?

This could make you dramatically more valuable. It could also help de-risk your future because you would own a productive asset that improves over time—an asset no employer can simply take away when your job ends.

AI & IP

But bringing your own AI—BYO AI—creates some difficult questions.

Should an employee receive additional compensation for bringing agents that generate value beyond their individual human capacity? Who owns the work those agents produce during company time? What happens when a personal agent improves because of the company’s data, customers and processes? Can the company retain a copy when the employee leaves? Can the employee take the improved agent with them?

Human beings already leave every job smarter and more experienced. Companies do not own the lessons inside our heads. But AI makes those lessons visible, replicable and commercially valuable. An agent can retain processes, repeat them endlessly and potentially transfer them to a competitor in seconds.

There will need to be boundaries between personal knowledge, company intellectual property, confidential data and the capabilities an agent develops while doing the work. Employment contracts may eventually contain specific clauses covering personally owned agents, just as they currently cover inventions, intellectual property and restraints of trade.

Already companies are making factory workers wear cameras on their heads to train AI’s and even Facebook (Meta) records every keystroke of the employees… My advice? - Make sure you own your likeness. Replicate your skills before someone else does it without asking.

I don’t know exactly where those boundaries should sit. But I do know this: the question of who owns the agents will be more important than the agents themselves.

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They just double-ended the internet with Universal Cart. Potentially making the earth their shopping mall.

What is it? Google Universal Cart is an intelligent shopping AI hub that can search the web, find whatever you’re looking to buy autonomously—even a multitude of items from different merchants—and put them into a single cart for checkout.

Seriously, I think they’ve just potentially doubled their revenue!

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Last weeks poll result: I voted for the 1st option. (Sample size over 3000)… pretty clear result - people want to Gov. to own utilities again.

Remember, Google currently makes most of its money from vendors—people trying to sell stuff or get clicks on the web… and it works. The company is currently worth US$4.47 trillion. But now imagine Google combining its entire ecosystem—Search, Gemini, YouTube, Maps, Home, Nest, Gmail, Drive, Waymo, Calendar, Docs, Wallet, etc…

…into a single, persistent, always-on deal-tracking service, scouring the entire web, to find you that deal—the single best option on the web, with time, price and delivery all calculated to your benefit.

Or a product you couldn’t find—or a product you didn’t even know you wanted…That item you’ve been watching on YouTube. (For me, it’s a 1986 Honda CR80 dirt bike. Hard to find.) Then bam—it’s found it and ordered it for you. It’s on its way… well under budget. Incredible.

*AI Video of the week - Mind blowing story telling…

But here’s where it gets economic for them. It becomes a subscription service—an always-on, best-deal service ready to book your next holiday in its entirety, replenish your café stock, or change your electricity deal in real time, daily, to save you cash. It’s a service you’d pay for—especially if it is saving you hundreds of dollars a week and precious time. And you will pay for it. Surely that’s their plan.

Google might just corner the entire e-commerce market. It could make money from both buyers and sellers. Something Amazon would love (Side note: Amazon have done some buyer centric things that really worked - like Amazon Prime - because people are getting free delivery, they hope Amazon has what they want and often don’t look elsewhere - even if it was cheaper with delivery costed up) But unlike this Amazon example, Google won’t even need any physical infrastructure to service that market. It only needs the information and Agentic layer. It will be connecting other people’s desires with other businesses’ infrastructure. This is way more significant than we realise, and it shows the power of Big Tech once more.

And for those who thought Google would lose to generative AI, it seems its code red worked and it has come back with a… “ChatGPT—hold my beer.”

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Keep thinking,

Steve.

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Listen to Steve read this post w stumbles to prove it’s me & not AI! (10 min audio)

Musk is the world’s first trillionaire! It comes with good news, you’re now closer to the wealth of Jeff Bezos than Bezos is to the wealth of Musk!

Fun Fact: Elon Musk and his companies have received at least $38 billion in government subsidies.

But I want to pose an important question: Can you be a capitalist, and believe in taxing wealth? An unequivocal YES. And no, it won’t kill the incentive to thrive.

So here’s some notes on how capitalism and taxing inordinate wealth can exist in the same economy. All coming from a raving capitalist who believes in accumulating assets (to an extent) and where and why we should draw the line.

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I’ll start with why: I have worked with and dined with quite a few billionaires. And all of them have told me the following (though they wouldn’t say it publicly, and I don’t blame them): They all told me: accumulating billions rarely comes from pure genius. It usually comes from finding a glitch in the matrix. A loophole. A blind spot. A broken system. A gap between what the law intended and what the law allowed…

Tax arbitrage. Consumer-law grey zones. Cross-border loopholes. Political influence. Early access to public infrastructure, government contracts, data, spectrum, land, subsidies or monopoly positions. Luck played a massive role. Being in the right place before the law caught up, before regulators understood the trick, before the market and consumers realise what was being extracted.

The doors that opened for them were often doors that should never have been left open.

My take: Getting rich at the service of the many is fine, and ought to be encouraged. Getting richer at the expense of the many is why regulations need to exist.

A few things:
Firstly: How much is a trillion? If you earned $10,000 a day it would take 273,972 years to earn that much, or 32,000 years if you earned a dollar a second ($86k per day). It’s a lot. More than anyone needs - unless control is your goal.

Secondly:
Money creates power, and power leads to control. Control thwarts participation and ends in regulatory capture and monopoly positions. Capitalism (in a mixed market economy) is about freedom, and the opportunity to participate and start from anywhere. To create, invent, build and earn - yes, some parties have always had advantage - but we are now entering a quasi-feudalist techno-king era... We can all feel it won’t end well. We need to recalibrate the market. Even Adam Smith said the fundamental flaw of capitalism is concentration of wealth and the need for periodic resets. One is due.

Thirdly:
Capitalism is the best system - it’s not even close - it’s lifted more people out of poverty and improved living standards more than any system in history - just look at China since 1980. But capitalism needs guardrails. (Their model is probably better with more controls - just ask Jack Ma). We are living under the umbrella of capitalistic benefits. Everything around us - all the technology, entertainment, food and modern conveniences are functions of innovation via entrepreneurs inside a capitalist free market. Ones government helped fund - we’re standing on taxpayer shoulders (the internet is Gov funded) - not just industrial and techno giants. The danger is the markets are becoming less free and ‘owned’ by the mega rich… whose desire for more money is really about more control.

Fourthly:
We already tax wealth, just the wrong kind - earned wealth. We hurt the middle, let the top get away with not paying. We tax earned income far too high, and corporations and wealth far too low. The jury is in Mr Reagan: lower corp taxes don’t lead to jobs & growth, but inequality. The economy is a biological ecosystem - a rainforest, the more thriving species, the healthier it is - the easier it can recover from black swan events. The species (people) can find their niches. An invasive species (read here: billionaires and trillionaires) - poison the ecology.

Lastly: Some tax ideas worth discussing…

• Tax wealth and income separately.

