Long before artificial intelligence became the centre of a global investment frenzy, a conversation between two influential business leaders offered a glimpse of where the next great economic transformation might emerge. One understood the technology’s potential. The other had the capital to act on what he learned.
In the history of technological revolutions, the most consequential decisions are not always made by the people who invent the technology. Sometimes, they are made by investors who recognise that a new technology will require an entirely different economic infrastructure.
That distinction helps explain the significance of a conversation between Stephen Schwarzman, the co-founder and chief executive of Blackstone, and Jack Ma, the founder of Alibaba.
Schwarzman has recalled that when Ma spoke to him about artificial intelligence in 2015, he had “literally no idea” what Ma was talking about. It was an unusually candid admission from a businessman whose career had been built on identifying opportunities, assessing risks and allocating capital on a global scale.
Yet the conversation became an important reference point in Blackstone’s subsequent engagement with AI and the infrastructure supporting the digital economy.
Today, data centres have become one of the most strategically important assets in Blackstone’s portfolio. The facilities that house servers, networking equipment and computing systems are increasingly essential to the operation of cloud platforms, financial services, digital commerce and advanced AI models.
The connection between that early conversation and Blackstone’s later investments illustrates a broader economic principle: the greatest opportunities created by a technological revolution may lie not only in the technology itself, but also in the physical systems that make its widespread adoption possible.
For investors, this raises a fundamental question. When an unfamiliar technology begins to emerge, should the focus be on predicting which company will dominate it, or on understanding what the entire industry will need in order to grow?
Blackstone’s experience suggests that the second question can be just as important as the first.
It also raises a question for countries such as Nigeria and other African economies. If AI is becoming a foundational technology, will they build and own enough of the infrastructure that powers it, or will they remain primarily consumers of systems developed, financed and controlled elsewhere?
The answer will have implications extending far beyond the technology industry.
1. The 2015 conversation: recognising what you do not understand
In 2015, artificial intelligence was not the cultural and commercial phenomenon it has become today.
Machine learning was already an established area of research, and companies were using AI-related techniques for recommendation engines, image recognition, fraud detection, search, logistics and predictive analytics. Major technology companies were investing heavily in research, while cloud computing was steadily expanding the market for remote computing services.
However, the technology had not yet entered the mainstream business conversation with the intensity that followed the arrival of generative AI.
For someone outside the technology industry, artificial intelligence could still seem abstract. Its capabilities were difficult to evaluate, its commercial applications were uneven, and the path from laboratory research to mass-market adoption was uncertain.
This context makes Schwarzman’s admission important.
He was not a novice in business. Blackstone had grown from a financial advisory partnership established in 1985 into a major alternative investment manager, with operations spanning private equity, real estate, credit and other investment strategies.
His expertise lay in evaluating businesses, managing capital and identifying opportunities that could generate attractive returns. But expertise in finance did not automatically translate into an understanding of the future direction of AI.
Ma, meanwhile, had spent years building Alibaba into a major force in e-commerce and digital services. His experience exposed him to the ways in which data, software, online marketplaces and computing infrastructure could reshape commercial activity.
The two men were looking at the future from different positions.
Ma could see how digital technologies were changing the relationship between businesses and customers. Schwarzman could evaluate what those changes might mean for investment opportunities across industries.
The significance of their conversation lies in that intersection.
A successful investor does not need to become an expert in every emerging technology. But an investor who dismisses developments outside their immediate expertise risks overlooking changes that could fundamentally alter the value of existing assets and create entirely new markets.
Schwarzman’s response demonstrated the value of intellectual humility: recognising a gap in one's knowledge and treating it as a reason to investigate rather than a reason to dismiss an idea.
The lesson is especially relevant in an era when technological change is increasingly crossing traditional industry boundaries. AI is no longer simply a subject for computer scientists. It affects banking, manufacturing, healthcare, transportation, telecommunications, energy and real estate.
Investors who evaluate these industries without understanding their technological dependencies may miss important risks as well as opportunities.
2. Why Jack Ma’s perspective mattered
Jack Ma’s significance in this story extends beyond his reputation as the founder of Alibaba.
Alibaba emerged from China's rapidly expanding internet economy, where digital marketplaces, online payments, logistics and cloud services became increasingly interconnected.
The company’s business model demonstrated that digital commerce was not simply about putting products on a website. It required sophisticated systems for managing transactions, processing information, matching buyers with sellers, coordinating logistics and maintaining trust across enormous networks of users.
As these systems grew, computing became a strategic resource.
The same principle applies to AI. A model may appear to the user as a simple interface, but the service behind that interface depends on vast amounts of computing capacity, software engineering, data management and network infrastructure.
