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SpaceX Starlink ‘space lasers’ successfully tested in orbit for the first time
SpaceX has revealed the first successful test of Starlink satellite ‘space lasers’ in orbit, a significant step along the path to an upgraded “Version 2” constellation.
In simple terms, those “lasers” are a form of optical (light-based) communication with an extremely high bandwidth ceiling, potentially permitting the wireless, high-speed transfer of vast quantities of data over equally vast distances. Of the ~715 Starlink satellites SpaceX has launched over the last 16 months, some 650 are operational Version 1 (v1.0) spacecraft designed to serve a limited group of customers in the early stages of the constellation. Prior to SpaceX’s September 3rd announcement, it was assumed that none of those satellites included laser interlinks, but now we know that two spacecraft – presumably launched as part of Starlink-9 or -10 in August – have successfully tested prototype lasers in orbit.
Ever since CEO Elon Musk first revealed SpaceX’s satellite internet ambitions in early 2015, those plans have included some form of interconnection between some or all of the thousands of satellites the company would need to launch. While a functional low Earth orbit (LEO) satellite internet constellation doesn’t intrinsically need to have that capability to function or be successful, inter-satellite links offer some major benefits in return for the added spacecraft complexity and cost.
The single biggest draw of laser interlinks is arguably the major reduction in connection latency (ping) they can enable compared to a similar network without it. By moving a great deal of the work of networking into orbit, the data transported on an interlinked satellite network would theoretically require much less routing to reach an end-user, physically shortening the distance that data has to travel. The speed of light (300,000 kilometers per second) may be immense but even on the small scale of the planet Earth, with the added inefficiencies inherent in even the best fiber optic cables, routing data to and from opposite ends of the planet can still be slowed down by high latency.
Without interlinks, Starlink and internet constellations like it function by acting more like a go-between for individual users and fixed ground stations that then connect those users to the rest of the Internet. Under that regime, the performance of constellations is inherently filtered through the Earth’s existing internet infrastructure and is necessitates the installation of ground stations relatively close to network users. If a satellite without interlinks can ‘see’ (and thus communicate with) customers but can’t ‘see’ a ground station from the same orbital vantage point, it is physically incapable of connecting those communications with the rest of the internet.
This isn’t a showstopper. As SpaceX’s very early Starlink constellation has already demonstrated through beta testers, the network is already capable of serving individual users 100 megabits per second (Mbps) of bandwidth with latency roughly comparable to average wired connections. The result: internet service that is largely the same as (if not slightly worse and less convenient than) existing fiber options. To fully realize a LEO internet constellation’s potential of being much better than fiber, high-performance laser interlinks are thus a necessity.
With laser interlinks, the aforementioned connection dropout scenario would be close to impossible. In the event that an active satellite finds itself serving customers without a ground station in reach, it would route those forlorn data packages by laser to a different satellite with immediate ground station access. One step better, with enough optimization, user communications can be routed by laser to and from the ground stations physically closest to the user and their traffic destination. With a free-floating network of satellites communication in vacuum along straight lines, nothing short of a direct, straight fiber line could compete with the resulting latency and routing efficiency.
Interlinks offer one last significant benefit: by sacrificing latency, an interlinked network will be able to service a larger geographic area by allowing the connections of users far from ground stations to be routed through other satellites to the nearest ground station. Large-scale ground station installation and the international maze of permitting it requires can take an inordinate amount of time and resources for nascent satellite communications constellations
SpaceX’s fully-interlinked Starlink Version 2 constellation is targeting latency as low as 8 milliseconds and hopes to raise the bandwidth limit of individual connections to a gigabit or more. As soon as a viable Starlink v2.0 satellite design has been finalized and tested in orbit, SpaceX will likely end v1.0 production and launches, entering the second phase of iteration after the v0.9 to v1.0 jump.
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Nvidia CEO Jensen Huang explains difference between Tesla FSD and Alpamayo
“Tesla’s FSD stack is completely world-class,” the Nvidia CEO said.
NVIDIA CEO Jensen Huang has offered high praise for Tesla’s Full Self-Driving (FSD) system during a Q&A at CES 2026, calling it “world-class” and “state-of-the-art” in design, training, and performance.
More importantly, he also shared some insights about the key differences between FSD and Nvidia’s recently announced Alpamayo system.
Jensen Huang’s praise for Tesla FSD
Nvidia made headlines at CES following its announcement of Alpamayo, which uses artificial intelligence to accelerate the development of autonomous driving solutions. Due to its focus on AI, many started speculating that Alpamayo would be a direct rival to FSD. This was somewhat addressed by Elon Musk, who predicted that “they will find that it’s easy to get to 99% and then super hard to solve the long tail of the distribution.”
During his Q&A, Nvidia CEO Jensen Huang was asked about the difference between FSD and Alpamayo. His response was extensive:
“Tesla’s FSD stack is completely world-class. They’ve been working on it for quite some time. It’s world-class not only in the number of miles it’s accumulated, but in the way it’s designed, the way they do training, data collection, curation, synthetic data generation, and all of their simulation technologies.
