SAN FRANCISCO, CA, February 13, 2026 /24-7PressRelease/ -- For over a decade, artificial intelligence progress has been measured in parameters, compute speed, and model accuracy. But according to Silicon Valley–based technology expert Tamar Toledano, the next chapter of AI will not be written in code. It will be written in kilowatts, transmission lines, and regulatory decisions. "The AI race is quietly shifting," Toledano said. "We are no longer competing on who builds the smartest models. We are competing on who can power them reliably, affordably, and at scale."

That shift is already underway. Global AI data center power demand is projected to reach 68 gigawatts by 2027, roughly equivalent to California's entire 2022 power capacity. By 2030, global demand could surge to 327 gigawatts, up from just 88 gigawatts in 2022. In the United States, AI and cloud computing are expected to drive nearly half of all electricity demand growth this decade.
Yet Tamar Toledano argues the real story is not the size of the demand, but the mismatch between digital ambition and physical reality. "AI moves at software speed," she explained. "Energy infrastructure moves at regulatory speed. That gap is becoming the defining constraint on innovation."

Nowhere is this clearer than in the U.S. power grid. In major data center hubs such as Virginia, interconnection requests can take four to eight years to complete. Transmission projects must navigate complex multistate permitting processes, environmental reviews, and local opposition. Meanwhile, shortages of transformers, gas turbines, and electrical switchgear are delaying construction nationwide.

Compounding the problem is geographic concentration. Nearly half of U.S. data center capacity sits in just five regions, intensifying local grid stress and increasing vulnerability to outages and price volatility. "This is not a capacity problem alone," Toledano said. "It's a planning failure. We optimized for speed of deployment, not long-term resilience."

Energy diversification is emerging as a partial solution, but each option comes with trade-offs. Wind and solar are expected to supply roughly half of new power demand by 2035, yet they require massive investments in transmission, storage, and grid modernization to manage intermittency. Natural gas remains essential for baseload and peak demand, but turbine shortages have reached such extremes that some AI operators are sourcing used engines from airlines and rail systems.

Nuclear energy is also re-entering the conversation. Small modular reactors are increasingly viewed as a viable zero-carbon, dispatchable option capable of powering large-scale computing reliably. At the same time, onsite generation and grid-interactive data center designs are gaining traction as companies seek to reduce dependence on strained public infrastructure.

Still, Tamar Toledano emphasizes that technology choices alone will not resolve the bottleneck. Policy and governance are now central to AI competitiveness. The United States continues to lag in permitting reform and infrastructure investment, leaving states to experiment with their own responses. California and Texas have proposed new tariffs and rate structures for large data center users, reflecting growing tension between AI expansion and grid stability. In the absence of federal coordination, judges and state legislators are increasingly tasked with managing AI-related risks, resulting in fragmented and inconsistent oversight. "Without a national framework, we're outsourcing strategic decisions to courtrooms," Toledano said. "That's not how you win a global technology race."

Meanwhile, global competitors are moving decisively. China has invested heavily in long-term energy infrastructure explicitly designed to support its AI ambitions, reducing uncertainty for developers and accelerating deployment timelines. Toledano warns that infrastructure velocity, not just talent or capital, will determine leadership in the next phase of AI.

Yet there is a twist. AI itself may help close the gap it has created. Advanced AI systems are already optimizing energy networks, improving load forecasting, and strengthening grid resilience. Research suggests AI-driven grid management could reduce outage durations by 30 to 50%. Smart demand management programs could allow flexible AI workloads to pause during peak grid stress, unlocking as much as 126 gigawatts of idle capacity without new generation. "AI doesn't have to be an energy liability," Toledano said. "If designed responsibly, it can become one of the most powerful tools we have for managing complexity at scale."

Toledano's perspective is shaped by years of experience advising high-growth technology companies under real operational pressure. With a background in computer engineering and a career spanning Silicon Valley startups and enterprise transformation, she has seen firsthand how systems fail when physical constraints are ignored. Her work today focuses on helping organizations align AI strategy with infrastructure reality, resilience, and long-term value creation.

Her conclusion is pragmatic and urgent. The AI energy bottleneck is not a future risk. It is a present-day filter determining which ideas reach scale and which never leave the lab. "The next AI superpower won't be the one with the best models," Toledano said. "It will be the one that treats energy as a strategic asset, not an afterthought."

To learn more visit: https://tamartoledanomarketing.com

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