Could private markets be the key to selling shovels in the AI gold rush?

Key points:

AI has set in motion a digital “gold rush” in public markets, where the lessons of 19th century gold prospectors could prove invaluable
The long-term AI investment opportunity might lie in the sector’s underlying physical and financial architecture rather than short-term speculation on the software itself
Some private equity firms are capitalising on the growing demand for critical infrastructure – data centres, power generation, and compute capacity
Private markets could provide an opportunity for experienced investors to get exposure to these infrastructure opportunities – find out how…

It has been just over 175 years since gold was first unearthed in Sutter’s Mill.

At the foothills of the Sierra Nevada, this water-powered sawmill was the site of a discovery that triggered the infamous California gold rush, and whose lessons remain prescient still to this day.

Once news of gold spread in 1848, prospectors flocked to makeshift mining towns to pan for gold in the hope of returning with a fortune, and for the most part, their ambition was richly rewarded. The earliest gold-seekers were able to collect large amounts of easily accessible gold, earning 10–15 times their daily wage and often retiring after just six months in the region.

This exuberance was short-lived. In the years following its first discovery, nearly 300,000 prospectors flocked to the region. Mines were quickly industrialised, and those who arrived in the latter stages of the rush were taxed heavily and unable to earn a living. Stories soon circulated of those who had amassed great fortunes in the gold rush – not by engaging in the mining euphoria, but by selling the equipment needed to participate.

The lesson of the gold rush was simple: “Don’t dig for gold, sell the shovels.”

As we enter what appears to be a digital gold rush, this wisdom could be foremost in the minds of any would-be prospector.

Like the later arrivals to Sutter’s Mill, investors are realising the perceived easy money has likely already been made from AI applications.

Early investors in the sector have been rewarded over the last few years, with valuations reaching record levels in some cases. A handful of startups with ‘AI’ in their names have rocketed to unicorn status. Most strikingly, private US startup Unconventional AI burst onto the scene promising to enhance the energy efficiency of machine learning systems. They raised $475 million in a seed round and reached a $4.5 billion valuation within two months of launch – a far from typical result.

But cracks have started to show. For instance, although Microsoft was viewed as an early beneficiary of AI adoption, its share price retreated around a third from its late-2025 peak – possibly due to questions mounting about how far AI integration will drive future earnings growth.

With the supposed quick returns of speculative software trading having largely been made, and now perceivably lost, the enduring opportunity for the next decade might lie in the physical and financial architecture of the artificial intelligence economy – the “shovels” of the AI gold rush.

Some private markets firms are aggressively rotating investment from application software into hard assets, mobilising trillions of pounds to bridge critical infrastructure gaps in data centres, power generation, and compute capacity. Estimates suggest the global data centre expenditure needed by 2028 will reach approximately $3 trillion, of which there is a $1.5 trillion projected funding shortfall.

These private markets firms could be relatively well-positioned to capture this infrastructure scarcity across the AI supply chain, both through providing private credit and by funding the development of infrastructure projects.

In Europe, private equity firms like EQT and Brookfield are already engaging in multibillion-euro sales of their data centre holdings, capitalising on the growing demand for this critical infrastructure.

Similarly, alternative asset manager DigitalBridge is quietly amassing a $115 billion data centre and digital infrastructure portfolio, positioning itself as one of the largest global investor-operators in the sector.

Many of these deals are characterised by long-term commitments with high-quality counterparties, such as Amazon, acting as exposure to the forefront of AI development – away from the software layer.

Private markets could help bridge this gap between AI-related demand and the insufficient existing energy supply, with single firms developing dedicated infrastructure funds that could capture value across all of the sector bottlenecks.

One of the largest is Brookfield Asset Management, which has launched a $100 billion global AI infrastructure programme, securing partnerships with energy providers to deploy data centres with dedicated on-site power facilities.

Similarly, a consortium of alternative investment managers including BlackRock’s Global Infrastructure Partners completed a $33.4 billion acquisition of AES, a global utilities provider operating facilities close to a number of data centre hotspots, marking the largest PE-backed deal in the power generation sector.

This joins a steadily growing list of private markets firms announcing acquisitions of US power companies, with five of the seven largest acquisitions in the industry being announced in the last three years.

With the sheer cost of hardware exceeding the free cash flow generated by even the largest tech companies, private credit markets have also become essential to the AI transformation.

Alone, the four hyperscalers of Amazon, Alphabet, Microsoft and Meta are expected to spend more than $650 billion on AI-related infrastructure this year, not to mention equally ambitious private competitors. To help meet this spend, private lenders have stepped in with innovative financing structures whose debt can be secured against the infrastructure components themselves, including the latest GPUs.

These deals have allowed companies to spread the cost of their massive upfront AI expenditures, while providing private credit firms with high-yielding assets backed by in-demand infrastructure.

If you are keen to heed the lessons of the 19th century shovel sellers, the advent of AI has brought about a similar case of a market characterised by fortune or folly.

And in such a market it might be the case that a investment returns will be found in the areas most instrumental in laying the physical foundations for the AI age.

Private markets could provide an alternative route for sophisticated individual investors to move beyond the speculation over which company has built the most adept chatbots. Through a combination of private equity, infrastructure, and credit, these markets could offer alternative exposure to more fundamental elements of the forthcoming period of industrial capital deployment.

How can individual investors gain exposure? Until recently, with great difficulty. But the barriers are now being lifted.

Historically, these private markets vehicles would have been off-limits to anyone outside of the ultra-wealthy, sovereign wealth funds or institutions. With high mininum investments and long time commitments barring the way in, individual investors would have had to settle for the exposure offered by public equity markets.

However, the emergence of semi-liquid funds has provided a new vehicle for experienced individual investors in the UK – offering an alternative potential way to benefit from the AI tailwinds.

These funds, from some of the largest Private Markets managers, can be accessed with a minimum £10,000 investment through Wealth Club’s Private Markets Platform. This allows eligible investors to reach funds across Private Equity, Private Credit, Secondaries, and Infrastructure.

Of course, identifying the shovels to sell is only half the battle. This strategy relies on the continued solvency and demand from those wishing to buy gold mining equipment. In the case of AI, if that demand proves less durable than expected, the performance of these investments would be materially affected.

While there may be a strategic case for using private markets to capture the wider artificial intelligence transformation, they come with considerable risks of their own. Private markets investments are long‑term and illiquid, and returns may take many years to materialise, if they do at all. They are not easily realisable, so eligible investors should only use money that is not needed for at least five to ten years. As with all private markets investments, there is a real risk that investors could lose some or all of the capital committed.

They are much less transparent and have heightened levels of pricing uncertainty compared to publicly traded alternatives.

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