Last month was the S&P 500’s best month in six years, marking the biggest rally since the Covid lows in April of 2020. The S&P 500 rose 10.43%, while the Nasdaq gained more than 15%. Yet the single best-performing large-cap stock in that historic month was not Nvidia, Microsoft, Meta, or another obvious AI leader. Rather, it was Bloom Energy, which rose roughly 109%.
Bloom was our 2026 Top Stock pick, published on February 27th when shares were at $160.90. However, my firm’s history on the stock began one year ago when we first identified AI energy as the next bottleneck, with initial buys during the April lows at $16.64 and $17.04. For most of the last 12 months, we’ve held Bloom at a high allocation of 10% or higher, with real-time trade alerts sent to our Research Members. Today our returns from those entries are roughly 1300%. Many of Wall Street’s most renowned firms eventually followed the I/O Fund much later and entered at significantly higher prices.
Earlier this year, I designated Bloom as our Top 2026 Stock Pick on February 27 when shares were at $160.90 (about 10X on our cost basis).
The decision to place Bloom as our Top Stock pick required strong conviction — not only in Bloom Energy’s positioning, but in the sheer pressure from AI’s primary bottleneck to believe the stock could see a repeat year of strong performance. Repeat years are especially rare after a big run-up as early investors typically book gains and move on.
Below, I’ll walk you through why Bloom outperformed in the strongest rally tech has seen in six years, why the recent Q1 2026 results confirm the fundamentals beneath the rally, and why I believe the setup still holds – even after the stock rose 109% in April, just two months after we named it our 2026 Top Stock pick.
Why an Energy Stock — Not Software or Semiconductors — Led Tech’s Biggest Rally in 6 Years
Investors should take note that tech’s biggest month in six years was not led by a Mag 7 stock, a semiconductor, or a software platform like it was in 2020. Although many of these sectors were deservedly ranked in the top 10, the month’s biggest outperformer was centered around power availability.
The reason for this is straightforward as companies like Microsoft, Google and Meta are spending hundreds of billions annually on AI, with tens of billions allocated to Nvidia’s GPUs and custom silicon like Google’s TPUs. These systems risk being delayed if Big Tech cannot energize and deploy them quickly. Meanwhile, the market has already penalized these companies for outsized spending on AI infrastructure. The effects of low immediate ROI only compound with a timing risk as GPUs sit idle, while competitors who do have power amplify the consequences of a delay.
Despite power being the primary bottleneck, the market is hyper-focused on whether Big Tech can monetize AI. My contention in my original article on Bloom Energy is that the market is missing the point. the market is missing the point. Monetization has never been Big Tech’s weakness as explosive revenue growth and high margins have defined their businesses for decades. While execution risk always exists, these companies remain the world’s most reliable operators at scale.
Instead, the real risk to the AI economy lies in the physical constraints of scaling these AI ambitions — not in compute availability from companies like Nvidia or Broadcom, and certainly not in Big Tech’s software capabilities, but in power availability, thermal management, and infrastructure that were never designed for this magnitude of demand.
Bloom Energy Solves AI’s Most Critical Bottleneck: Time to Power
Over the next two years, Nvidia’s GPU systems are expected to require a 5x increase in power per rack from what was needed in the first half of 2025 as we move across GPU generations from Blackwell to Rubin Ultra. As stated, if hyperscalers cannot energize these systems quickly, billions of dollars of AI capex can sit idle, especially critical now that the AI market is shifting toward generating inference revenue.
Therefore, due to the rapidly increasing power requirements for AI systems, it is not enough to say the AI economy needs more power, but rather it needs power urgently. These are two entirely different matters; for example, the first could be supported by the expansion of nuclear power and the electrical grid, but the latter cannot. In fact, combining these two is something very few companies can do.
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Behind‑the‑Meter Fuel Cells vs Grid‑Dependent Power
Bloom Energy offers onsite power generation through solid oxide fuel cells that are behind the meter to reduce dependency on the grid. By providing behind-the-meter generation, Bloom reduces reliance on utility infrastructure and accelerates time-to-power for customers. An added benefit is the United States is the largest producer of natural gas, therefore, Bloom does not struggle to secure supply given the United States has large, well-developed gas supplies and pipeline infrastructure.
Our primary message has been “time to power” for Bloom, and the company continues to stand out for this very reason as it is finding strong product market fit in AI data center power needs. This is a key advantage as on-site power is becoming more of a necessity as grid constraints and connection timelines rise.
Regarding grid constraints, PJM has already fallen short of reliability requirements in its last two capacity auctions, including a roughly 6.6 GW shortfall for the 2027/28 planning year, while ERCOT’s interconnection queue has surged to about 226 GW, including roughly 165 GW from data center projects targeting approval by 2030. Against that demand, ERCOT added only 23 GW of new capacity in 2024–25, underscoring why time-to-power is becoming a central bottleneck for AI data centers.
This further validates Bloom’s positioning by enabling new data center projects to come online sooner with on-site, behind the meter power without sitting in interconnection queues for years at a time. Bloom has already proven that it can quickly establish data center power solutions in a rapid manner, completing shipments to Oracle Cloud Infrastructure in just 55 days of its 90-day delivery request.
Its fuel cells are also fuel-flexible and can run on natural gas, biogas, or hydrogen, and provide continuous power with 99.9-99.999% reliability metrics. They are also modular in nature and can scale from 20 MW to 500 MW+, allowing flexibility in deployments and ease of scaling. Bloom is also continuously improving on price-performance, stating that its fuel cells have seen double digit YoY cost reductions each year for the past ten years, and a 10X increase in power production in the same footprint versus ten years ago.
Oracle’s Project Jupiter Sends Bloom’s Stock Soaring in April
We’ve covered previously that Bloom delivered a fuel cell system to Oracle in 55 days, standing out among the longer to deploy solutions in the market. In April, Bloom Energy announced an expansion with Oracle for a total of 2.8GW of fuel cell capacity with 1.2GWs shipping now.
Following the capacity announcement, Oracle announced Project Jupiter yesterday stating the company will utilize up to 2.45GWs “to fully power the AI data center campus” located in New Mexico. This is an important development as it means the AI data center will not use gas turbines and the diesel generators as originally planned. According to the press release, nitrous oxide emissions will be cut by 92% compared to the previous gas turbine plan.
The following was stated about the new deal: “It will be 100% Bloom. When completed, it will be one of the largest islanded microgrid power facilities in the world. Oracle pivoted to Bloom only solution for 2 main reasons: first, be a responsible corporate citizen and partner by being responsive to resident concerns about air quality, water use, noise and increasing electricity rates.
