ENTO Subsidiary Grid AI Announces Letter of Intent with First Hyperscaler AI Data-Center Customer

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ENTO Subsidiary Grid AI Announces Letter of Intent with First Hyperscaler AI Data-Center Customer

BOULEVARD 19.11.2025

BOCA RATON, FLORIDA / ACCESS Newswire / November 19, 2025 / Entero Therapeutics, Inc. (NASDAQ:ENTO) ("Entero" or the "Company") today announced that its subsidiary Grid AI has executed a Letter of Intent (LOI) with a stealth-mode hyperscaler artificial intelligence (AI) data-center developer to deploy Grid AI's advanced power-cluster optimization and orchestration platform at the customer's first large-scale AI campus. The campus-expected to be among the more sophisticated AI-ready digital-infrastructure sites in development-will be located in the ERCOT market in Texas.

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The LOI represents Grid AI's first hyperscaler agreement and marks a major milestone in Entero's strategy to build the leading global platform for AI-driven power orchestration across next-generation data-center campuses. The definitive commercial contract is expected to be executed in early Q1 2026.

The stealth mode customer's initial Texas campus is scheduled to begin generating revenue for Grid AI in Q3 2026 with approximately $8 million in annual revenue in 2027, scaling to over $50 million in 2029, with multiple additional campuses already in the customer's development pipeline.

"This LOI is a foundational moment for Grid AI," said Jason Sawyer, Entero Therapeutics' CEO. "The power requirements of hyperscale AI development are creating one of the most consequential infrastructure challenges of the next decade. Grid AI is positioned to become the premier orchestration layer that synchronizes generation, storage, market participation, and compute load at the largest and most complex AI campuses around the world."

A Strategic Partnership to Deliver AI-Ready Intelligent Infrastructure

The LOI outlines a multi-phase strategic partnership to develop a best-in-class orchestration platform that unifies power-cluster design, coordinated dispatch, commercial optimization, and data analytics. The platform's architecture reflects key elements described in the LOI between the stealth mode customer and GridAI, including:

An initial deployment site in Texas, used to validate and scale up a world-class optimization engine designed specifically for large-scale digital-infrastructure assets.
Development of a scalable, high-performance infrastructure model integrating behind-the-meter generation, storage, and flexible load management to deliver cost-efficient, resilient, and grid-supportive power solutions.
A unified technoeconomic simulation model to quantify $/MWh, reliability, and PUE impacts across various asset configurations and commercial structures.
A next-generation dispatch-control and commercial-optimization engine capable of co-optimizing grid imports, battery assets, and gas-generation blocks, while coordinating financial and physical hedge positions.
A secure, cloud-based data-capture and reporting platform providing continuous operational visibility and market transparency for data-center operators.

"AI-centered digital infrastructure is growing and changing at an unprecedented rate, and the power-optimization stack needs to advance with it," said Marshall Chapin, CCO and Acting CEO of Grid AI. "This LOI signals that Grid AI is ready to support hyperscale operators with real-time orchestration tools capable of managing multi-hundred-megawatt power clusters with precision, economic intelligence, and reliability, ultimately delivering enormous value to our customers."

A Multi-Campus Global Opportunity

While the customer remains in stealth mode, the LOI notes that the initial Texas campus is the first of several planned facilities. The multi-campus roadmap creates strong visibility into future revenue streams and supports a scalable go-to-market model for Grid AI.

Beyond this initial customer, Grid AI is now positioned to pursue a rapidly expanding global opportunity as hyperscalers, cloud providers, and private AI-infrastructure developers race to build energy-dense campuses requiring: