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Bloom Energy ((BE - Free Report) ) is a clean-energy/fuel-cell provider that designs, manufactures, and installs solid-oxide fuel cell (SOFC) systems that generate electricity on-site. The Zacks Rank #1 (Strong Buy) company is best known for its energy servers, called “Bloom Boxes.” Unlike traditional power plants that burn fuel to create steam and turn a turbine, Bloom’s technology generates electricity through a clean chemical reaction. Bloom Energy’s technology converts fuels such as natural gas, biogas, and hydrogen into electricity without combustion, delivering high efficiency and lower emissions than traditional generators.
Why Tech Companies Choose Bloom Energy to Power the AI Revolution
The artificial intelligence boom has already become the largest industrial buildout in history, and it is only expected to grow larger.
Image Source: Zacks Investment Research
In fact, AI-related capital expenditure (CAPEX) spending from big tech companies is estimated to grow more than 70% year-over-year, from $390 billion to $674 billion.
Image Source: Carson Investment Research
AMD Analyst Day Suggests AI Computing Demand Remains High
Advanced Micro Devices ((AMD - Free Report) ) is one of the leading GPU providers. In a recent CNBC interview, AMD CEO Lisa Su suggested AI spending is unlikely to slow, saying: “In the past twelve months, all of our customers have said, demand is accelerating because now we are starting to get real productivity out of the AI use cases. We have all the largest hyperscalers in the world saying that they are investing more in CAPEX because they can see the return on the other side of it.” When asked about whether the CAPEX spending boom is a gamble, Su replied, “I don’t think that it’s a big gamble; it’s the right gamble.”
Meanwhile, NVIDIA ((NVDA - Free Report) ) CEO Jensen Huang sees the AI market like his AMD counterpart, growing in a hockey stick-like fashion. Recently, Huang said that he expects NVIDIA to generate a mind-boggling $1 trillion in cumulative data center revenue through 2027.
AI Model Training is Energy Intensive
Training a large language model (LLM) like OpenAI’s “ChatGPT” or Alphabet’s ((GOOGL - Free Report) ) “Gemini” requires immense computational power. AI models are trained when thousands of high-performance graphics processing units (GPUs) from companies like NVIDIA conduct millions of computations that require unprecedented power. Additionally, as consumer and enterprise demand soars, these AI models require consistent training to remain relevant.
As artificial intelligence becomes an integral part of daily life and soars in popularity, more compute (data centers) is needed. According to the Penn State Institute of Energy and the Environment, data centers consumed 4.45% of U.S. electricity, a number that could triple by 2028. Even before the AI boom, which requires enormous computational resources, the U.S. electric grid was antiquated and overwhelmed. However, by 2030-2035, the strain on the power grid will be unsustainable, with data centers expected to account for 20% of global electricity use.
An example of just how overwhelmed the electrical grid is occurred a few years ago when the Electricity Reliability Council of Texas (EROC) was forced to pay Bitcoin miner Riot Platforms ((RIOT - Free Report) ) $30 million to limit its electricity usage during a heat wave.
Image Source: Zacks Investment Research
Why On-Site Power is Necessary for Data Center Owners
AI training workloads are massive, non-stop, and require extreme power density that the aging U.S. electrical grid was never designed to handle. Below are four reasons AI hyperscalers will be forced to adopt on-site power for their data centers, including:
1. Delayed Time-to-Power: As more competition enters the market and tries to get its piece of the AI revolution, America’s big tech companies are in a rush to get their data centers up and running. However, in order to secure a high-capacity grid connection, it can take between 5 and 10 years.
2. Trump Administration Influence & Incentive: Early last month, the Trump Administration announced a voluntary “Ratepayer Protection Pledge” with major technology companies to ensure that expanding AI data centers do not increase electricity prices for ordinary households and businesses. America’s prominent AI companies, including Amazon, Google, Meta, Microsoft, Oracle, OpenAI, and xAI, have all signed the pledge, promising to build their own power sources for data centers or fund necessary grid infrastructure upgrades. In exchange for the commitments, the administration has worked to cut regulatory red tape and expedite the approval process for power plants to just 2-4 weeks.