“Of course, the latest generation is end-to-end Full Self-Driving—meaning it’s one large model trained end to end. And so… Elon’s AD system is, in every way, 100% state-of-the-art. I’m really quite impressed by the technology. I have it, and I drive it in our house, and it works incredibly well,” the Nvidia CEO said.
Nvidia’s platform approach vs Tesla’s integration
Huang also stated that Nvidia’s Alpamayo system was built around a fundamentally different philosophy from Tesla’s. Rather than developing self-driving cars itself, Nvidia supplies the full autonomous technology stack for other companies to use.
“Nvidia doesn’t build self-driving cars. We build the full stack so others can,” Huang said, explaining that Nvidia provides separate systems for training, simulation, and in-vehicle computing, all supported by shared software.
He added that customers can adopt as much or as little of the platform as they need, noting that Nvidia works across the industry, including with Tesla on training systems and companies like Waymo, XPeng, and Nuro on vehicle computing.
“So our system is really quite pervasive because we’re a technology platform provider. That’s the primary difference. There’s no question in our mind that, of the billion cars on the road today, in another 10 years’ time, hundreds of millions of them will have great autonomous capability. This is likely one of the largest, fastest-growing technology industries over the next decade.”
He also emphasized Nvidia’s open approach, saying the company open-sources its models and helps partners train their own systems. “We’re not a self-driving car company. We’re enabling the autonomous industry,” Huang said.
Elon Musk
Elon Musk confirms xAI’s purchase of five 380 MW natural gas turbines
The deal, which was confirmed by Musk on X, highlights xAI’s effort to aggressively scale its operations.
xAI, Elon Musk’s artificial intelligence startup, has purchased five additional 380 MW natural gas turbines from South Korea’s Doosan Enerbility to power its growing supercomputer clusters.
The deal, which was confirmed by Musk on X, highlights xAI’s effort to aggressively scale its operations.
xAI’s turbine deal details
News of xAI’s new turbines was shared on social media platform X, with user @SemiAnalysis_ stating that the turbines were produced by South Korea’s Doosan Enerbility. As noted in an Asian Business Daily report, Doosan Enerbility announced last October that it signed a contract to supply two 380 MW gas turbines for a major U.S. tech company. Doosan later noted in December that it secured an order for three more 380 MW gas turbines.
As per the X user, the gas turbines would power an additional 600,000+ GB200 NVL72 equivalent size cluster. This should make xAI’s facilities among the largest in the world. In a reply, Elon Musk confirmed that xAI did purchase the turbines. “True,” Musk wrote in a post on X.
xAI’s ambitions
Recent reports have indicated that xAI closed an upsized $20 billion Series E funding round, exceeding the initial $15 billion target to fuel rapid infrastructure scaling and AI product development. The funding, as per the AI startup, “will accelerate our world-leading infrastructure buildout, enable the rapid development and deployment of transformative AI products.”
The company also teased the rollout of its upcoming frontier AI model. “Looking ahead, Grok 5 is currently in training, and we are focused on launching innovative new consumer and enterprise products that harness the power of Grok, Colossus, and 𝕏 to transform how we live, work, and play,” xAI wrote in a post on its website.
Elon Musk
Elon Musk’s xAI closes upsized $20B Series E funding round
xAI announced the investment round in a post on its official website.
xAI has closed an upsized $20 billion Series E funding round, exceeding the initial $15 billion target to fuel rapid infrastructure scaling and AI product development.
xAI announced the investment round in a post on its official website.
A $20 billion Series E round
As noted by the artificial intelligence startup in its post, the Series E funding round attracted a diverse group of investors, including Valor Equity Partners, Stepstone Group, Fidelity Management & Research Company, Qatar Investment Authority, MGX, and Baron Capital Group, among others.
Strategic partners NVIDIA and Cisco Investments also continued support for building the world’s largest GPU clusters.
As xAI stated, “This financing will accelerate our world-leading infrastructure buildout, enable the rapid development and deployment of transformative AI products reaching billions of users, and fuel groundbreaking research advancing xAI’s core mission: Understanding the Universe.”
xAI’s core mission
Th Series E funding builds on xAI’s previous rounds, powering Grok advancements and massive compute expansions like the Memphis supercluster. The upsized demand reflects growing recognition of xAI’s potential in frontier AI.
xAI also highlighted several of its breakthroughs in 2025, from the buildout of Colossus I and II, which ended with over 1 million H100 GPU equivalents, and the rollout of the Grok 4 Series, Grok Voice, and Grok Imagine, among others. The company also confirmed that work is already underway to train the flagship large language model’s next iteration, Grok 5.
“Looking ahead, Grok 5 is currently in training, and we are focused on launching innovative new consumer and enterprise products that harness the power of Grok, Colossus, and 𝕏 to transform how we live, work, and play,” xAI wrote.
Nvidia CEO Jensen Huang explains difference between Tesla FSD and Alpamayo
Elon Musk confirms xAI’s purchase of five 380 MW natural gas turbines
