For decades, venture capital has had a simple playbook: fund innovation in software, scale it globally, and reap exponential returns. But as the AI boom pushes hyperscalers to build data centers at an unprecedented pace, capital is flowing into a foundational component of the broader infrastructure—the energy systems that keep those data centers running. A new breed of specialists is emerging to help investors better understand and optimize value beyond the traditional buy-and-hold strategy. Among them is commodities quant Abhinava Sikdar, whose work sits at the intersection of artificial intelligence, quantitative modeling, and power market dynamics.
Studied at IIT Delhi and Columbia University, Sikdar has published peer-reviewed research cited by leading academics and applies machine learning, optimization, and statistical techniques to a historically overlooked area of the modern economy: the U.S. power grid. "What I do ultimately helps the grid operate more securely, efficiently, and competitively," he says. "That can translate into lower system costs for consumers—while also supporting stronger performance for investors."
That dual value proposition has become increasingly relevant as institutional investors commit billions to energy assets to meet the demands of a digital, data-intensive economy. According to a BloombergNEF report, global spending on power grid infrastructure is projected to exceed $21 trillion by 2050, with the United States accounting for a significant share. Much of that investment has historically come from private equity and infrastructure funds purchasing power plants and securing long-term revenue through power purchase agreements (PPAs). While these structures provide stability, professionals like Sikdar explore complementary approaches that can uncover additional efficiencies.
Rather than relying solely on long-term contracts or derivatives, Sikdar uses advanced modeling to inform trading strategies tied to power infrastructure—from combined-cycle gas turbines to large-scale battery storage systems. By predicting market dynamics and optimizing how assets bid into deregulated power markets, he helps operators and owners position their assets more competitively.
The complexity of energy markets makes that easier said than done. Unlike equities or commodities like oil and gas, electricity cannot be easily stored or transported, and its price is shaped by local grid constraints, weather patterns, and regulatory structures. For Sikdar, the biggest learning was that traditional quantitative models often fall short in such an environment. "You can't just apply a standard model to these markets," he says. "They don't always behave rationally. You need to understand the fundamentals—how natural gas, oil, macroeconomics, and even geopolitics feed into power pricing—and then design models grounded in that reality."
The blend of quantitative rigor and physical-world grounding is increasingly valued as energy markets evolve. With natural gas and electricity prices becoming more volatile and renewables adding new layers of intermittency, funds are seeking deeper analytical capabilities to help manage risk and enhance returns. "Five years ago, many infrastructure investors were content to sign a 20-year PPA and call it a day," says one energy-focused investor at a New York-based fund. "Today, they want to understand how to manage around those assets and unlock additional value. That's where specialists like Sikdar come in."
Sikdar has already applied his methods across major U.S. power markets.. His work has helped energy companies and funds identify trading opportunities and improve asset competitiveness in increasingly dynamic environments. It's an approach that mirrors a broader trend: the blending of traditional infrastructure investing with more sophisticated, data-driven strategies.
Looking ahead, Sikdar envisions contributing more broadly to the global energy transition, not only through trading and asset optimization, but also by supporting the development of new infrastructure projects and engaging in strategic initiatives. As energy systems evolve to meet long-term climate and reliability goals, there is a growing demand for professionals who can bridge technical, commercial, and financial domains
"Infrastructure used to be seen as steady and predictable," Sikdar says. "Now, with AI, electrification, and decarbonization reshaping demand, it's one of the most dynamic areas of investing."
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