The Behind-the-Meter AI Buildout
Why Bloom Energy’s Q2 shows that onsite power may be starting to scale
BTM extends the capacity-partner model
In our prior report, The Hyperscaler Capacity Partner Hierarchy, we talked about the importance of partners for hyperscalers, particularly those with a specific type of business model that allowed them to keep a preferred margin profile when they could not secure infrastructure fast enough to convert their backlog. We think, as the market adopts behind-the-meter power and standards emerge, this can accelerate time to power and perhaps shift the speed at which hyperscalers can get land, shells, and other physical infrastructure in place (all things easier to secure and build), then work with a partner like Bloom, and others, to bring capacity online while they still work to get broader grid connectivity.
Grid connectivity is the hardest part of this equation. The hyperscalers that needed to monetize their compute were doing deals with those who had secured power via the grid, often on less favorable terms that impacted their margins, because they were desperate for power. As BTM scales and becomes more viable, it gives them another choice since it can operate without going through all the hoops needed to secure a grid connection. We are not saying they will stop pursuing grid connections. By adopting BTM, they can scale the easier part of the project, if anything in this process is actually easy: getting a shell built and all the surrounding infrastructure in place. That gives them first-party ownership instead of forcing them to go through partners simply because those partners have grid contracts done and ready to go. The maturation of BTM means the option now exists to add the grid later. That is why BTM can cost more and still be the better choice.
Inference changes the equation for BTM
From conversations a year ago, when BTM seemed more like a theoretical with potential but needed to be proved out, a few things have changed. First, the AI infrastructure mix moving from training toward more inference is a big driver. In training, the workloads are bursty and thus prone to massive power spikes versus more nominal and consistent power draw. Inference is less bursty and more manageable as a workload, meaning that having a range of redundancies in place to handle power spikes, which was a challenge for pure BTM, is less of an issue with inference. But even with training workloads now, the latest GPUs and rack-scale systems are putting more power control into their systems to help regulate power spikes and make them smoother. One data center operator we spoke with told us anything around 25% or lower for this kind of power spike was low enough that BTM can suffice, and that the latest rack-scale systems can keep those spikes at 25% or lower.
Public data supports the view from our conversations with those in the power industry. Microsoft production data measured a maximum two-second change of 37.5% of provisioned power for training, compared with 9% for interactive inference. NVIDIA’s GB200 documentation describes programmable power smoothing, while its newer Vera Rubin rack architecture adds more local energy buffering. We would not treat 25% as a universal engineering cutoff. It is one operator’s practical marker, and the broader point is that better rack-level power management gives the onsite generation system a smoother load to follow. Smoother workloads are one enabler of the BTM shift. Grid delays and the economics of bringing compute online earlier remain the larger forces.
Customers have moved from “can we use BTM?” to “how will we use it?”
We are not saying BTM becomes the standard or only source of power, only that BTM was not in a place where it was as viable as it appears now. That waas clear from Bloom’s call where they disclosed every major customer is now engaged to use BTM as a way to get to revenue faster. Customers have moved from BTM looks good on paper to all systems go to ramp BTM. We are in a cycle where the supply chain needs to scale and start to ramp to meet demand so customers can use BTM either to bridge the time to grid service or offset some grid needs and work toward more favorable power economics by using a hybrid architecture. BTM seems to be inflecting, for all the reasons we include, and now the main players need to start ramping their supply chains to meet demand.
From talking to operators in the field, we had consistently heard about the challenge that is electrical integration for the full site. While the equipment that goes into a BTM solution is one piece of the puzzle, the customer still needs to secure labor and testing, then bring together switchgear, transformers, UPS capacity, microgrid controls and a host of other things. The best-positioned companies are those that have already secured the fuel, have utility relationships and have sites ready to go.
Power is only part of the equation when analyzing who captures most of the value. This is why the ability to control the complete delivery schedule and stand behind the contract matters, because any delay has a direct impact on the customer’s time to revenue. The guarantee must cover an operating outcome. That shifts the risk of turning a power plan into operating compute away from the hyperscaler and onto the power or capacity partner.
Bloom is the clearest public proof point for now
Bloom went from essentially entering this market nine months ago, per the call, with one customer, to management saying its technology is now validated and approved by all major U.S. hyperscalers and more than a dozen other AI infrastructure customers. Those relationships span live deployments, booked and shipped systems under construction, and definitive agreements. Customer engagement has clearly inflected. The next proof is whether those approvals become repeat deployments and accepted, revenue-producing critical IT MW.
The reported results now sit behind that claim. Revenue reached $1.065 billion in Q2, up 165.5% from a year earlier, while GAAP gross margin increased to 33.4%. The next proof is repeat orders and accepted, revenue-producing critical IT MW.
A handful of large projects can make the market appear broader than it is. Developers may reserve several generation options for the same campus, while different suppliers count the same prospective demand. Repeat deployments provide stronger evidence. They show that the first installation worked well enough for the customer to use the architecture again. Accepted, revenue-producing critical IT MW then confirms that the project cleared every gate: fuel, permits, generation, electrical integration, commissioning, and usable compute. That would establish BTM as a repeatable procurement model rather than a collection of emergency power projects.
Inside the Full Report
How BTM gives hyperscalers a faster path to owned compute
The six gates between a power plan and usable capacity
Where Bloom leads and how engines and turbines compete
What Bloom’s Q2 confirms and what still needs proof
Who captures value across the BTM delivery stack
How BTM can scale and what would weaken the thesis