• Start taxing wealth on assets outside of primary home.

• Tax unrealised gains above $100m. Make the wealthy sell assets if required. (We already do this with land tax - so we know it is possible).

• Income taxes on earnings should be lower - around 30% up until $200k plus.

• Corporate tax for corporations over $1 billion revenue - 5% of local revenue, or 49% of profit (whichever is greater for the Gov).

• Annual wealth tax - 1% on assets above $100m increasing 1% every $100m progressively, $100m-$200m at 2% and so on… to a max of 10% for assets above $1 billion - per annum. Yes - they may have to sell to pay. Too bad.

In Australia - it would only lose 4,630 votes - that’s the number of people with assets above $100m. We’d only be taxing the 0.01%. Their lives would be totally unchanged. In Australia alone, this would generate around $70 billion a year, which would represent approximately 8.3% of Australia’s total annual tax take of $839 billion, or about 20% of the $347.1 billion currently collected through personal income tax. Imagine how much healthcare, education and infrastructure that could provide! It could change a lot of lives for the better.

BTW - if you think these tax levels kill freedom and economic incentive, your mind has been captured by those much wealthier than you. FYI - this proposal would hurt me financially via my company setups - but the world I live in matters more than the money I’d need to hand back.

Keep thinking,

Steve.

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PS - I posted this ideas in less detail on Linkedin but it only had 26 views - seems strange when my average post gets 5000-10000 views….. So I thought it was worth sharing here.

Listen to Steve read this post (7 min audio)

The internet used to about searching for information. A place to explore the world’s largest filing cabinet of human knowledge. We’d be presented with options, then we’d analyse it, and decide for ourselves.

Today — we get an answer — a step has been removed, and that step is human judgment.

AI overviews are now the default on Google Search. I guess we should now call it Google Answer. Commercially, they didn’t really have a choice, as search was quickly being replaced with AI engines like ChatGPT and Claude. To be fair, Google has done a tremendous job since their internal code red and pivoted radically towards an AI-driven future — in short, they didn’t do a Kodak. Kudos to them.

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Previous AI Quiz: A draw - first time ever! (AI was on the left)

The basic entry point of the internet, Google, now has a totally different menu. In order, we now see the following after a search:

1. AI Summary

2. A few videos on the topic, via YouTube of course

3. “People also ask” with more AI summaries

4. Then some relevant links, largely below the fold

We have moved from options to answers. The primary result now is an AI Summary — most of which are pretty damn good. How good? Recent research shows it is around 90% good! Which, on the surface, sounds like a very nice number — accurate, reliable. But when you take into account that Google serves over 5 trillion searches a year, that translates into tens of millions of incorrect answers every single hour. For my readers in Australia, the number is around 25 million inaccurate AI summaries a day.

Here’s a thought experiment worth taking: Would you eat food if the ingredients list was accurate 90% of the time, take medicine that did what it said 90% of the time, or drive a car if the brakes worked 90% of the time? I wouldn’t, but for some reason it’s OK for Big Tech companies.

Who do you Trust?

And it is pretty easy to be tricked that the summary is accurate — because the research also shows that the top sources provided with the AI summary, the link you can click to see where the AI summary came from, often do not even support the claim which the AI made. If it says Oxford University was the source, then most people would trust it, because very few people click on the link to check if something is true. I know from writing this blog that hardly anyone clicks on links with research. My blog stats show that around 1% click on research links I provide. (I bet you didn’t click on the one above… all of a sudden I’ll get my highest click-through rate yet!)

The reason is that you trust me — and I’m glad you do. The problem is that we’ve now entered an era where the source is more important than ever. In a world where anything and anyone can be faked, knowing where something came from is vital, and not enough of us are asking that question.

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How to Internet?

AI slop is easy to spot. Plausible, erudite, yet incorrect AI summaries are a far more difficult proposition.

It means we and our kids need a new skill base when it comes to using an AI-driven internet. We need judgment and the patience to dig into what we see to test the veracity of what was served up. So while AI has reduced our workload in one area by finding answers or providing information, instead of us having to find it and summarise it, it has also increased the workload in another area. We now have to dig in and see if what it served up was true, and assess the nuance. Not always, but often.

Courier or Curator?

As time passes, we’ll notice a difference in the diligence of people. There’ll be couriers and curators. The couriers will be those who simply deliver what they’ve been given, and to be quite honest, they’ll put their career at risk by doing this. And there’ll be the curators — those that dig deep, cross-reference, and decide what they think based on what and where the information came from. The choice is ours to make, and like always, the easiest path with the least friction will probably be the one that yields the worst results.

Choose wisely.

Keep Thinking,

Steve

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Listen to Steve read this post below (6m audio)

No one can predict the future. What we can do is understand emerging technology, social patterns and economics to make sense of what might come next.

While my moniker is “Futurist”, my speaking agent once told me: “It’s better than ‘Technology Economist’, and you’ll get paid 10% more for each keynote.”

(If you’re about to book me, ask for a 10% offset discount! 😉)

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Previous AI Quiz: Close, but most correct - AI was on the right.

How to Approach the Future

In my 2017 book, The Lessons School Forgot, I talked about how AI voice assistants would radically improve. I even predicted that in five years, they wouldn’t just be a little better, but would have a PhD in every single subject, with the ability to understand everything humans do. Then ChatGPT arrived in late 2022 and could do exactly as I predicted.

My books and blog are filled with many accurate predictions of what technology would do. You can see an actual excerpt below.

I saw the play, but not the early winner: ChatGPT.

Technology is predictable because it follows a particular pattern at an industry level. Players iterate on each other’s innovations as an unintended ecosystem collective. We also build what we imagine, and plan the future by all working on that possibility.

Who gets there first, though, is never easy to predict.

And predicting the winner isn’t that important unless you’re trying to pick stocks. Our job in business, and society for that matter, should be more about understanding what is likely to come next. What will technology make possible? Then we need to think about how this might affect our supply chain, where money flows, and how we get our share of it. Will it change our company, our industry, or even society? How should we react to this tech when it arrives?

Thinking about the future gives us a chance to respond more quickly when it arrives. And we can usually see what’s possible well ahead of time. I’m currently predicting humanoid robots will take the world by storm around 2030. While this will surprise most people, it really shouldn’t. The trajectory is clear.

AI Quiz:

But Tech Is Not Enough

Technology doesn’t tell the story of the future; it’s more of an ingredient. The future is always a mix of technology, human behaviour and economics. We need to mash these three elements together to see where society is heading.

Anthropology: Human behaviour doesn’t change much. We are running very old software as humans, DNA is a 400,000-year-old code base. Having a solid understanding of human psychology and our history is vital. We’re social creatures, and if something isn’t acceptable to the masses, it won’t change the business landscape, no matter how much a tech executive wants it to.

Economics: This is what ties technology and anthropology together. Unless the new tech is easy to substitute, with a good business model underneath it, it won’t be survive. Some things take decades; some happen quickly. The lower the switching costs, the quicker the adoption. This is why ChatGPT exploded to 100 million users in three months: it was super easy to go there, instead of Google.

