Gray: AI infrastructure crucial to India's long-term growth

Prime Minister Narendra Modi's central message to the forum was to "use power to empower," and Gray saw in it a blueprint for how artificial intelligence can address India's structural challenges. Healthcare and education emerged as sectors where AI's potential to lift living standards across the population proved most compelling to the Blackstone executive.

Gray believes the technology can serve as more than a productivity tool for large corporations. The focus on how AI might reach and benefit ordinary Indians—through better medical diagnostics, personalized learning, and expanded access to services—shaped his optimism about the investment landscape ahead.

The central bank mounted what interviewer Yvonne Man described as a "historic defense of the rupee," marking the biggest currency moves since the 2013 taper tantrum.

The divergence reveals a two-speed economy: institutional capital flowing into private deals and infrastructure projects while retail and domestic investors navigate volatility in listed shares. This split has shaped Blackstone's strategy to anchor its India growth in deal-making rather than public market exposure.

Elevated energy prices have directly hurt the Indian economy and weakened the rupee. Alongside this external pressure, Indian IT services companies face uncertainty about how artificial intelligence might reshape their business models and demand for offshore engineering talent.

India's equity valuations, which stood at very high multiples two years ago, have undergone a reset as investors reassess the risk-reward. Gray acknowledged these near-term challenges as material constraints on market sentiment, even as he insisted the broader economic picture remained intact.

Yet the long-term fundamentals persist: annual GDP growth above 7 percent, a young population, a rising middle class, and a government oriented toward growth. For contrarian investors willing to tolerate near-term volatility, current weakness represents an attractive entry point into a structurally sound economy.

Gray explicitly rejected the notion that Blackstone follows crowd sentiment. Instead, the firm adopts a deliberate, slow-and-steady methodology: identify fundamentally strong businesses in attractive sectors, invest capital, and work to build those companies over years.

This philosophy allows Blackstone to remain committed to India even as public market participants flee volatility. The strategy assumes that a country with rising GDP growth, demographic tailwinds, and pro-growth policy will eventually reward patient capital.

The firm's current portfolio in India divides roughly evenly between real estate and private equity, giving it exposure to both physical infrastructure and operating company growth. The push to double AUM signals confidence that both streams remain under-invested relative to opportunity.

A second cluster centers on India's rising middle class: healthcare, education, financial services, and travel.

Gray noted that Blackstone owns stakes in one of India's largest hospital chains and one of its biggest mall companies, allowing it to capture both consumer spending growth and the infrastructure undergirding that consumption. Over time, the firm has taken 14 companies public, creating exit liquidity for investors on the back end of deals.

Gray emphasized that infrastructure and energy investments sit at the core of Blackstone's thesis, given the "enormous needs" around both the AI buildout and the underlying economy's expansion. India's power systems must scale to support data centers and compute while simultaneously fueling broad-based industrial and commercial growth.

Gray observed across Blackstone's portfolio companies worldwide a palpable hunger to deploy artificial intelligence—from cutting lease application processing times at rental housing firms by 90 percent to improving visualization techniques in garage door manufacturing. Personalized AI tools reaching individuals, such as Muse and Instinct, are driving usage upward across consumer and enterprise segments.

Yet the supply side moves far more slowly. The constraints are rooted in the physical world: manufacturing capacity for chips, power generation and distribution, regulatory entitlements, and the capital required to build all three. This structural imbalance differs fundamentally from past technological bubbles.

In the telecom boom, for instance, providers built network supply ahead of actual demand, fueled by excessive leverage. The current AI infrastructure cycle exhibits none of that loose capital discipline. Instead, demand keeps accelerating while supply remains tightly bottlenecked, creating sustained pricing power and deployment needs.

This model generates predictable cash flows while avoiding the capital intensity of owning and operating data centers outright.

Gray acknowledged that excesses inevitably arise during moments of technological breakthrough, yet he argued the current AI cycle differs markedly from previous bubbles. The telecom expansion saw vendors build capacity ahead of demand with heavy leverage; capital discipline was absent. Today's AI infrastructure buildout exhibits structural discipline: supply remains constrained, leverage is measured, and the fundamental imbalance between demand and available capacity remains intact.

Blackstone operates Airtrunk, the region's largest data center player, and holds a stake in NASA, a neo-cloud company in India at an early stage of development. Despite these positions, Gray emphasized how underdeveloped Asia's AI compute footprint remains relative to need.

Gigawatt capacity in Asia outside China still ranks as "very, very small" against the underlying compute demand that exists. This gap suggests years of buildout opportunity ahead, particularly in India where power infrastructure and regulatory frameworks continue to evolve.

A number of AI infrastructure companies have had to delay planned public offerings as investors grown cautious about valuations and growth assumptions. Gray characterized this market discipline as healthy, a necessary brake on euphoric buildout that could lead to oversupply and financial distress.

Gray predicted long-term compute demand will persist as artificial intelligence matures from novelty to infrastructure utility. Meeting that demand, however, requires capital aggregators who can simultaneously secure funding for data center construction, finance chip purchases, and invest in power generation and grid expansion.

Blackstone's scale and diversified capital sources position it to play that integrating role, financing not just compute facilities but the enabling infrastructure—semiconductors and energy—that underpins the entire buildout.