NEWS
Orbital is planning for its first test mission to deploy AI data centers in low Earth orbit – aiming to overcome Earth-based power and cooling limitations.
Backed by a16z Speedrun, Andreessen Horowitz’ s startup accelerator, the company will launch Orbital-1 in April 2027 while opening its R & D facility, Factory-1, in Los Angeles.
As demand for AI compute accelerates, energy – not hardware – has become the primary constraint. Orbital’ s approach shifts compute infrastructure into space, where continuous solar power and radiative cooling offer a scalable alternative.
Orbital secures funding and targets 2027 launch for space-based AI data center test mission
Orbital founder Euwyn Poon emphasized that traditional data centers are limited by electricity and cooling challenges. Orbital instead focuses on AI inference workloads – which can be distributed across multiple nodes in orbit, unlike tightly coupled training systems.
Orbital-1 will launch aboard a SpaceX Falcon 9, testing sustained GPU performance, radiation resilience and commercial AI inference capabilities in space. The company is also preparing regulatory filings to deploy a full satellite constellation.
Poon, previously founder of Spin, sees orbital infrastructure as essential to scaling AI.
“ The energy ceiling on AI is real,” he said.“ This is the solution.”
“ Orbital is taking on AI’ s biggest constraint with a bold idea,” said Andrew Chen, a16z.
The company is developing a constellation of satellites equipped with NVIDIA-powered servers. Operating in sun-synchronous orbit, these systems benefit from uninterrupted solar exposure and eliminate reliance on terrestrial power grids.
US public sector applies AI to mainframe modernization
US public-sector organizations are increasingly applying AI to modernize and sustain mainframe environments, according to a new report from Information Services Group.
The 2026 ISG Provider Lens Mainframe Services and Solutions report highlights how agencies are rethinking strategies as the December 2026 deadline for pandemic-era funding approaches.
Agencies must balance maintaining mission-critical systems with reducing costs, improving efficiency and ensuring long-term sustainability, the report says, identifying AI emerging as a key enabler, helping organizations address longstanding challenges without disrupting essential services.
By documenting legacy applications and extracting business rules, AI tools improve understanding of complex systems often maintained for decades by retiring experts. This, the report says, reduces reliance on scarce talent and simplifies onboarding for new staff.
Public-sector organizations are increasingly adopting targeted modernization strategies focused on business outcomes rather than full-scale transformations. The report says agencies are determining which applications to retain on mainframes, integrate with cloud platforms or retire entirely. Secure APIs are facilitating connections between legacy systems and cloud environments, enabling data access for analytics and AI while maintaining strict security controls
Cost pressures and evolving cybersecurity risks are also influencing decisions. Many agencies are, the report says, embracing hybrid architectures and selectively offshoring non-sensitive workloads to enhance efficiency. Technologies such as microservices and DevOps support this transition while preserving control over sensitive data.
Additionally, mainframes are being used as platforms for AI processing and AI is helping build test cases to evaluate migration strategies. These innovations provide critical support as agencies navigate funding challenges and workforce constraints. www. intelligentcio. com
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