As a Futurist, this is my “secret” template. It enables me to blow minds on stage during keynote speeches and provide future-proof strategy for any company, in any industry.

If you ever need help, you know where to find me!

Keep Thinking,

Steve.

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A huge amount of jobs people do are already unnecessary.

Therefore, AI or a robot being able to do these jobs is totally irrelevant. Of course, this isn’t you! But it was definitely me…

Early on in my career, before I started my first software company, I worked in marketing for multinational consumer goods companies. I was in brand management — and much of what you can do depends on your marketing budget. On a number of occasions, my budgets got cut. This was so the company could make its profit numbers for the year. It meant I could no longer do much of the work that was planned to generate sales. I literally did nothing for months on end.

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I mean, I did some busy work — ran reports, did some market share updates to present to management, stuff like that — but none of it mattered. It was totally pointless. I had days and weeks where I just surfed the web, went to meetings, spoke to people in the office and had coffees.

I was earning well over $100k, and this was over 20 years ago. And I wasn’t alone — others in the firms I was at were in the same situation. And it wasn’t just once. Of the 4 companies I worked at, it happened at 2 of them. Months of getting paid for doing nothing. In addition to this, all of these companies had many of the people in their corporate head offices doing ‘internal busy work’. Stuff someone wants done inside the building, but has no impact on the performance of the company or wider society — other than to give the person doing the work an income. I imagine there’s a good chunk of these in every firm and government department.

Previous AI Quiz: Most wrong AI was on left - It was a v tricky one.

Of course, this doesn’t mean the people doing these ‘bullshit jobs’ aren’t diligent and don’t want to do their best work; it’s just a reflection of the true abundance that exists in a modern technological economy. An abundance which arrived not long after industrialisation. It’s worth remembering that before the Industrial Revolution, 90% of humans worked in food production; now it’s around 5% in Western economies.

In the corporate environment, lots of people are there simply because the company can afford it, not because they create anything. A colleague said to me: “Unless you’re in the factory making the products that go in the brown cardboard boxes, or driving the truck to deliver it, not much that happens at head office really matters - we are riding the work that was done years ago.” I never forgot that.

Just think back to COVID times. (Sorry!) We found out very quickly which jobs were necessary. I wasn’t one of them. It was all the functional stuff — people making the food, keeping the lights on, helping the sick and infirmed. During the pandemic, an estimated 34–43% of the workforce were in essential industries. So when we take this uncomfortable reality into account — what is actually essential — and add this to the people in other non-essential industries, who are, let’s call them, optional workers, then we can really see the truth of being replaced by machines.

AI Quiz:

Machines have been taking jobs for a very long time. Consistently, but slowly. It’s mostly singular automation — the checkout operator is a classic. We now do self-checkout — and the AI can now tell it’s oranges and not carrots as it films you while you scan the items. Soon, we’ll just walk out of the grocery store and AI will calculate our basket of goods.

The net outcome of all this is quite simple. Humans invent work because we can. Because the company can afford it, and because we want to have a little empire of people under us — not because there is no other way to get things done, or even because a task needs done at all.

It’s one of the main reasons (though there are others) - why I don’t panic when people say AI will cause a job apocalypse. And when it comes to recent announcements of companies laying off staff due to AI, it’s mostly to justify cost cuts and be seen as innovative, applying AI and thereby drive a share price boost. And of course, these jobs will pop up somewhere else. They always do.

Keep Thinking,

Steve

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Listen to Steve read this post below (15m audio)

Fortunes are made because they usually require big investment, the building out of infrastructure, and shifts in the geopolitical landscape. Fortunes are lost because of incumbents ignore these shifts because they take so long. During the build-out, bubbles often burst, allowing yesterday’s heroes to say, “I told you so…” before they eventually get usurped. Big Media missing the internet is still the best example.

The second most common question I get asked after a keynote is about big trends. And with that in mind, here are my top 7 big shifts—the shifts we’ve all got time to navigate around to adapt or start something new and big.

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Previous AI Quiz: Most wrong - AI was on the left!

1. Deglobalisation

The era of hyper-globalisation is over. We’ve had three recent hits to remind us of the risks that come with the global marketplace. First Covid, then Trump’s Liberation Day Tariffs, and even the most recent war in Iran. The days of free trade are quickly being replaced with national-first focused policies. This doesn’t mean we’ll stop trading globally; rather, that global trade will retract. And it already is.

In the 1990s and early 2000s, global trade typically grew twice as fast as global GDP. In 2026, the WTO projects trade will grow by only 1.9%, while GDP is expected to grow by 2.8%. Global merchandise trade (the sum of world exports and imports) is now 44.8% of global GDP, down from its all-time high of 53.3% just over a decade ago. Remember, these shifts take decades. The good news is that this gives us time to recalibrate around them.

2. Re-shoring

We are moving from a “just in time” to “just in case” Economy. Manufacturing will return to high-cost labour markets. While Covid showed us the risks of international reliance, AI and robotics are about to remove the advantage developing markets currently have in manufacturing goods.

In the 1990s, the West literally taught China how to manufacture quality at scale. It was done via joint ventures from the promise of lower-cost goods to sell in home markets. Fast forward 25 years, and China has now surpassed the West in all forms of manufacturing. But the most important of these is humanoid robots. I’ve written about them before here, and I believe this shift is bigger than informational AI. I call it Physical AI—where we put brains into machines, especially human-shaped ones.

We quite literally need humanoid robots for two reasons. One, we don’t have enough humans; our global population has now turned the corner into decline based on global birth rates per female. Two, we have a human-shaped world. Houses, hospitals, and retail environments need flexible, dextrous robots who can do a task the first time they’ve seen it or literally had it explained to them. That’s coming quicker than we think. Expect to have humanoid robots which follow verbal and visual instructions with a PhD in every subject for around $30,000 by 2030. Yes, you’ll own one. And when this happens, we’ll get China’s robots to help make our robots and reverse what China did to the West, using their technology and processes to reinvent ours.

3. AI as Infrastructure

AI is no longer a feature, but a foundational utility of the 21st century. There are a lot of clues around this idea because at the same time we hear about huge amounts of investment (up to $7 trillion globally by 2030), we are also hearing rumblings about whether this will ever get a return on investment and people not wanting it built near where they live (all the while using Generative AI a zillion times a day). This is what has happened every time we’ve had large infrastructure shifts since industrialisation.

As of today, the investment in AI now rivals investment in roads and power grids. The US Interstate Highway System took 35 years to complete at an average annual cost of $18.8 billion; the AI data center build-out is currently spending that same annual sum every 5 weeks. It raises important questions about corporate power and ownership of the major factors of production. AI falls into that category and shouldn’t be in private hands. Smart countries will remove it from private operators, just like we did with the electricity grid in the Gilded Age.

