The Cisco logo is on display at the Mobile World Congress in Barcelona, Spain, on February 26, 2024.
Charlie Perez | Nurphoto | Getty Images
Enterprise technology titan Cisco Systems on Thursday unveiled a new security architecture product aimed at securing data centers, clouds, and other IT environments with the help of AI.
Called HyperShield, the product uses AI to protect applications, devices, and data across public and private data centers, clouds, and physical locations, according to a company press release.
HyperShield follows the company’s $28 billion acquisition of Splunk last year, a cybersecurity company competing with the likes of DataDog, Elastic, SolarWinds, and Dynatrace. Its launch also builds on Cisco’s partnership with Nvidia on managing and securing AI infrastructure.
“This is not a product, but a new architecture – the first version of something new,” Jeetu Patel, Cisco’s executive vice president and general manager of security and collaboration, told CNBC in an interview this week.
Other brands are also moving in a similar direction. Hewlett Packard Enterprise recently announced new large AI model integrations for its Aruba networking division, while Broadcom’s VMWare launched a tool to allow companies to use generative AI products in a privacy-secure way.
How it works
HyperShield serves as a “shield for security,” Patel said, explaining that it takes security directly to the things that need to be secured.
The technology acts like a “fabric,” rather than a “fence,” giving cyber workers better visibility of software vulnerabilities across applications, according to Patel.
The product has an autonomous segmentation feature aimed at helping businesses avoid vulnerabilities and breaches. It allows Cisco’s AI to divide a computer network into smaller parts to improve performance and security.
Another feature, called self-qualifying upgrades, lets organizations automate the process of testing and deploying upgrades.
Patel said organizations dealing with critical infrastructure — such as oil rigs, internet of things (IoT) devices, and MRI machines in hospitals — need to take particular care when upgrading their systems.
Designed with AI in mind
Patel said Cisco’s HyperShield technology was designed with a new world of digital AI assistants – like ChatGPT, Google Gemini, and other advanced tools – in mind.
“We’re moving from a world of scarcity to a world of abundance, with digital AI assistants for everything,” Patel told CNBC. “Those assistants live in data centres.”
“So when you consider the increase in requirements that this places on the data centre, and how we build for that, there is a need to rearchitect, not build more of the same,” said Patel.
He noted that a security architecture like HyperShield hadn’t been built previously because much of the architectures across the industry were created in a time when modern-day applications and technologies like generative AI didn’t exist.
It currently takes roughly four days for a network vulnerability to be discovered before it’s exploited, and the time taken to patch it is even longer at an average 45 days, according to Patel.
He said that new technologies like AI and machine learning are needed to identify and patch vulnerabilities to be compressed from days to minutes.
“Previously you had to work on the assumption that a breach had happened, [and that] once someone was in, there was lateral movement that you had to identify before you could respond,” Patel told CNBC.
“We need to move to a position where we can predict and respond.”
Why it matters for investors
Cisco shares have underperformed the Nasdaq in the last 12 months, falling nearly 5% year-over-year while the tech-heavy index has jumped over 30%.
Over the past five years, it’s been an even worse investment relative to the broader sector. The stock is down 14% over that stretch, trailing the Nasdaq’s 95% gain.
Cisco share price performance year-over-year, compared with the performance of the Nasdaq Composite over the same period.
Cisco has long been the world’s largest maker of computer networking equipment, like switches, modems, and routers. It’s been boosting its cybersecurity business to meet customer demands and fuel growth.
That’s where the company’s blockbuster acquisition of Splunk comes in: Splunk’s technology helps businesses monitor and analyze their data to minimize the risk of hacks and resolve technical issues faster.
As the public cloud has gobbled up more of Cisco’s traditional back-end business, the company has needed to find new and bigger revenue streams — with cybersecurity emerging as a key bet.
– CNBC’s Rohan Goswami and Jordan Novet contributed to this report
Larry Ellison, Oracle’s co-founder and chief technology officer, appears at the Formula One British Grand Prix in Towcester, U.K., on July 6, 2025.
Jay Hirano | Sopa Images | Lightrocket | Getty Images
Oracle is scheduled to report fiscal second-quarter results after market close on Wednesday.
Here’s what analysts are expecting, according to LSEG:
Earnings per share: $1.64 adjusted
Revenue: $16.21 billion
Wall Street expects revenue to increase 15% in the quarter that ended Nov. 30, from $14.1 billion a year earlier. Analysts polled by StreetAccount are looking for $7.92 billion in cloud revenue and $6.06 billion from software.
