Highway thieves are using sophisticated digital tactics that investigators worry make it easy to rob trucks carrying millions in expensive data center equipment.
NBC News spoke to “several industry leaders, consultants, and investigators advising law enforcement” offices probing the biggest thefts yet and reported that since June, thieves have “made away with more than $100 million in data center freight.”
Here’s how they said thieves get away with it: First, thieves hack freight brokers’ accounts on shipment-listing websites and post fake shipments to lure real bids, then send a malicious link to any trucking companies that respond. Once victims click, thieves install software to take control of their accounts and start bidding on real shipments. Sometimes, a real delivery driver is hired to collect the cargo, unaware of the scam, and other times fake driver’s licenses are used to get accomplices hired. Either way, the goods are eventually transferred to a driver working with the thieves to resell the gear, and, in one of the largest thefts, tracking coordinates were spoofed to prevent digital monitoring.
The lawsuit argues that DoD knew about problems with its cybersecurity practices but continued to “intentionally disregard its cybersecurity responsibilities.”
Elon Musk’s SpaceXAI is stuck facing a lawsuit over alleged data center pollution after Donald Trump failed to convince a judge that his Environmental Protection Agency (EPA) has complete control over all Clean Air Act lawsuits and can dismiss any suit at whim.
Soon after unpermitted gas turbines were discovered powering Musk’s giant Colossus data center in Memphis, Tennessee, the NAACP sued. Its lawsuit sought an injunction requiring xAI (which, following a merger, is known now as SpaceXAI) to use the best available pollution control technology when operating the turbines and ordering penalties up to $124,426 per day per violation. Without such remedies, the turbines risked drastically spiking pollution in already vulnerable neighborhoods housing predominantly Black communities nearby, the NAACP argued.
To block the lawsuit, Trump tried to intervene, arguing that the EPA had the power to unilaterally dismiss lawsuits any time the president decides against enforcing the Clean Air Act. According to Trump, it would supposedly “not serve the public interest” if citizens were allowed to seek massive civil penalties to punish polluting AI firms that were trying to get data centers operational as fast as possible to support Trump’s AI Action Plan.
• Chinese providers are catching up with global leaders in three areas: generative AI, operationalized solutions, and agentic AI, giving enterprises broader options to diversify their AI partners.
• However, gaps in go-to-market, professional services, and model development remain large – crucial areas to drive adoption and accelerate deployments.
AI demand continues to grow rapidly. GlobalData Market Opportunity Forecasts to 2030: Artificial Intelligence (published on June 30, 2026) forecasts the global AI market to increase at 48.2% five-year-CAGR to reach $1.3 trillion in 2030. This is also reflected by 88% of the 3,069 global enterprises interviewed by GlobalData, who increased their AI budget this year (source: GlobalData ICT Customer Insights Survey 2026. Asia-Pacific shows a similar trend with a 48.6% CAGR, accounting for 36.2% of the global AI market in 2026. While growth is reported across the stack from the GPU to the application layer, cloud-based enterprise platforms remain crucial to drive adoption by providing broad and flexible capabilities to develop, govern, and scale AI deployments. GlobalData’s recent updates on Cloud-Based Enterprise AI Platforms: Competitive Landscape Assessment (September 17, 2026) shows that global hyperscalers (Google and Amazon) continue to lead the market, followed closely by Microsoft and IBM. However, Chinese players including Alibaba, Baidu, Huawei, and Tencent are closing the gap. This is important especially in markets like Asia where these players already have a strong presence. This gives enterprises broader options to diversify their AI partners, but also attracts cost-sensitive customers through their cost-leadership strategy.
The Competitive Landscape Assessment report also analyzes vendors’ capabilities, competitive comparison, market assessment, market drivers, buying criteria, and vendor recommendations. It covers 10 vendors: Alibaba, Amazon, Baidu, Dataiku, DataRobot, Google, Huawei, IBM, Microsoft, and Tencent Cloud.
Gaps Getting Smaller
Chinese providers are catching up with global leaders in three areas: generative AI (GenAI), operationalized solutions, and agentic AI. In the report, Alibaba is rated Very Strong in GenAI, the same as Amazon and one level behind the leader, Google. Alibaba’s latest Qwen family lets users toggle reasoning on and off, supports over 200 languages and dialects, and includes the agent-focused Qwen3.7-Max. Meanwhile, Baidu, Huawei, and Tencent are rated Strong. Baidu’s ERNIE 5.1 and Qianfan platform combine in-house models with DeepSeek, Kimi, and GLM. Tencent’s Hunyuan 3D and video models have over 3 million Hugging Face downloads, and Huawei has open-sourced a version of Pangu as OpenPangu.
Operationalized solutions are the clearest convergence. Alibaba and Huawei are both rated Very Strong, matching Amazon and ahead of several others. Alibaba offers vertical portfolios in finance, education, transportation, and retail while Huawei supports over 400 prepackaged AI applications across 500 industrial scenarios. Rated Strong, Tencent’s Productivity Agent Suite spans more than 20 verticals. Tencent is also extending into physical AI with its Hy-Embodied models, Tairos platform, and Unitree partnership, although deployments remain early. Industry-specific solutions are becoming more important for enterprises exploring the technologies as well as when moving from PoC to production.