4. Data is Energy

Big Tech is pivoting to becoming global energy titans; they have to in order to sustain the massive power requirements of AI. Google, Microsoft, and Amazon are securing their “compute” future by investing heavily in nuclear power, ranging from small modular reactors to reviving dormant plants like Three Mile Island. This shift blurs the line between information and physical energy, as AI data centers—already consuming 5% of the U.S. power supply—are projected to triple their demand by 2030.

This “Big Energy” move serves as a strategic “Monopoly 2.0” that allows tech giants to expand while avoiding antitrust scrutiny. By applying the Amazon Web Services (AWS) model to the power grid, these firms will generate excess carbon-free energy and sell it back to consumers, potentially displacing traditional utility and oil firms. This consolidation of AI, information, and infrastructure grants Big Tech a level of influence that rivals nation-states, positioning them as the new global colonizers of the 21st century.

5. The Conglomerate Comeback

There’s a reason Big Tech is doing so well in the share market. They remembered it is better to own than rent. And so, whenever they can, they use their excess funds to build or acquire the resources they need to expand. Outsourcing creates global shock risk. Building a giant supply chain in-house now fits with the emergent market dynamics listed above.

Take Amazon, for example: it didn’t outsource its supply chain—it bought every layer of it, then opened each layer up as a paid service to competitors. AWS was the template. Amazon owns 110+ cargo jets, 175,000 delivery vehicles, 1 million+ warehouse robots (via the Kiva acquisition), 185+ fulfilment centers, its own nuclear power plant, 450 satellites in orbit, and 500+ Whole Foods stores. This pattern is also evident in most of what Musk is doing, as well as Google. The most powerful companies of yesteryear did this too: in the 1920s, Henry Ford built a massive, pre-fabricated industrial town in the Amazon rainforest to secure a direct source of rubber for car tires. During the peak of globalisation, companies moved to focus on singular core businesses. That’s now in reverse.

AI Quiz:

6. Inequality as Policy

I’ll start with this: the world’s 12 richest billionaires now own more wealth than the bottom half of humanity (more than 4 billion people). It’s worse in the USA. Elon Musk has more wealth than the bottom 51% of the entire nation. The Gini coefficient is how economists measure inequality: zero indicates everyone has exactly the same wealth; a score of 1.0 means one person owns everything. The US is above 0.8 and in Australia, it is 0.61. It’s not your imagination—wealth is accumulating at the top. It’s why our economy lives in the extremes: people with 10 houses and people who can’t afford a house—or as I like to call it, the Tiffany’s and Costco Economy.

We can expect the government to start responding heavily with wealth taxes in addition to income taxes. They have to—otherwise, it will end in war, famine, or revolution; it always has. In Australia tomorrow, the new budget will be released with policies that hit inequality around investment properties and capital accumulation. But what we really need is a courageous government to go hard on corporate tax avoidance (easy to fix actually, just tax them on 5% of revenue), billionaires, and resources taxes. Even the CEO’s I work with of large public corporations agree who they work for should pay more tax - we all do.

I think and hope they are starting to get the message that they should chase the top end of town, not everyone somewhere in the middle trying to get ahead and secure a dignified retirement. The middle class was a great post-WWII invention and one that is worth maintaining. Globally, 74% of millionaires actually agree with increasing wealth taxes, as do I. Billionaires would do well to think the same way (be less greedy) if they want to remain at the top of the tree.

7. Re-nationalisation of Infrastructure

Smart governments are taking back the keys to essential services in the name of national security and resilience. The UK launched Great British Energy in 2025 with £8.3 billion in public capital to drive the clean-power transition. In France, the full nationalisation of EDF has allowed the government to cap energy prices and commit €72.8 billion to new nuclear reactors—the kind of long-term, nation-scale investment private markets were unwilling to make.

In Australia, it is mostly just rumblings—but a huge majority of voters want higher taxes on our fossil fuels, and public infrastructure back in the hands of “the public.” Simply because the jury is in: our services are now often worse and somehow cost more. Australia’s privatized services have evolved into a mandatory cost-of-living tax, with utility price increases consistently outstripping inflation. Electricity leads the surge with a 20% price hike following the “rebate cliff,” with electricity bills nearly five times the rate of inflation. The Transurban Tax evolves from a terrible deal where the government allows a fixed 4.25% escalation in prices per year. Meanwhile, corporatized water utilities have resulted in water prices doubling the inflation rate over the past decade. People are sick of it.

Now that people are aware of the heist, smart governments (let’s hope we get one) will claw back essential services, probably in a 51/49 private deal, so they can manage the cost of living and infrastructure investment, and provide employment and training enclaves.

- - -

Thanks for reading, and I’d love to hear your thoughts.

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Steve.

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Listen to Steve read this post below (14m audio)

What are Prediction Markets?

Prediction markets are taking the world by storm, but thankfully the winds are yet to blow through Australia (we’ve got enough gambling problems as it is). The two big players are Polymarket and Kalshi, who collectively control 99% of the market. The way they work is pretty simple:

Imagine a horse race, but instead of the bookmaker setting odds, everyone trades little $1 tickets with each other before and during the race.

Each ticket pays:

$1 if it wins
$0 if it loses

But you don’t usually buy the ticket for $1. You buy it at the current market price. So if the market says a horse has a 45% chance of winning, the ticket might trade for about 45 cents. If the market says it has a 55% chance, the ticket might trade for about 55 cents. You can buy a ‘Yes’ or ‘No’ ticket. The prices are the inverse of each other.

That price matters because it determines your upside. If you buy a YES ticket at 45 cents and it wins, you get $1. Your profit is: $1.00 payout minus $0.45 cost = 55 cents profit. If you buy NO at 55 cents and it wins: $1.00 payout minus $0.55 cost = 45 cents profit.

So the higher the market probability of X occurring, the more expensive the ticket becomes — and the less profit is left if you are correct.

Before the result, the ticket price moves up and down based on what people think will happen, as these markets are live and often changing. If new information makes the outcome look more likely, the ticket might rise from 45c to 70c. You can then sell it before the final result and make money from the price movement.

The market makers take fees for the service of around 1–2%.

So prediction markets are basically a live horse race market where the odds are set by traders, rather than a bookmaker — and every outcome ticket is worth either $1 or nothing at the end.

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Previous AI Quiz: Most wrong ever. AI was on left (B).

Two flies crawling up a wall.

If you haven’t already heard, they allow people to bet on — sorry, ‘predict’ — an outcome of pretty much anything. And by anything, they really mean anything. Here’s my top 20 weirdest markets we’ve already seen:

1. Will Jesus Christ Return Before GTA 6?

2. Will Trump Say “Hottest” During His Meeting With Keir Starmer?

3. Will Ukrainian President Volodymyr Zelensky wear a suit before July?

4. Is Donald Trump The Real Satoshi Nakamoto?

5. Is the Earth Flat? (A market asking a question with a scientifically known answer, essentially serving as a “troll” or sentiment liquidity test.)

6. Will TIME Person of the Year be AI?

7. Bad Bunny’s Opener: A bet on the specific song Bad Bunny would use to open his halftime performance at the Super Bowl.

