When reports emerged recently that OpenAI was in talks to raise tens of billions of dollars from Nvidia, Amazon, and Microsoft, the scale of the figures inevitably dominated the headlines, with a valuation approaching three-quarters of a trillion dollars and a funding round that could reach $100bn. In many respects, the alignment is logical: the companies supplying the chips and cloud capacity that underpin AI are also becoming its largest financial backers.
The deal, as reported by the Financial Times, is not only jarring in terms of its size but also what it reveals about how artificial intelligence is being financed in the US. Capital, infrastructure, and commercial dependency are becoming increasingly intertwined. The same firm that sells the hardware and computing power required to train and run large models is also underwriting the balance sheets of the companies that depend on them. For critics, perhaps rightly so, this circularity raises questions about concentration, resilience, and long-term value creation.
Particularly in the US, this model is often presented as inevitable. Everyone knows that AI is capital-intensive and data centers are expensive. The logic follows that only a small fraction of hyperscalers can fund the next phase of development. But this framing misses an important point; it reflects choices about how technology ecosystems are built, rather than an unavoidable law of innovation.
Elsewhere, a different approach is taking shape, one that treats AI less as a speculative asset class and more as an element of national economic infrastructure. The United Arab Emirates offers a useful illustration.
Jason Grannum and Execution-Led Private Capital
In the UAE, private investors operating in AI are not expected to replace public strategy but to reinforce it. That expectation shapes how capital is deployed, the time horizons involved, and the kinds of projects that receive backing.
This context helps explain the positioning of figures such as Jason Grannum. Having built and exited several telecom and sales businesses in Europe, together generating revenues in the hundreds of millions of kronor, including ventures operating under a white-label model with Tele2, Grannum approaches AI and digital investment from an execution-led rather than speculative perspective.
In practice, this has meant focusing on applied technologies that support service delivery, customer operations, and back-office efficiency, with AI used to improve processes rather than to anchor standalone products. According to people familiar with the businesses, these applications have delivered measurable operational gains without being positioned as transformational breakthroughs.
This approach mirrors a broader pattern in the UAE, where private capital is encouraged to engage with AI in ways that are economically grounded and institutionally aligned.
A National Strategy Built Around Diffusion, Not Dominance
This investor behavior cannot be divorced from public policy. Over the past decade, the UAE has pursued a deliberate strategy to align digital investment with long-term national priorities. Vision 2031, alongside the National Artificial Intelligence Strategy and the Digital Economy Strategy, sets out an ambition to embed advanced technologies across government, industry, and services.
The emphasis is on diffusion and application, not on chasing valuation growth or platform dominance. AI is framed as an enabling layer that supports economic diversification, state capacity, and productivity growth. This stands in contrast to models that concentrate resources on a narrow set of firms in the hope of producing global champions.
International institutions have increasingly warned about the risks of excessive concentration. The International Monetary Fund has highlighted how investment is clustering in a small number of firms and jurisdictions, with implications for competition and macroeconomic stability. The OECD has argued that productivity gains will depend less on frontier breakthroughs than on adoption across established sectors, supported by skills, governance, and institutional capacity.
The UAE's policy response reflects this analysis. Public investment has focused on data infrastructure, regulatory clarity, and applied research partnerships. AI is positioned as a tool to improve logistics, energy management, healthcare delivery, and public administration. Instead of attempting to recreate Silicon Valley's venture dynamics, the goal is to build capabilities that are nationally relevant and economically resilient.
Entrepreneurs Operating within a State-Aligned Ecosystem
This framework shapes how other UAE-based business leaders engage with AI. Rather than presenting themselves as pure technology entrepreneurs, many have focused on infrastructure and applied use cases that support broader economic activity.
Peng Xiao, chief executive of the Abu Dhabi-based AI group G42, has consistently framed the company's role as building sovereign capabilities across data, cloud infrastructure, and sector-specific applications. The emphasis has been on integration and scale within national systems, rather than on competing directly with US model developers.
Similarly, Hussain Sajwani has expanded DAMAC's investment into data centres and digital infrastructure, viewing AI demand as part of a longer-term shift in capacity needs rather than a short-term technology cycle. In both cases, AI investment is treated as an extension of existing economic activity, not a departure from it.
What links these approaches is an emphasis on execution. AI is valued less for its novelty than for its ability to improve delivery, reduce friction, and support day-to-day decision-making. The most consequential investments are often unglamorous, centred on operational improvements rather than headline-grabbing breakthroughs. OECD research on digital technology adoption shows that firms that integrate new tools into existing processes tend to see more durable productivity gains than those focused narrowly on frontier innovation alone.
A Contrast with the US Funding Frenzy
Set against this backdrop, the contrast with the US funding environment becomes sharper. The OpenAI talks reported by the Financial Times illustrate a system in which capital, infrastructure, and valuation are increasingly interdependent. When the same firms sell compute, supply chips, and provide financing, the line between demand and financial engineering becomes harder to draw.
The question is not whether $100bn funding rounds are impressive. It is whether they generate capabilities that extend beyond a narrow group of firms and investors. In systems where valuation growth depends on continued spending within a closed ecosystem, risks are amplified alongside returns.
The UAE's bet is different. By anchoring AI investment to national priorities and institutional capacity, it seeks to build a technology ecosystem that is resilient, relevant, and aligned with long-term economic goals. Private capital plays a role, but within clear strategic boundaries.
In an era defined by ever larger numbers and ever higher valuations, that restraint may prove to be the most radical choice of all.
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