3. Power Density and Thermal Requirements: AI chips, such as the latest GPUs and specialized accelerators, consume significantly more power per rack than standard servers. Traditional data centers might run at 10–15 kW per rack, whereas AI-optimized racks are pushing 50–100 kW+. On-site generation allows owners to build high-voltage infrastructure directly adjacent to the server halls, reducing the transmission losses and complexity of stepping down power from a distant utility substation.
4. Reliability: AI training runs can last for weeks or months. A momentary power flicker can crash a training checkpoint, potentially wasting millions of dollars in compute time and electricity. Bloom Energy’s technology can provide a constant “baseload” of clean energy that intermittent renewables (solar/wind) cannot match without massive overbuilding.
5. Cost Hedging & Energy Arbitrage: Electricity is the single largest OpEx for an AI data center. Relying solely on the grid exposes owners to volatile spot prices and "demand charges" during peak hours.
· Price Certainty:Owning the generation source—whether through on-site natural gas or long-term nuclear PPA (Power Purchase Agreements)—locks in energy costs.
· Grid Services:During periods of low compute demand, data centers can actually sell excess power back to the grid, turning a utility cost into a potential revenue stream.
Currently, the lack of consistent, clean, and sufficient energy is the biggest roadblock for the AI revolution. AI data centers require immense amounts of energy to run the high-performance computers needed to train AI models.
Oracle Deal is a Major New Bullish Catalyst
On Monday, April 14th, Bloom Energy announced an expanded partnership with Oracle to support up to 2.8GW of fuel cell deployments for AI cloud and infrastructure. This deal, building on a July 2025 agreement, sees Bloom providing fast, reliable, on-site, behind-the-meter power, significantly reducing Oracle’s reliance on traditional grid infrastructure and d represents the largest direct hyperscaler fuel cell commitment in Bloom’s history. Zacks Consensus Estimates already suggest Bloom will deliver triple-digit EPS growth for 2027, but these numbers will likely need to be revised higher in the coming weeks and months.
Image Source: Zacks Investment Research
BE: A Powerful Breakaway Gap Setup
Tuesday night, BE crushed Wall Street expectations, recording revenue of $751M vs Est. $540M and EPS of $0.44 vs Est. $0.13. Wednesday, shares jumped more than 20% as volume swelled to more than double the 50-day norm. This type of powerful price and volume action signals a breakaway gap pattern, suggesting a new, more powerful trend is just beginning. As I mentioned in a recent commentary, the breakaway gap has been the top setup of 2026.
Image Source: TradingView
Bloom Energy: A Wall Street Expectation Breaker
Wall Street is a game of expectations. A company that consistently beats Wall Street expectations typically moves higher. From this perspective, Bloom Energy is an extremely bullish example. The company has beaten Zacks Consensus Earnings expectations in three of the past four quarters with an average positive surprise of 111.62%!
Image Source: Zacks Investment Research
Bottom Line
As the artificial intelligence revolution shifts from speculative growth to industrial reality, the primary constraint is no longer just silicon—it is power. With the traditional electrical grid unable to keep pace with the exponential energy demands of hyperscale data centers, Bloom Energy has positioned itself as an essential infrastructure partner. By providing high-density, reliable, and combustion-free on-site power, Bloom bypasses years of grid-related delays and regulatory hurdles. The massive 2.8GW partnership with Oracle, combined with a technical "breakaway gap" on the charts and a consistent history of crushing earnings estimates, underscores a clear narrative: Bloom Energy is no longer just a clean-energy alternative; it is the mission-critical engine powering the future of computing. For investors, the convergence of favorable federal policy, triple-digit growth projections, and a massive supply-demand imbalance in the energy sector makes BE a compelling play at the heart of the AI buildout.