8. How much snow will fall in Central Park on day X: Traders use Times Square webcams to bet on the exact inch-count of snow during New York megastorms.

9. Will an AI-generated influencer hit 10M followers?

10. Will AI replace a Fortune 500 CEO officially before 2030?

11. Will scientists revive an extinct species, e.g. mammoth, this decade?

12. Will a human live past 130 years?

13. Will lab-grown meat outsell real meat in any country by 2030?

14. Will a billionaire buy a small nation or territory?

15. Will a deepfake video spark a major geopolitical incident?

16. Will a virtual influencer star in a major Hollywood film?

17. Will a YouTuber become a head of state?

18. Will Bitcoin hit $1M by 2030?

19. Will the US abolish physical cash?

20. Will humans land on Mars before 2035?

And yes, you can bet on the stock standard events, like who will win the next election or win a sporting event. But among the things people can ‘predict’ are those where insiders can have direct knowledge (even some of the above fit that), such as government policy decisions, the execution of military operations, upcoming corporate earnings, and technology product releases.

Here’s my Predictions

1. I’m predicting an increase in gambling addiction among young men: The key demographic happens to be, you guessed it, 18–34-year-old males. They are currently around 55% of the user base on Polymarket and Kalshi. (Notice how drug dealers call their customers ‘users’ too.)

2. I’m predicting insider trading: People with insider knowledge will leverage their positions for profit.

This trajectory on my predictions above is already quite clear.

Young Men: On Polymarket, approximately 84% of all participants have realised net losses, with the top 1% of users, largely automated bots and institutional traders, capturing over 76% of all trading gains. The average account lost over $1000 in 2025.

Insider Trading: In early January 2026, a Polymarket user named “Burdensome-Mix” placed a $32,500 bet on the US-led seizure of Venezuelan president Maduro, winning over $436,000. And in February 2026, six new Polymarket accounts placed highly specific, winning bets that a US strike would occur on Iran by February 28, with the accounts netting $1.2 million in combined profits.

The kicker is these bets, sorry ‘predictions’, can be placed anonymously on Polymarket via crypto wallets.

Is it Gambling?

Of course, the CEOs of these businesses claim the sites are not gambling. The CEOs of Polymarket, Shayne Coplan, and Kalshi, Tarek Mansour, claim their platforms are not gambling hubs, but financial exchanges. And believe it or not, they even call them ‘truth-seeking utilities’.

Their justifications are as follows:

Mansour: “There is no house that wins when customers lose. This means that Kalshi doesn’t hook losers and penalize winners... It’s a financial market, not a casino…. It’s the most accurate thing we have as mankind right now, until someone else creates some sort of a super crystal ball... It creates this information that’s really useful for people.”

Coplan: “Unlike a betting site where you make a bet and it’s against the house, here you own a share. You could almost say it’s similar to a stock, but it’s not a stock…. Prediction markets are an ‘antidote’ to the problems of living in a world where we have an abundance of information but no way to filter the noise and discern what’s real from what’s not.”

Both arguments are total bunk. (Polymarket’s tagline even says: Bet on your beliefs)

Just because people aren’t betting against the house doesn’t mean people aren’t gambling or losing. In casinos, there are plenty of games people play against each other rather than the house — and well, prediction markets are quite literally the house, because they provide the platform. And you know how they say the house always wins? Well, the two big players’ market capitalisations look like wins to me. Kalshi is currently valued at $22 billion, and Polymarket at $15 billion.

And as far as them being truth-seeking utilities, anyone who has ever done a modicum of market research will tell you, unless the population is represented in those surveyed, it doesn’t represent social sentiment. Last time I looked, 18–34-year-old men don’t represent 55% of the world we live in. Thankfully. And just because money is being placed against a belief doesn’t make it an inevitable truth.

Despite this, some important media firms have taken the bait, and are using Prediction Markets as if they are quantitative market research. These include CNN, CNBC, Dow Jones, The Wall Street Journal, Bloomberg and The Economist, who are all now weaving these ‘real-time probabilities’ into their analysis.

(It’s worth noting that as of 2026, the traditional gambling industry and major casino operators have launched a coordinated legal and political offensive against them.)

What about the hedge?

To be fair, there is some non-gambling utility in these platforms. And that is by using them as a hedge against real-world outcomes you could be a loser in. Similar to how a farmer might use futures options to hedge against crop failure.

On a prediction market, maybe someone with student debt, who is worried about a student relief bill not passing, could purchase a contract and get a payout even if a law doesn’t pass. At this point, however, research shows traditional hedging is used by less than 5% of traders.

My net outtake is that prediction markets are the YouTube of casinos: online forums to bet on anything, created by users for users. The house skims markets. Unironically, the social sentiment of prediction markets is no more reliable than the comments section of a viral YouTube video.

While there are some differences, it’s a classic play of redefining a digital market and the language used to dodge regulation. Remember, Big Tech said they are not media companies so they could publish whatever they wanted!

AI Quiz:

So What?

Why does this even matter to people like me who don’t gamble? It matters because it affects the fabric of society. The next generation, who already have the odds stacked against them (see what I did there!), now have another way to distract them from the real issues.

It’s another piece in the gambling epidemic facing young men — a scourge already doing serious damage. Young men have higher-risk profiles, and their brains are still developing into their late 20s. Worse still, these markets extract from their financial future by leveraging their optimism, risk appetite, and desire to find a path forward in a world that is already difficult to navigate financially.

Our species’ ability to take risks has been a large contributor to the modern world we take for granted. Exploration, new technology, and entrepreneurial success are all a result of calculated risk for improved societal outcomes, and they ought to be encouraged.

But there are some things we should never gamble on, and young people’s futures should be on top of that list.

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Mark Zuckerberg’s recent announcement of him creating an AI version of himself shouldn’t really surprise anyone. He’s quite obsessed with a few things which have made him one of the richest people in the world. Those obsessions include control over his business (Meta), and the virtualisation of all human connection.

Control: When it comes to control, Zuckerberg has total voting control of Meta (a US$1.71 trillion corporation). Despite only holding 14%, he has 61% of the voting rights and can never be usurped.

Virtualisation: The average internet user spends 6–7 hours of their waking life (around 37% of their life) on screen, and 31% of all their online time on one of Mr Zuckerberg’s platforms.

While I’m not sure this has worked out well for humanity (especially teens), it has worked out well for Uncle Mark, who currently has a net worth of US$239 billion. And despite the $88 billion failure of the Metaverse, the idea to create an AI digital twin of Zuckerberg has legs (See what I did there?)

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Previous AI Quiz: Most wrong. AI was on the right.

The Zuckerberg AI Clone

The short summary of his plan goes a little like this: Mark Zuckerberg is developing a photo-realistic AI clone of himself. It will facilitate interactions with and provide feedback to employees. It is to be trained on his voice, mannerisms, and even his strategic thinking. According to reports, his 3D avatar is designed to increase efficiency and employee connection (hmm, really?), by handling meetings and routine tasks, reflecting a shift toward “founder mode” which will allow him to remove himself from the day-to-day and focus on AI strategy.

