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Indexing Energy-Intensive Infrastructure with H3

· 3 min read
Robert Murphy
Robert Murphy
CEO @ Othersphere

Robert Murphy is CEO of Othersphere, a spatial economics platform spun out of Bill Gates' Breakthrough Energy that helps decide where energy-intensive infrastructure should be built. At the H3 Conference 2026 in New York City he showed how Othersphere tiles the world with H3 so developers and financial firms can screen everywhere for good sites before digging into the details.

Tiling the world at level 8​

Years ago, while working in oil and gas at Chevron, Robert went to a talk on H3 tiles that he called part of the formative thinking behind Othersphere. The company focuses on siting the energy-intensive infrastructure the world needs, such as data centers, renewable plants and refineries, so it gets built as efficiently and sustainably as possible.

Othersphere assigns every kind of data relevant to infrastructure development to H3 tiles, primarily at level 8:

  • Geospatial data: where pipelines and power lines are, population density
  • Time series data: grid carbon intensity, power prices
  • Derived data: average power price, range of temperatures for the tile

The aim is a holistic view of a good project: infrastructure and market fundamentals, but also human and environmental variables. Robert described it as pixelating the world to optimize where infrastructure gets deployed.

Searching the world with Explorer​

Robert splits the work into two layers, global search and location diligence, and H3 does most of its work in the first. Working at level 8 gives up some spatial detail, which is brought back once the search narrows to a specific location.

In a live demo of Explorer, Othersphere's paid tool, he searched for data center sites: tiles with a high enough data center score that are also within a kilometer of a natural gas transmission pipeline. The matches first appear as level 4 tiles, which open up into the individual tiles that meet both criteria. Stacking filters like this across many variables narrows the whole world down to candidate sites, and dropping into any location shows the underlying data live. Robert put this at roughly the 5,000-foot view.

From tile to site with generative AI​

Below that, the work has to move from tiles to the polygon level. Once a level 8 tile looks promising, for example close to power lines with relatively low grid carbon intensity, Othersphere combines the H3 data, the original source data and generative AI into in-depth diligence reports. The H3 data acts as guardrails while the AI digs into community sentiment, local permitting realities and other less quantitative factors that decide whether a project can be built there.

Scout, a public view of the tiles​

Othersphere recently released Scout, a free public tool that runs on H3 under the hood. Robert searched for the address of a site in Texas that he identified as one of OpenAI's Stargate data center facilities. Scout showed the level 8 tile there, a short LLM-generated summary, and the variables behind the data center score, including:

  • grid power prices and natural gas power prices
  • grid carbon intensity
  • IEA data on country-level methane emissions intensity from natural gas infrastructure
  • local buildability, such as whether people live nearby and whether the area is water stressed

The trade-off of tile-level search is precision. Distance from power lines, for example, is measured from the tile centroid, so it can be off by a couple of hundred meters, and a bankable assessment still needs the detailed work. But for someone combing the world for where to build, or checking a site that a solar or data center developer has approached them about, the H3 search saves months of work up front.

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