The report lands at a critical moment for Oracle, which has tried to position itself at the center of the artificial intelligence boom by committing to massive build-outs. While the move has been a boon for Oracle’s revenue and its backlog, investors have grown concerned about the amount of debt the company is raising and the risks it faces should the AI market slow.
The stock plummeted 23% in November, its worst monthly performance since 2001 and, as of Tuesday’s close, is 33% below its record reached in September. Still, the shares are up 33% for the year, outperforming the Nasdaq, which has gained 22% over that stretch.
Over the past decade, Oracle has diversified its business beyond databases and enterprise software and into cloud infrastructure, where it competes with Amazon, Microsoft and Google. Those companies are all vying for big AI contracts and are investing heavily in data centers and hardware necessary to meet expected demand.
OpenAI, which sparked the generative AI rush with the launch of ChatGPT three years ago, has committed to spending more than $300 billion on Oracle’s infrastructure services over five years.
“Oracle’s job is not to imagine gigawatt-scale data centers. Oracle’s job is to build them,” Larry Ellison, the company’s co-founder and chairman, told investors in September.
Oracle raised $18 billion during the period, one of the biggest issuances on record for a tech company. Skeptical investors have been buying five-year credit default swaps, driving them to multiyear highs. Credit default swaps are like insurance for investors, with buyers paying for protection in case the borrower can’t repay its debt.
“Customer concentration is a major issue here, but I think the bigger thing is, How are they going to pay for this?” said RBC analyst Rishi Jaluria, who has the equivalent of a hold rating on Oracle’s stock.
During the quarter, Oracle named executives Clay Magouyrk and Mike Sicilia as the company’s new CEOs, succeeding Safra Catz. Oracle also introduced AI agents for automating various facets of finance, human resources and sales.
Executives will discuss the results and issue guidance on a conference call starting at 5 p.m. ET.
The U.S. has banned the export of Nvidia’s Blackwell chips, which are considered the company’s most advanced offerings, to China in an effort to stay ahead in the AI race.
DeepSeek is reportedly using chips that were snuck into the country without authorization, according to The Information.
“We haven’t seen any substantiation or received tips of ‘phantom datacenters’ constructed to deceive us and our OEM partners, then deconstructed, smuggled, and reconstructed somewhere else,” a Nvidia spokesperson said in a statement. “While such smuggling seems farfetched, we pursue any tip we receive.”
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Nvidia has been one of the biggest winners of the AI boom so far because it develops the graphics processing units (GPUs) that are key for training models and running large workloads.
Since the hardware is so crucial for advancing AI technology, Nvidia’s relationship with China has become a political flashpoint among U.S. lawmakers.
President Donald Trump on Monday said Nvidia can ship its H200 chips to “approved customers” in China and elsewhere on the condition that the U.S. will get 25% of those sales.
The announcement was met with pushback from some Republicans.
DeepSeek spooked the U.S. tech sector in January when it released a reasoning model, called R1, that rocketed to the top of app stores and industry leaderboards. R1 was also created at a fraction of the cost of other models in the U.S., according to some analyst estimates.
In August, DeepSeek hinted that China will soon have its own “next generation” chips to support its AI models.
The Starcloud-1 satellite is launched into space from a SpaceX rocket on November 2, 2025.
Courtesy: SpaceX | Starcloud
Nvidia-backed startup Starcloud trained an artificial intelligence model from space for the first time, signaling a new era for orbital data centers that could alleviate Earth’s escalating digital infrastructure crisis.
Last month, the Washington-based company launched a satellite with an Nvidia H100 graphics processing unit, sending a chip into outer space that’s 100 times more powerful than any GPU compute that has been in space before. Now, the company’s Starcloud-1 satellite is running and querying responses from Gemma, an open large language model from Google, in orbit, marking the first time in history that an LLM has been has run on a high-powered Nvidia GPU in outer space, CNBC has learned.
“Greetings, Earthlings! Or, as I prefer to think of you — a fascinating collection of blue and green,” reads a message from the recently launched satellite. “Let’s see what wonders this view of your world holds. I’m Gemma, and I’m here to observe, analyze, and perhaps, occasionally offer a slightly unsettlingly insightful commentary. Let’s begin!” the model wrote.
Starcloud’s output Gemma in space. Gemma is a family of open models built from the same technology used to create Google’s Gemini AI models.