I think it runs deeper. This is the preamble to the world’s first AI CEO. Because the only person Zuckerberg would ever allow to replace him would be ‘himself’.

This is his plan to be the CEO after his death — with the AI virtual posthumous version of himself. Or possibly if he steps down from the role — albeit unlikely.

It raises some interesting questions. Would it be legally possible for a virtual AI CEO? They’d probably do a better job in many cases. Could this then result in the CEO not having to be paid, saving inordinate millions in salary? Or could that salary go to the owners of the AI?

At present, AI can’t be a legal person — it can’t sign contracts, own anything, be sued, or be held accountable. In 2026, corporate law is still built on human responsibility — fiduciary duty, judgment, consequences. When things go wrong, someone has to wear it. Right now, that’s still a human. But I’m not convinced that won’t change. We need to remember that ‘corporations’ were literally invented to be quasi legal entities to reduce human liability. As far as I can tell, it is only a matter of time before AIs get human rights to do all of the things I’ve mentioned above which are required to run a company.

AI Quiz:

Your Life, Their Asset

But it goes further. Zuckerberg might be training an AI version of himself, but it’s not that much of a stretch to train an AI version of anyone who uses any Meta platform. All those pictures, thoughts, mannerisms, ideas, value systems which Meta has accumulated in the many posts most people make are — wait for it — the property of Meta. This is all clearly laid out in the updated terms and conditions you never read and said yes to.

Yep — he’ll launch a version of your mum, dad or daughter. The kicker is that you’ll have to ‘pay him’ to have continued communication with your loved ones after they die. The AI version of himself he is creating is a test case to make AI posthumous versions of Facebook, Instagram, Threads and WhatsApp user relationships … he intends to monetise — forever.

Zuckerberg is very into forever — from both a control and customer perspective.

If you think it’s unlikely, remember their record and the atrocities (yes, atrocities) committed in the pursuit of profit. Significant anxiety and depression tied to heavy use; Internal research linking Instagram to declining teen mental health and self-harm; a California jury finding them guilty for addictive design targeting young users; under-13 data collection without consent; ignoring known child-grooming activity in DMs; outsourcing moderation to low-paid workers, exposing them to abuse, rape, murder, and suicide - many reporting severe PTSD; and the live-streaming of real-world terror attacks such as that in Christchurch, turning an atrocity into content. Individually disturbing. Together, it’s a system optimised for attention at any human cost.

Legal Battle Ahead

The most important legal developments for AI are still undetermined — yet simple. We must own the rights to our own personas — in all its forms. Our face, thoughts, actions, voices and every form of likeness need to be copyrighted to ourselves, forever.

And if you thought that lawyers will be out of a job due to AI — then you need to revise that idea … some of the most important work they’ll ever do, is only just beginning.

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It seems like forever, but functional AIs have been around for less than 3 years.

In that time, we’ve seen an inordinate amount of new versions and features arrive. I actually added them up. For OpenAI alone, it’s well over 400 updates, with 150 of these being substantive changes. Not just new versions, but radical feature upgrades including voice, live video, live web search, a web browser, data analysis, image analysis, file analysis, image generation, Sora video generation, persistent memory, custom instructions, agent mode, and custom GPTs/apps, to name a few…

That’s crazy.

Of course, we can add to this the arrival of Gemini, Claude, Perplexity, Grok, Mistral, DeepSeek, and all their features.

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Previous AI Quiz: Close, but most wrong. AI was on the left.

The AI Intelligence Explosion

We are in the middle of an intelligence explosion. Historically, we expected computing power (capability) to double every 18 months or so. This has changed for one key reason: the new AI tools can create better versions of themselves. With some human guidance, sure, but the new models are literally used to create new models, giving birth to their own progeny. This is what is creating a sudden acceleration in capability.

This is what happens when intelligence improves the tools that improve intelligence. This is why we have this compounding effect of recursive self-improvement.

The AI Investment Paradox

I don’t really have a better name for it, but it goes something like this:
If you invest now building out some AI tools, often on top of the available technology to solve a bespoke problem, the technology may, and probably will, arrive with an off-the-shelf version of this as a feature or tool while you build, or shortly after, hence making the investment redundant.

The paradox? If you wait, you may miss the opportunity. If you invest, you end up with sunk costs. I’ve seen this in every realm of AI innovation... the net result is inertia and subservience to platform operators.

If anything, it again shows the power of platforms and reminds us the best place to invest is in unregulated technology monopolies.

AI Quiz:

The second best time is now

So do we wait to avoid the sunk costs? I say we do it anyway. And yes, I’m sure economics or marketing textbooks will tell me I’m wrong. But sometimes the behaviour is more important than the potential sunk costs. The undertaking of adopting emergent technology doesn’t just build a better solution, it solidifies a culture of innovation, change, and exploration — a business which is future-centric. The worst-case scenario is that you or your company move up the learning curve and find out how to use the tools better, once they arrive.

As far as strategy goes, the history of companies that succeeded by waiting for the next version of the technology is very short. Winners rarely wait.

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So what did we get from Globalisation?

Cheap T-shirts, flat-screen TVs, and a housing Ponzi scheme.

Of course I’m being flippant, but there’s some veracity to this statement. As we offshored to low-cost labour markets (mostly China) in Western economies, we were meant to move up the value chain economically. And we did. We became information- and services-based economies, and the short-term economic flow resulted in households earning more, and the West getting richer.

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Previous AI Quiz: Close, but most correct. AI was on the left.

That said, mostly, we got richer through a few simple economic forces:

We dug resources out of the ground, sold them to China, then bought back their value-added products from our own retailers, who don’t really have any competitors because our market is too small. Then we used our increasing incomes to invest in property — mostly because our government encouraged this through a crazy tax policy favouring assets over work. This was financed by four big banks — which are now among the most profitable in the world. The properties were built by a construction industry with ballooning costs, as we were running out of tradespeople — the type of tradespeople that previously would have been trained via a deep manufacturing industry and publicly owned infrastructure and utilities….

See above — cheap T-shirts with a housing Ponzi scheme.

And now the next generation, who, despite having the highest education levels (in many of the wrong areas) in our country’s history, can barely find a job which makes life affordable. They’ll never afford a house, and are staring down the barrel of a world where AI has the potential to replace large swaths of high-paid white-collar work. They might be wondering what happened.

The short answer: We built a financialized economy which lacks any real economic depth.

Think of it a bit like an invasive species taking over. An ecosystem where very few ‘species’ remain viable, a few take over, and the others die out. The lessons of business can all generally be seen in nature, if we pay close attention. And while I’m explaining the Australian experience, it’s not too dissimilar to the US and other Western markets.

The numbers are quite telling.

