Google Cloud and SLB Team Up to Boost Geothermal Energy Adoption

Mar 7, 2025
Google Cloud has announced a collaboration with SLB and Project Innerspace to accelerate the global adoption of geothermal energy, leveraging advanced data and AI technologies.
Google Cloud and SLB Team Up to Boost Geothermal Energy Adoption

Google Cloud has announced a collaboration with SLB and Project Innerspace to accelerate the global adoption of geothermal energy, announced in a press release. This partnership aims to harness the potential of geothermal energy, which could meet global electricity demands 140 times over.

The collaboration will integrate Project Innerspace's GeoMap, a tool developed with the support of over 100 researchers and scientists, with SLB's GeothermEx geothermal consulting services. GeoMap, hosted on Google Earth Engine, utilizes Google Cloud's scalable infrastructure, BigQuery, and Vertex AI to visualize and analyze geothermal potential across various regions.

By combining these resources, the partnership seeks to simplify the identification, development, and deployment of geothermal energy solutions worldwide. This initiative is part of Google's ongoing efforts to support the development of advanced geothermal technologies, providing clean energy around the clock.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Silicon Brief or Daily AI Brief.

Also, consider following us on social media:

AI Chips & Datacenters AI Brief AI Brief (X)
Free newsletter

Silicon Brief

Weekly coverage of AI hardware developments including chips, GPUs, cloud platforms, and data center technology.

Guide
AI interconnect and networking startups

AI interconnect and networking startups

29 private startups developing the chips, optical switches and memory fabrics that move data through AI infrastructure.

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You may also like

Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.