Overview Energy plans to collect sunlight in orbit and send it to Earth, giving Meta early access to a new source of round-the-clock power
Updated
April 29, 2026 3:20 PM
A corona mass ejection erupts from our sun. PHOTO: UNSPLASH
Overview Energy, a startup focused on space-based power systems, has announced a new agreement with Meta to develop a new source of electricity for data centers. The partnership centres on space solar energy, with an orbital demonstration planned for 2028 and commercial power delivery targeted for 2030.
The deal gives Meta early access to as much as 1 gigawatt of future capacity from Overview’s system. That matters because large technology companies are searching for reliable power sources as demand rises from AI computing and data center expansion.
Overview’s idea is straightforward, though the engineering is ambitious. The company plans to place satellites in orbit that collect sunlight continuously in space. That energy would then be sent to existing solar sites on Earth, where it would be converted into electricity.
Unlike ground-based solar farms, which only generate power when the sun is shining locally, a space-based system is designed to extend power generation beyond daylight hours. In theory, this could help solar facilities produce electricity around the clock without using extra land.
"Space solar technology represents a transformative step forward by leveraging existing terrestrial infrastructure to deliver new, uninterrupted energy from orbit. We're excited to partner with Overview Energy to pioneer innovative energy solutions to advance our AI ambitions and infrastructure", said Nat Sahlstrom, VP of Energy and Sustainability, Meta. "This collaboration demonstrates our commitment to innovation – leveraging cutting-edge technology to strengthen America's energy leadership".
For Meta, the agreement is less about a near-term energy fix and more about securing future options. Major data center operators are increasingly competing for electricity as AI systems require more computing power and more cooling capacity. Traditional energy projects can take years to build, making alternative supply models more attractive.
Overview says its system is designed to work with solar projects that already exist. Instead of building entirely new power plants, the company aims to increase output from current sites by adding energy received from orbit.
"Space is becoming part of America's energy infrastructure", said Marc Berte, CEO of Overview Energy. "Our approach to space solar energy enables hyperscalers and technology providers to secure clean power with reliable siting, and speed to power.” "Together with Meta, we're looking beyond traditional constraints on where and when power can be delivered to meet the growing demand for electricity".
The larger significance of the partnership is what it signals about the energy market. As AI infrastructure expands, companies are beginning to look beyond conventional grids, gas plants and land-based renewables. Technologies once considered experimental are now being explored as part of long-term infrastructure planning.
There is still a long road ahead. Space solar power has been discussed for decades, but commercial deployment remains unproven. Launch costs, regulation and system reliability will all matter.
Even so, the Meta-Overview agreement shows how rising demand for constant power is reshaping where the technology sector looks for its next energy source.
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Examining the shift from fast answers to verified intelligence in enterprise AI.
Updated
January 8, 2026 6:33 PM
Startup employee reviewing business metrics on an AI-powered dashboard. PHOTO: FREEPIK
Neuron7.ai, a company that builds AI systems to help service teams resolve technical issues faster, has launched Neuro. It is a new kind of AI agent built for environments where accuracy matters more than speed. From manufacturing floors to hospital equipment rooms, Neuro is designed for situations where a wrong answer can halt operations.
What sets Neuro apart is its focus on reliability. Instead of relying solely on large language models that often produce confident but inaccurate responses, Neuro combines deterministic AI — which draws on verified, trusted data — with autonomous reasoning for more complex cases. This hybrid design helps the system provide context-aware resolutions without inventing answers or “hallucinating”, a common issue that has made many enterprises cautious about adopting agentic AI.
“Enterprise adoption of agentic AI has stalled despite massive vendor investment. Gartner predicts 40% of projects will be canceled by 2027 due to reliability concerns”, said Niken Patel, CEO and Co-Founder of Neuron7. “The root cause is hallucinations. In service operations, outcomes are binary. An issue is either resolved or it is not. Probabilistic AI that is right only 70% of the time fails 30% of your customers and that failure rate is unacceptable for mission-critical service”.
That concern shaped how Neuro was built. “We use deterministic guided fixes for known issues. No guessing, no hallucinations — and reserve autonomous AI reasoning for complex scenarios. What sets Neuro apart is knowing which mode to use. While competitors race to make agents more autonomous, we're focused on making service resolution more accurate and trusted”, Patel explained.
At the heart of Neuro is the Smart Resolution Hub, Neuron7’s central intelligence layer that consolidates service data, knowledge bases and troubleshooting workflows into one conversational experience. This means a technician can describe a problem — say, a diagnostic error in an MRI scanner — and Neuro can instantly generate a verified, step-by-step solution. If the problem hasn’t been encountered before, it can autonomously scan through thousands of internal and external data points to identify the most likely fix, all while maintaining traceability and compliance.
Neuro’s architecture also makes it practical for real-world use. It integrates seamlessly with enterprise systems such as Salesforce, Microsoft, ServiceNow and SAP, allowing companies to embed it within their existing support operations. Early users of Neuron7’s platform have reported measurable improvements — faster resolutions, higher customer satisfaction and reduced downtime — thanks to guided intelligence that scales expert-level problem solving across teams.
The timing of Neuro’s debut feels deliberate. As organizations look to move past the hype of generative AI, trust and accountability have become the new benchmarks. AI systems that can explain their reasoning and stay within verifiable boundaries are emerging as the next phase of enterprise adoption.
“The market has figured out how to build autonomous agents”, Patel said. “The unsolved problem is building accurate agents for contexts where errors have consequences. Neuro fills that gap”.
Neuron7 is building a system that knows its limits — one that reasons carefully, acts responsibly and earns trust where it matters most. In a space dominated by speculation, that discipline may well redefine what “intelligent” really means in enterprise AI.