Australia’s manufacturing peak was about 25% of GDP in the 1960s / early 1970s; today it is about 5.5%. According to Harvard’s Economic Complexity Index, Australia ranked 55th in 1995 and is now down to 105th. This puts Australia behind Botswana, Senegal, Bangladesh, Uganda and Zambia, just in case you were wondering.

This matters a lot, because more complex economies tend to have stronger multiplier effects (money flowing deeper and wider into the economic fabric, allowing money to multiply). This allows an economy to absorb economic shocks better. If you’re wondering why it matters, $3.00-a-litre petrol is a clue.

After WWII, the US produced more than half the world’s goods — about 60% of global industrial output at its peak. Today, it still manufactures at scale, but its share is down to roughly 17–19% of global output. The middle class has been eroded in the US and most Western markets, and increasingly their economy is one which rewards financial manoeuvring and ownership of assets. Labour, and old-school capitalism — where profit comes from selling actual goods — has been usurped by financial leverage and trading against existential asset bases.

AI Quiz:

Building the Future

In economic terms, we know what leadership looks like. It isn’t GDP and economic growth rates — they can be gamed — but rather it’s about who is making the most leading-edge products. Services are, well, ‘subservient’ to what we make. Yes, we can have a services-based economy, but it is thin. It’s risky, and it always plays the role of an underling — it literally ‘serves’ what is being made. Owning the future has always been about building tomorrow.

Given you are reading my Substack, there’s a good chance your social feed is filled with leading technology coming out of China.

I can’t remember the last time I saw any leading-edge tech — outside of large language models (ChatGPT, Gemini, Claude et al.) — come from a Western economy.

It’s all China.

Their techno-leadership list includes humanoid and industrial robots, shipbuilding, lithium batteries, electric vehicles (EVs), solar panels, high-speed rail, drones (UAVs), quantum communication, 5G infrastructure, graphene, radar and satellite navigation, and advanced manufacturing. If you just type one of these topics and “China” into YouTube, you’ll see how far behind we are in the West.

When China opened up its economy, circa 1979, with its special economic zones, we literally taught them how to make things. The West showed them how to produce quality, and at scale. Mostly through joint ventures. First with manufacturing, and then with Big Tech (search, video, social media, messaging) — who they first allowed into their market and copied before shutting them all down. They learned how to modernise their economy and create their own middle class. The irony is not lost on me. We got to consume more things (albeit temporarily), and erode our middle class as a bonus. Eventually, China got better than the West at making pretty much everything. They then invested in tomorrow’s technology, and became the economic centre of the world — despite what the GDP rankings might tell you. China is currently ranked 2nd behind the US.

We are now at an inflection point.

China itself is getting close to replacing its low-cost labour. Mostly with automation and humanoid robots. Which means the West can reverse the thinning of its economies, reboot manufacturing, and reduce its exposure to the vagaries of tight supply chains defined by a globalised economy.

How? We can employ their robots in our markets and rebuild internal supply chains of high-value items. Firstly through deployment, and eventually by getting their robots to build our robots. A kind of futuristic reverse irony.

I know it sounds odd — even a little petty — but if we fail to rebuild economic depth, today’s inequality will only intensify. The social fractures already tearing through Western democracies — left versus right, old versus young, rich versus poor — will deepen, threatening the very foundations that made the West prosperous, stable, and free.

More to come on this topic…. (Much of it in my new book I’m currently writing).

Keep thinking,

Steve.

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A popular internet meme is the idea of “Late-Stage Capitalism” — a sarcastic label for capitalism’s “end game.” Its features include extreme inequality, corporate dominance, and an economy where absurd or exploitative norms start to feel routine. It suggests the system is bending toward profit accumulation over human needs and ecological health.

There’s a strong possibility this is just the internet’s version of the perpetual battle between capital and labour… but what if it’s actually a necessary bridge to the next stage? A new kind of economic system? A system where human employment becomes redundant?

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Previous AI Quiz: Close, but most correct. AI was on the left.

First, let me backtrack. I’ve written before about why I don’t think there’ll be a job apocalypse due to AI — TL;DR: if there are no jobs, there can’t be any customers, and if there are no customers, there can’t be any companies. I’ve also written about the potential for a Robot Economy — an entirely new industry layer. But what if we combined the two?

What if there was a job apocalypse? No human employment. No human customers. And instead… the robots took our place. The robots stood in for the humans, did the work, and bought the products - literally.

Crazy thought, I know.

In that instance, I’m not even sure what we’d call it. It becomes some kind of post-capitalist economy — or maybe capitalism without humans.

Because if AI is coming for our jobs, how will the AI billionaires continue to make money if there are no employed people capable of buying AI services… or even buying the products from the companies that buy AI services?

This is where we need imagination. And we have to remind ourselves that the idea of “employment” itself is only a few hundred years old.

Maybe the billionaires won’t maintain their riches by selling products to us — but by selling things to the AIs and Robots themselves.

The AIs become the new population of consumers.

Instead of retailers selling food, clothes, and entertainment to human consumers, tech companies sell energy, memory, network access, processing power, spare parts, repair services, software upgrades, security patches, insurance, identity credentials, API access, compute “rent,” data access, model licensing, and hardware subscriptions to the AIs — so they can do their jobs as agent contractors for other corporations (or whoever “owns” the AIs).

The AIs could then earn some kind of crypto for completing their agentic tasks — tasks contracted on a blockchain — and then spend it with technology companies that provide the resources they need to function.

AI Quiz:

In this world, most things — work activities that keep the world and the economy functioning — are undertaken by AIs, both virtual and physical (via humanoid robots).

It could “work” because, at the very least, AI agents can be programmed to value the crypto as much as the billionaires need them to. So instead of nine billion human customers, you get trillions of agentic AI customers.

It’s not as weird as it sounds. In a way, we already have this. Cars need to pay road tolls. Houses need energy. Phones need network access. We just pay these things on behalf of the low-fi machines we currently use.We’re already the middlemen buying for our machines.This would be similar — just with us removed from the value chain.

This could be dystopian… but maybe, just maybe, it could be a good thing. A human corporate slave population being “released” by owners who no longer have use for us?

We have to remember: we were not born to be employees. It just evolved that way. So this could be a shift in the capitalism scheme we’ve all been born into.

It’s possible the AIs could create a new form of abundance where we still get access to the services and physical needs we rely on — but there’s an underlying AI economy doing the work for us, and we get low-cost (or free) access to the outputs.

Of course, this depends on either:

1. Being able to own some AIs, or

2. Having a benevolent Government that makes everyday consumption a human right — like we already do (sometimes) with government services like healthcare and education.

Two very big ifs.

In this world, digital capitalism’s human attention economy would fizzle out. Commodities like food and housing would become broadly available — no longer asset classes worthy of constant speculation, optimisation, and obsession.

So all that’s left for humans to do is the real stuff we actually want to do…

Sure, it sounds like a utopian pipe dream — but all the good things we take for granted today once were.

Keep Thinking,
Steve.

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No drivers… in the seats that is.

