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Blackstone President Jon Gray was reported to have expressed a favorable view of AI infrastructure in a Bloomberg interview listed on April 23, 2026. The case for the sector is about more than buying AI chips: it also depends on power, cooling, networking, data access and the ability to place workloads where they can run efficiently. The available interview summary does not include a transcript, so it does not verify that Gray used the exact phrase “right place to be.”
What Gray’s reported view means
A secondary listing of Gray’s Bloomberg interview says he discussed an AI infrastructure boom and a strong IPO market, and summarizes his outlook as favorable toward AI infrastructure as a potential support for economic growth. That is a reported investment thesis, not proof that demand will persist at a particular pace or that infrastructure investments will earn attractive returns. The listing does not provide a full transcript or enough surrounding context to verify the quoted wording. The Bloomberg interview listing is summarized by the secondary interview-summary page.
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For readers, the practical point is that AI’s expansion requires substantial physical capacity. That can create opportunities for infrastructure owners and operators, but whether a specific project or security benefits depends on factors such as power access, utilization, operating costs and financing. The available material does not settle those investment questions.
What counts as AI infrastructure
AI infrastructure is the connected set of facilities and systems that let models be trained and used. It includes accelerators and other compute, but also the supporting components that determine whether that compute can be deployed, supplied with data and operated at scale.
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- Compute and memory: Accelerators perform model workloads, while memory bandwidth affects how quickly data can reach processors.
- Interconnect and networking: Fast links between processors and systems support the movement of data required by large workloads.
- Packaging: Packaging complexity affects how advanced components are assembled into usable systems.
- Data centers, power and cooling: Facilities need reliable electricity and systems to remove heat from dense computing equipment.
- Data location and workload placement: Compute must connect to the data a workload needs, with network performance and latency affecting where it can run.
WisdomTree’s May 12, 2026 commentary describes memory bandwidth, interconnect speed, packaging complexity and power availability as constraints as spending shifts from training toward inference. That is an asset manager’s perspective, not a neutral industry-wide finding. Read WisdomTree’s commentary.
Why power and site selection matter
Power is not just a utility bill; it can determine whether a facility can be built or whether an AI workload can be placed there. Flexential’s September 8, 2026 article reports that 89% of surveyed enterprise IT leaders said reliable grid power influences AI deployment decisions. It also says 55% ranked power cost or pricing differences as the top factor influencing where AI workloads are located. Flexential attributes both figures to its State of AI Infrastructure Report; the original report was not separately reviewed, so treat the figures as survey results reported by Flexential, not independently verified statistics. See Flexential’s article.
A site with available electricity may still be a poor fit if it is far from the data the workload needs. Training and inference can also have different requirements: a facility suited to dense training workloads may not be appropriate for latency-sensitive inference, where distance and network delay can matter more. Cooling, network performance and the ability to scale add further constraints. As Flexential’s operational discussion emphasizes, power availability alone does not identify the right location.
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How to assess the infrastructure thesis
Gray’s reported outlook points to a broad sector, not a single asset type. Anyone evaluating the thesis should distinguish plans and demand claims from evidence that a facility is operating effectively and producing durable economics.
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| Question | Why it matters | Evidence to look for |
|---|---|---|
| Is power available, reliable and affordable? | Without dependable supply at workable prices, capacity may be delayed or costly to operate. | Site-specific power arrangements, delivery timelines and cost assumptions. |
| Which workload will the facility serve? | Training and latency-sensitive inference can require different locations and facility capabilities. | Customer and workload needs, including latency and expected utilization. |
| Can compute reach the necessary data? | Distance, network performance and data location affect whether compute can serve its intended use. | Connectivity, network capacity and the location of relevant data. |
| Can the facility cool and scale its equipment? | Dense computing raises cooling needs, while expansion depends on additional facility capacity. | Cooling design and credible plans for scaling. |
| Is there evidence of durable economics? | Announced demand or planned capacity does not establish achieved utilization or realized returns. | Operating performance, customer commitments and costs, rather than plans alone. |
These checks help explain why “AI infrastructure” should not be treated as one uniform bet. A constraint that benefits one asset—such as access to power—does not by itself establish that the asset is near the data, suitable for the workload or economically attractive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the reported partnership does—and does not—show
Telecom Review Americas reported a Blackstone–Google AI infrastructure partnership, with a $5 billion initial equity commitment from Blackstone-managed funds and an expectation that the first 500 MW of capacity would come online in 2027. The report is secondary and could not be independently checked against a primary announcement in the material available here. Those terms should therefore be treated as unverified reporting, not established project outcomes. Even if the commitment and capacity expectations are accurate, planned investment and expected capacity are not evidence of completed facilities, utilization or returns.
What remains uncertain
The interview summary supports describing Gray’s reported stance as favorable, but not reconstructing his exact words or the full reasoning he gave. The sector evidence identifies real deployment constraints; it does not establish how long demand will remain strong, how quickly new supply will come online, or which companies or investments will benefit. Those outcomes depend on execution and economics as well as demand.
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