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Bull of the Day: Bloom Energy (BE)
Bloom Energy: An Innovative Clean Energy Provider
Bloom Energy ((BE - Free Report) ) is a clean-energy/fuel-cell provider that designs, manufactures, and installs solid-oxide fuel cell (SOFC) systems that generate electricity on-site. The Zacks Rank #1 (Strong Buy) company is best known for its energy servers, called “Bloom Boxes.” Unlike traditional power plants that burn fuel to create steam and turn a turbine, Bloom’s technology generates electricity through a clean chemical reaction. Bloom Energy’s technology converts fuels such as natural gas, biogas, and hydrogen into electricity without combustion, delivering high efficiency and lower emissions than traditional generators.
Why Tech Companies Choose Bloom Energy to Power the AI Revolution
The artificial intelligence boom has already become the largest industrial buildout in history, and it is only expected to grow larger.
Image Source: Zacks Investment Research
In fact, AI-related capital expenditure (CAPEX) spending from big tech companies is estimated to grow more than 70% year-over-year, from $390 billion to $674 billion.
Image Source: Carson Investment Research
AMD Analyst Day Suggests AI Computing Demand Remains High
Advanced Micro Devices ((AMD - Free Report) ) is one of the leading GPU providers. In a recent CNBC interview, AMD CEO Lisa Su suggested AI spending is unlikely to slow, saying: “In the past twelve months, all of our customers have said, demand is accelerating because now we are starting to get real productivity out of the AI use cases. We have all the largest hyperscalers in the world saying that they are investing more in CAPEX because they can see the return on the other side of it.” When asked about whether the CAPEX spending boom is a gamble, Su replied, “I don’t think that it’s a big gamble; it’s the right gamble.”
Meanwhile, NVIDIA ((NVDA - Free Report) ) CEO Jensen Huang sees the AI market like his AMD counterpart, growing in a hockey stick-like fashion. Recently, Huang said that he expects NVIDIA to generate a mind-boggling $1 trillion in cumulative data center revenue through 2027.
AI Model Training is Energy Intensive
Training a large language model (LLM) like OpenAI’s “ChatGPT” or Alphabet’s ((GOOGL - Free Report) ) “Gemini” requires immense computational power. AI models are trained when thousands of high-performance graphics processing units (GPUs) from companies like NVIDIA conduct millions of computations that require unprecedented power. Additionally, as consumer and enterprise demand soars, these AI models require consistent training to remain relevant.
As artificial intelligence becomes an integral part of daily life and soars in popularity, more compute (data centers) is needed. According to the Penn State Institute of Energy and the Environment, data centers consumed 4.45% of U.S. electricity, a number that could triple by 2028. Even before the AI boom, which requires enormous computational resources, the U.S. electric grid was antiquated and overwhelmed. However, by 2030-2035, the strain on the power grid will be unsustainable, with data centers expected to account for 20% of global electricity use.
An example of just how overwhelmed the electrical grid is occurred a few years ago when the Electricity Reliability Council of Texas (EROC) was forced to pay Bitcoin miner Riot Platforms ((RIOT - Free Report) ) $30 million to limit its electricity usage during a heat wave.
Image Source: Zacks Investment Research
Why On-Site Power is Necessary for Data Center Owners
AI training workloads are massive, non-stop, and require extreme power density that the aging U.S. electrical grid was never designed to handle. Below are four reasons AI hyperscalers will be forced to adopt on-site power for their data centers, including:
1. Delayed Time-to-Power: As more competition enters the market and tries to get its piece of the AI revolution, America’s big tech companies are in a rush to get their data centers up and running. However, in order to secure a high-capacity grid connection, it can take between 5 and 10 years.
2. Trump Administration Influence & Incentive: Early last month, the Trump Administration announced a voluntary “Ratepayer Protection Pledge” with major technology companies to ensure that expanding AI data centers do not increase electricity prices for ordinary households and businesses. America’s prominent AI companies, including Amazon, Google, Meta, Microsoft, Oracle, OpenAI, and xAI, have all signed the pledge, promising to build their own power sources for data centers or fund necessary grid infrastructure upgrades. In exchange for the commitments, the administration has worked to cut regulatory red tape and expedite the approval process for power plants to just 2-4 weeks.
