It’s not your imagination – we want our time back.

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!

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.

Keep Thinking,

Steve.

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Why the AI business model doesn’t compute

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.

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

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.

Keep Thinking,

Steve.

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Can be learned at home, and not via formal education.

Listen to Steve read this post (7 min audio)

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.

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…

The Game changed

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 companies 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.

Keep Thinking,

Steve.

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