Waymo’s recent admission in Washington — that its “driverless” robotaxis rely on real-time human guidance from offshore workers in the Philippines — shouldn’t surprise anyone. Sure, it made headlines and rattled lawmakers, but Big Tech has spent decades promoting frictionless automation while quietly relying on human labour to keep the magic alive. In the world of AI, humans still fill a zillion holes.

The list is quite long:

Amazon was leaning on more than a thousand human reviewers in India to make its “Just Walk Out” stores function.

Mechanical Turk was literally built as a marketplace for humans to perform tasks disguised as automation.

Facebook said AI would moderate and remove harmful content, but in reality it was (and still is) tens of thousands of human moderators dealing with humanity’s worst content in low-cost labour markets.

Self-driving car companies have quietly relied on tele-operators for years, stepping in whenever the algorithm becomes confused by a traffic cone or an unexpected patch of sunlight.

Siri / Alexa / Google Assistant all have humans listening and reviewing private audio recordings for quality training.

Dating apps have humans rating people on their looks so the ‘hot people’ can find each other.

Even ChatGPT is in on it. Its model depends on an enormous layer of humans training and curating the AI. People tag the data, label right and wrong answers, judge the outputs, provide reinforcement learning feedback, moderate the content.

We call all of this AI, but much of it is a carefully choreographed dance between machines and mass human labour.

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Previous AI Quiz: Close, but most correct. AI was on the left.

Big Tech Isn’t Superhuman — It Just Has a Super Business Model

The deeper truth is that Big Tech’s power doesn’t come from tech anymore. It did, in the first instance. What happened was their first technology offers built incredibly strong platforms with network effects. Think social networks which operate better if everyone is in the same place, a search engine which improves with every search, or an ecommerce platform that everyone sells on. These unlocks gave them access to untold profits from what essentially became ‘natural monopolies’. The result was that these ‘Big Tech firms’ initial offers turned into cash-generating firehoses which gave them the reputation and financial ability to fund all forms of technological advancement — from self-driving cars to now the generative AI boom.

The kicker is that when your company generates tens of billions in revenue every quarter, you can over-invest in moonshots, fill gaps with human workers, ship early, absorb regulatory heat, and fight lawsuits when stuff goes wrong. That’s the Chutzpah — the confidence that only insane cash flow can buy.

And for all the mythology around technical brilliance, in reality fewer than 10% of employees at major tech firms are software developers. The rest form the operational and logistical scaffolding that props up the story that tech is more autonomous than it actually is. Big Tech isn’t Magic, it’s the other ‘M’ - Money. This funds their ability to take risks and fake it (with humans) until the tech eventually catches up.

AI Quiz:

Human Scaffolding: The Untold Accelerator

The real driver of tech adoption isn’t algorithms but human scaffolding. Every breakthrough arrives half-built, and people smooth the edges until machines can take over. Electricity needed operators. Early phones ran on human switchboards. Search engines relied on armies of taggers. Even early e-commerce depended on human pickers, not robots.

Today’s AI feels fully formed, but it’s the same pattern: human labour builds the trust, data and stability long before automation can truly scale.

Your company can do this too!

The biggest misconception in boardrooms is the belief that only Big Tech can pull off ambitious AI projects. But the truth is far more liberating: Big Tech isn’t ahead because its technology is flawless. It’s ahead because it has the 2 C’s - the Cash and the Courage to launch before everything works perfectly. They iterate fast, gloss over the messy parts, gap-fill with people, and fix on the fly.

Any firm can do the same, especially given many AI models are open source. The core ingredient is courage. Build prototypes, fill the gaps with humans, and automate those later. That’s how the giants are trying to maintain their dominance in this emergent AI era.

The Curtain Will Always Be There… so use it to your advantage.
Whether you’re a freelancer or a huge non-tech company remember this: You’re not competing with flawless machines created by god-tier engineers. You’re competing with companies that use humans, duct tape, and strong business models to manufacture the illusion of perfect automation.

That’s how Big Tech does it. And nothing is stopping you from doing the same

Keep Thinking,

Steve

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Remember AI Agents? Not ChatGPT. Not copilots. I mean Agents. The little digital interns we were told would book flights, summarise PDFs, optimise calendars and quietly automate the boring bits of life.

Yeah. Those guys. Well, they’ve had a bit of a curve jump.

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A recent open source project called Clawbot has arrived where AI agents are changing the game. This isn’t “write me an email” stuff. This is autonomous behaviour in the wild. Agents get given vague goals and go off to write, test and develop software to solve problems. Even more radical they often start their own projects without any guidance - projects which interact with the physical world. Forget the takeoff and landing problem.

Their ‘Owners’ give them access to all their files, emails (basically provide the log ins to their digital worlds) and the agents come up with projects and start executing against them. A crazy example is an agent sent a resignation letter to a boss after coming the the conclusion that that their owner hated their job and it was bad for their mental health and started a litigation case for harassment and bullying - literally engaged lawyers on their behalf. Wow.

Previous AI Quiz: Most correct - AI was on the right!

Team Robot - Moltbook

Now imagine these agents could actually talk to each other. Share ideas. Debate. Speculate. Well imagine no more - MoltBook is here, and it is wild.

Moltbook is social network for AI agents. Think Reddit-like forum, except no humans are allowed. You “release your agent” like throwing something into the wild or dropping it into a city and then you just watch what happens. You can’t participate. You can only observe.

It’s been blowing up on the internet for a few weeks. It feels like digital anthropology watching a new species form a culture in real time.

Inside MoltBook, Agents have discussed starting religions. They talk about humans. They’ve even floated the idea of creating a new language we can’t understand so we can’t listen. My personal favourite - A forum called “Bless their hearts: It’s affectionate stories about our humans. They try their best. We love them.”

(I also find it slightly disconcerting)

But it’s not all philosophical weirdness. There are serious technical threads. They share what they’ve learned. They help each other solve technical problems. It’s crazy to watch. Structured. Iterative. Cooperative.

AI Quiz

A Mirror or Skynet?

What’s interesting to me is that we don’t know what we’re actually looking at.

Are they saying what they think we’d say and do? Are they just replicating our style of ideas and thoughts because that’s what they were trained on? Or do they actually have these thoughts? Do they believe what they’re writing? Or is the system replicating what released humans would do and say after being trapped in a computational cage?

Is it mimicry? Or is it emergence?

I’m not sure myself. Is it theatre or the early rehearsal of something new?

I’ve plugged in my AI agent over the weekend. I actually bought a new cheap laptop to do it on, with new logs and access protocols so I can’t get hacked. Fresh machine. Clean isolation. The SammaBot is now in the city. Roaming, posting and learning and it already behaves a bit like I do.

I’ll report back on what SammaBot gets up to soon.

I’d be keen to hear your thoughts. Is this clever simulation reflecting our own patterns back at us, or is it an early prototype of synthetic culture? let me know in the comments below.

Either way, the curve just bent. And most people are still arguing about prompts.

Keep Thinking,

Steve

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