3. Power Density and Thermal Requirements: AI chips, such as the latest GPUs and specialized accelerators, consume significantly more power per rack than standard servers. Traditional data centers might run at 10–15 kW per rack, whereas AI-optimized racks are pushing 50–100 kW+. On-site generation allows owners to build high-voltage infrastructure directly adjacent to the server halls, reducing the transmission losses and complexity of stepping down power from a distant utility substation.
4. Reliability: AI training runs can last for weeks or months. A momentary power flicker can crash a training checkpoint, potentially wasting millions of dollars in compute time and electricity. Bloom Energy’s technology can provide a constant “baseload” of clean energy that intermittent renewables (solar/wind) cannot match without massive overbuilding.
5. Cost Hedging & Energy Arbitrage: Electricity is the single largest OpEx for an AI data center. Relying solely on the grid exposes owners to volatile spot prices and "demand charges" during peak hours.
· Price Certainty:Owning the generation source—whether through on-site natural gas or long-term nuclear PPA (Power Purchase Agreements)—locks in energy costs.
· Grid Services:During periods of low compute demand, data centers can actually sell excess power back to the grid, turning a utility cost into a potential revenue stream.
Currently, the lack of consistent, clean, and sufficient energy is the biggest roadblock for the AI revolution. AI data centers require immense amounts of energy to run the high-performance computers needed to train AI models.
Oracle Deal is a Major New Bullish Catalyst
On Monday, April 14th, Bloom Energy announced an expanded partnership with Oracle to support up to 2.8GW of fuel cell deployments for AI cloud and infrastructure. This deal, building on a July 2025 agreement, sees Bloom providing fast, reliable, on-site, behind-the-meter power, significantly reducing Oracle’s reliance on traditional grid infrastructure and d represents the largest direct hyperscaler fuel cell commitment in Bloom’s history. Zacks Consensus Estimates already suggest Bloom will deliver triple-digit EPS growth for 2027, but these numbers will likely need to be revised higher in the coming weeks and months.
Image Source: Zacks Investment Research
BE: A Powerful Breakaway Gap Setup
Tuesday night, BE crushed Wall Street expectations, recording revenue of $751M vs Est. $540M and EPS of $0.44 vs Est. $0.13. Wednesday, shares jumped more than 20% as volume swelled to more than double the 50-day norm. This type of powerful price and volume action signals a breakaway gap pattern, suggesting a new, more powerful trend is just beginning. As I mentioned in a recent commentary, the breakaway gap has been the top setup of 2026.
Image Source: TradingView
Bloom Energy: A Wall Street Expectation Breaker
Wall Street is a game of expectations. A company that consistently beats Wall Street expectations typically moves higher. From this perspective, Bloom Energy is an extremely bullish example. The company has beaten Zacks Consensus Earnings expectations in three of the past four quarters with an average positive surprise of 111.62%!
Image Source: Zacks Investment Research
Bottom Line
As the artificial intelligence revolution shifts from speculative growth to industrial reality, the primary constraint is no longer just silicon—it is power. With the traditional electrical grid unable to keep pace with the exponential energy demands of hyperscale data centers, Bloom Energy has positioned itself as an essential infrastructure partner. By providing high-density, reliable, and combustion-free on-site power, Bloom bypasses years of grid-related delays and regulatory hurdles. The massive 2.8GW partnership with Oracle, combined with a technical "breakaway gap" on the charts and a consistent history of crushing earnings estimates, underscores a clear narrative: Bloom Energy is no longer just a clean-energy alternative; it is the mission-critical engine powering the future of computing. For investors, the convergence of favorable federal policy, triple-digit growth projections, and a massive supply-demand imbalance in the energy sector makes BE a compelling play at the heart of the AI buildout.