Ma’s experience operating at the intersection of commerce and technology gave him a perspective on how these underlying systems could become economically significant.
Nevertheless, it is important not to exaggerate the historical record. Schwarzman’s recollection establishes that Ma discussed AI with him and that the subject initially seemed unfamiliar. It does not, by itself, establish that Ma predicted every subsequent development in generative AI or that the conversation alone determined Blackstone’s investment strategy.
Major investment decisions emerge from research, internal debate, financing conditions, market opportunities and relationships with customers. A conversation can influence the direction of an investigation without being the sole cause of everything that follows.
The deeper lesson is not that one person possessed a perfect forecast of the future. It is that an exchange between people with different kinds of expertise can prompt a decision-maker to examine an opportunity that might otherwise have remained outside their field of vision.
In the world of long-term investment, that can be a consequential starting point.
3. From AI as an idea to data centres as an asset class
To understand why the story matters, it is necessary to distinguish between artificial intelligence as a technology and data centres as an investment opportunity.
AI is a collection of techniques and systems that enable computers to perform tasks such as recognising patterns, generating content, processing language, making predictions and assisting with decisions.
Data centres are physical facilities designed to house and operate computing equipment. They provide the environment in which servers, storage systems and networking hardware function, supported by electricity, cooling, security and connectivity.
The two are related, but they are not the same business.
A company developing an AI model might generate revenue by selling subscriptions, licensing technology or providing access through an application programming interface. A data-centre operator might generate revenue by leasing space, power and computing infrastructure to customers under commercial agreements.
The economics differ considerably.
AI developers face uncertainty about competition, customer adoption, software costs and the speed at which their technology becomes commoditised. Data-centre operators face risks involving construction costs, electricity, financing, customer concentration, utilisation and the possibility that future demand will not justify the capacity they build.
Yet the two businesses depend on one another.
As AI applications spread, organisations require more computing resources. Some build their own infrastructure, while others rent computing power from cloud providers or specialised operators. Those providers, in turn, need facilities capable of accommodating the equipment.
This relationship creates opportunities for investors who understand that technological expansion requires physical capacity.
Blackstone’s data-centre investments illustrate this approach. Rather than concentrating exclusively on predicting which AI application would win, the firm built exposure to infrastructure that could serve a broad range of digital businesses.
That strategy does not eliminate uncertainty. It changes the nature of the investment question.
Instead of asking only which application will attract the most users, an infrastructure investor asks how much capacity customers will need, where it should be located, how it will be powered, what customers will pay and whether the projected revenue can support the cost of construction.
In some circumstances, the infrastructure can remain valuable even as individual applications rise and fall. In others, a change in computing architecture or a decline in demand can leave expensive facilities underutilised.
The distinction is critical: infrastructure is not automatically a safe investment simply because the technology it supports is important.
4. The rise of Blackstone’s data-centre empire
Blackstone’s subsequent investments demonstrate the scale at which institutional capital can enter the digital infrastructure market.
One of the most significant transactions was its acquisition of QTS Realty Trust in 2021. The transaction, valued at approximately $10 billion including debt, gave Blackstone a major position in the data-centre business.
QTS operates data centres that provide capacity and associated services to enterprise customers, cloud providers and other organisations requiring secure, reliable computing environments.
The acquisition was important for several reasons.
First, it gave Blackstone access to an established operator rather than requiring the firm to build an entire data-centre platform from scratch.
Second, it provided a foundation for expansion in a market where customer relationships, operational expertise, suitable sites and access to power can be difficult to assemble quickly.
Third, it positioned Blackstone to participate in the expansion of cloud computing and, subsequently, the extraordinary increase in interest in AI infrastructure.
Blackstone's reported data-centre portfolio expanded substantially after the acquisition. In its 2025 investment materials, the firm cited a data-centre portfolio valued at approximately $110 billion, including operating and development assets. Such figures should be understood in the context of the firm's own reporting and the valuation basis it uses; they do not represent cash already earned, guaranteed profits or an equivalent amount of unencumbered assets.
The distinction between portfolio value and realised profit is essential.
A large portfolio can reflect valuable land, buildings, contractual leases and development opportunities. But the eventual return depends on acquisition prices, financing costs, operating expenses, capital expenditure, customer demand and the prices at which assets can ultimately be sold.
The scale nevertheless demonstrates how seriously major investors have come to regard data centres.
What once might have been considered a specialised corner of commercial real estate is now connected to the strategic plans of the world's largest technology companies.
The reason is straightforward: digital services cannot expand indefinitely without sufficient computing capacity.
The companies that provide this capacity are increasingly positioned at the intersection of property, energy, telecommunications and technology.
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