Starcloud
Starcloud wants to show outer space can be a hospitable environment for data centers, particularly as Earth-based facilities strain power grids, consume billions of gallons of water annually and produce hefty greenhouse gas emissions. The electricity consumption of data centers is projected to more than double by 2030, according to data from the International Energy Agency.
Starcloud CEO Philip Johnston told CNBC that the company’s orbital data centers will have 10 times lower energy costs than terrestrial data centers.
“Anything you can do in a terrestrial data center, I’m expecting to be able to be done in space. And the reason we would do it is purely because of the constraints we’re facing on energy terrestrially,” Johnston said in an interview.
Johnston, who co-founded the startup in 2024, said Starcloud-1’s operation of Gemma is proof that space-based data centers can exist and operate a variety of AI models in the future, particularly those that require large compute clusters.
“This very powerful, very parameter dense model is living on our satellite,” Johnston said. “We can query, it and it will respond in the same way that when you query a chat from a database on Earth, it will give you a very sophisticated response. We can do that with our satellite.”
In a statement to CNBC, Google DeepMind product director Tris Warkentin said that “seeing Gemma run in the harsh environment of space is a testament to the flexibility and robustness of open models.”
In addition to Gemma, Starcloud was able to train NanoGPT, an LLM created by OpenAI founding member Andrej Karpathy, on the H100 chip using the complete works of Shakespeare. This led the model to speak in Shakespearean English.
Starcloud — a member of the Nvidia Inception program and graduate from Y Combinator and the Google for Startups Cloud AI Accelerator — plans to build a 5-gigawatt orbital data center with solar and cooling panels that measure roughly 4 kilometers in both width and height. A compute cluster of that gigawatt size would produce more power than the largest power plant in the U.S. and would be substantially smaller and cheaper than a terrestrial solar farm of the same capacity, according to Starcloud’s white paper.
These data centers in space would capture constant solar energy to power next-generation AI models, unhindered by the Earth’s day and night cycles and weather changes. Starcloud’s satellites should have a five-year lifespan given the expected lifetime of the Nvidia chips on its architecture, Johnston said.
Orbital data centers would have real-world commercial and military use cases. Already, Starcloud’s systems can enable real-time intelligence and, for example, spot the thermal signature of a wildfire the moment it ignites and immediately alert first responders, Johnston said.
“We’ve linked in the telemetry of the satellite, so we linked in the vital signs that it’s drawing from the sensors — things like altitude, orientation, location, speed,” Johnston said. “You can ask it, ‘Where are you now?’ and it will say ‘I’m above Africa and in 20 minutes, I’ll be above the Middle East.’ And you could also say, ‘What does it feel like to be a satellite? And it will say, ‘It’s kind of a bit weird’ … It’ll give you an interesting answer that you could only have with a very high-powered model.”
Starcloud is working on customer workloads by running inference on satellite imagery from observation company Capella Space, which could help spot lifeboats from capsized vessels at sea and forest fires in a certain location. The company will include several Nvidia H100 chips and integrate Nvidia’s Blackwell platform onto its next satellite launch in October 2026 to offer greater AI performance. The satellite launching next year will feature a module running a cloud platform from cloud infrastructure startup Crusoe, allowing customers to deploy and operate AI workloads from space.
“Running advanced AI from space solves the critical bottlenecks facing data centers on Earth,” Johnston told CNBC.
“Orbital compute offers a way forward that respects both technological ambition and environmental responsibility. When Starcloud-1 looked down, it saw a world of blue and green. Our responsibility is to keep it that way,” he added.
The risks
Risks in operating orbital data centers remain, however. Analysts from Morgan Stanley have noted that orbital data centers could face hurdles such as harsh radiation, difficulty of in-orbit maintenance, debris hazards and regulatory issues tied to data governance and space traffic.
Still, tech giants are pursuing orbital data centers given the prospect of nearly limitless solar energy and greater, gigawatt-sized operations in space.
Along with Starcloud and Nvidia’s efforts, several companies have announced space-based data center missions. On Nov. 4, Google unveiled a “moonshot” initiative titled Project Suncatcher, which aims to put solar-powered satellites into space with Google’s tensor processing units. Privately-owned Lonestar Data Holdings is working to put the first-ever commercial lunar data center on the moon’s surface.
Referring to Starcloud’s launch in early November, Nvidia senior director of AI infrastructure Dion Harris said: “From one small data center, we’ve taken a giant leap toward a future where orbital computing harnesses the infinite power of the sun.”