The artificial intelligence boom has shifted from a race for chips to a race for electricity. Every new AI data center requires enormous amounts of reliable power, yet the U.S. electric grid is struggling to keep pace. That mismatch is becoming one of the defining investment themes of the decade.
While semiconductor companies remain at the center of AI spending, the companies capable of supplying power quickly may become just as important. Hewlett Packard Enterprise’s (NYSE:HPE) latest outlook illustrates just how large the challenge has become — and why Bloom Energy (NYSE:BE) appears uniquely positioned to benefit.
A Growing Power Crisis in the Age of AI
Speaking at HPE’s 2026 Discover IR Summit, CEO Antonio Neri cited one statistic that should catch every investor’s attention: the U.S. is on track to face a 19-gigawatt power gap by 2028. He added that this amount of electricity could power roughly 16 million homes, while data centers could account for nearly half of U.S. electricity demand through 2030.
These numbers explain why “time to power” has become almost as valuable as computing power itself. The shortage creates opportunities across several industries:
- Nuclear Power: Offers reliable baseload generation but faces years of permitting and construction delays.
- Gas Turbines: GE Vernova (NYSE:GEV) is a near-monopoly in this space with a growing backlog, though revenue arrives over longer project timelines.
- Utilities: Can expand their rate base in AI regions such as Virginia, Texas, and PJM, but face regulatory pressure and transmission constraints.
- Battery Storage & Transmission: Supports grid stability but depends on broader infrastructure buildout.
- Bloom Energy: Offers behind-the-meter power that can be deployed in months.
Let’s focus on the final category because that’s where today’s opportunity appears strongest.
Fuel Cells Are the Fastest Solution to a Growing Power Gap
Unlike utilities building billion-dollar power plants, Bloom Energy installs modular solid oxide fuel cell (SOFC) systems directly at customer sites. Operating primarily on natural gas today, these systems generate electricity where it’s consumed, reducing dependence on an overloaded grid.
That speed is key. According to Bloom Energy’s 2026 Data Center Power Report, 27% to 38% of data centers are expected to rely on onsite power either fully or partially by 2030 — a sharp increase from current adoption. Bloom says many systems can be deployed in roughly 90 days, compared with years for new grid-scale generation.
Morgan Stanley recently estimated that onsite solutions like Bloom’s could contribute between 5 GW and 8 GW toward closing America’s growing power deficit. Customers are already signing large agreements.
Bloom expanded its master services agreement with Oracle (NYSE:ORCL) to support up to 2.8 GW of capacity, with 1.2 GW already under contract. The company also signed a 20-year, $2.65 billion agreement with American Electric Power (NASDAQ:AEP) for up to 1 GW, alongside a $5 billion strategic partnership with Brookfield Asset Management (NYSE:BAM) targeting AI infrastructure.
Reports earlier this year indicated Bloom secured approximately $7.65 billion in data center-related contracts over a relatively short period.
Bloom’s Financials Reflect the Demand
Bloom’s operating performance suggests those contracts are beginning to translate into revenue.
First-quarter earnings show revenue climbed 130% year over year to $751 million, while product revenue surged 208%. Management also increased full-year 2026 guidance to $3.4 billion to $3.8 billion, implying approximately 80% growth at the midpoint after generating a record $2.02 billion in 2025 revenue.
Granted, risks remain. Bloom must successfully expand manufacturing from roughly 1 GW of annual production capacity to 2 GW by the end of 2026 while managing fuel availability, permitting requirements, and a valuation that assumes continued execution.
That said, those risks appear more manageable than waiting years for traditional power plants to come online.
Why This Matters: The AI Infrastructure Buildout is Here
The shift from computing power to energy infrastructure marks a pivotal moment in the AI industry. As data centers expand to meet the demands of large language models and generative AI systems, their energy consumption has become a critical factor in both operational costs and environmental impact.
HPE’s warning about a 19 GW power gap by 2028 highlights how urgent this challenge is. The traditional grid infrastructure is not designed for the rapid scaling required by AI-driven applications. This creates an opportunity for companies like Bloom Energy, which can provide scalable, modular solutions that align with the pace of AI development.
While long-term projects such as nuclear power plants or new utility infrastructure are essential, they take years to complete. In contrast, Bloom’s fuel cell systems offer a faster deployment timeline and greater flexibility in meeting immediate energy needs. This makes them particularly attractive for companies looking to future-proof their data centers without waiting for traditional grid upgrades.
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The Bottom Line: Bloom Energy is Well-Positioned
In short, AI’s next bottleneck isn’t chips — it’s electricity. HPE’s projection of a 19 GW power shortfall by 2028 shows just how urgent the challenge has become.
GE Vernova should benefit over the long run as utilities build new generating capacity, but those projects require years to complete. Bloom Energy operates on a different timeline. Its modular fuel cells can be deployed in months, its customer list already includes Oracle, American Electric Power, and Brookfield Asset Management, and its revenue is already accelerating.
Ultimately, when the market needs power today instead of five years from now, Bloom Energy looks like one of the clearest investment opportunities in the AI infrastructure buildout. Investors should watch closely for updates on Bloom’s manufacturing expansion, fuel supply chains, and continued customer growth as these factors will determine how well it can capitalize on this growing demand.
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