The gap in agentic AI is closing more slowly. Alibaba, Baidu, and Tencent are rated Strong while Huawei is Competitive. Alibaba offers AgentRun, AgentTeams, and Agent Sandbox. Alibaba also offers a Skills portal, which converts cloud capabilities into MCP-compatible formats. Tencent has ADP 4.0 which is compatible with OpenAI’s Agents SDK) while Huawei offers AgentArts and AgentSphere. This shows not only serious investment by these players, but also increasing initiatives to support open standards rather than build closed ecosystems. However, their development platforms and tooling are rather new and less proven compared to global players.
This growing momentum is underpinned by their vertical integration strategies with investment in infrastructure such as Huawei’s Ascend 950 chips, the 1,024-card Atlas 950 SuperPod, Alibaba’s Zhenwu M890, and Baidu’s Kunlun chips. This is also reflected in their financial performance. Huawei reported $7.5 billion in AI chip revenue, Alibaba claimed 11 consecutive quarters of triple-digit AI revenue growth and over 290,000 GenAI customers, and Tencent said its AI cloud revenue doubles every year.
Where Gaps Remain
The widest gap is go-to-market. Alibaba, Baidu, Huawei, and Tencent are all rated only Competitive. While they have a strong presence in China supported by various references, their global footprints are relatively small: Alibaba has 32 regions in 17 countries, Huawei 34 in 16, and Tencent 21 in 10. All four have a predominantly Asia-centric or China-centric customer base. Geopolitical headwinds are a structural constraint, particularly in North America and Western Europe.
Another key area is professional services. Rated Competitive, these players still lack professional and managed services as well as partner ecosystem, which is crucial in helping enterprises with deployments, but also lag in ethical and governance frameworks. IBM’s 21,000 data and AI consultants and Google’s ecosystem of over 220,000 GenAI-enabled partner consultants set the benchmark.
The third is model development. Chinese providers have end-to-end tooling, such as Alibaba’s PAI, Huawei’s ModelArts, and Tencent’s TI Platform, but the experience is less unified. Tencent’s tooling, for example, is fragmented across TI Platform, ADP, and TokenHub. Global players are also ahead in the governance and monitoring tools that enterprises need to move from PoC to production.
SpaceX’s Falcon 9 rocket rises from its California launch pad, sending 130 payloads into orbit. (Credit: SpaceX via X)
Starfish Space’s first full-scale Otter satellite servicing vehicle went into orbit today, marking a major milestone for the Tukwila, Wash.-based startup.
The Otter spacecraft was among 130 payloads launched from Vandenberg Space Force Base in California aboard a SpaceX Falcon 9 rocket. Otter is roughly the size of a kitchen oven, with solar arrays that were folded up for launch.
Other payloads on the satellite rideshare mission, known as Transporter-18, included Cowboy Space’s first power-beaming satellite, a prototype for Google’s AI data-center constellation, and a NASA-funded probe that will track the energy released by galaxies and supernovas.
Today’s liftoff through surface-level fog — and the return of the Falcon 9’s first-stage booster to a nearby landing pad — proceeded like clockwork. A little more than an hour after launch, Otter separated from the rocket’s upper stage and powered up for orbital operations.
Once Otter is fully commissioned, it will rendezvous with and inspect a series of inoperable satellites for a $15 million NASA mission known as Small Spacecraft Propulsion and Inspection Capability, or SSPICY for short. The capabilities demonstrated by SSPICY could be used during future missions to check the health of active satellites, or dispose of defunct satellites.
SSPICY may not be the only mission that Otter takes on during its operating lifetime.
“Success in the mission is to actually go and provide this commercial inspection service on a number of these different spacecraft, and that will be a huge win in and of itself,” Starfish co-founder Trevor Bennett told GeekWire last week. “But we’re also bringing Otter along to provide servicing well beyond that, so we could go and inspect more spacecraft, or potentially do some additional docking past the initial mission.”
This inaugural Otter mission follows up on two orbital flight tests involving smaller-scale Otter Pup prototypes. The first test fell short of linking up with its target satellite but was able to demonstrate Starfish’s satellite-tracking system. The second Otter Pup went into orbit last year and is currently closing in on its target.
The blue circle highlights the placement of Starfish Space’s Otter spacecraft inside the Falcon 9 rocket’s upper stage prior to launch. (Credit: Starfish Space / SpaceX)
Google’s Project Suncatcher M1 prototype, built by Planet Labs, will test the tensor processing units that Google plans to use for its orbital AI data-center constellation. The prototype is “designed to gather in-orbit data on how our TPUs handle the physical stress of spaceflight and the radiation and thermal extremes of space,” Google said in a blog post.
Star Catcher’s Protostar satellite will be involved in an experiment to beam power from the main spacecraft to a smaller deployable satellite equipped with off-the-shelf solar panels. The Protostar mission is meant to blaze a trail for the orbital power grid that Star Catcher hopes to create.
The SPRITE CubeSat is part of a $4 million mission funded by NASA and led by the University of Colorado that will study how stars and galaxies shaped the evolution of the early universe. The satellite is equipped with a far-ultraviolet imaging spectrograph. SPRITE stands for Supernova Remnants and Proxies for Reionization Testbed Experiment.
• Artificial intelligence (AI) is reshaping Australia’s fiber map, driving investment beyond city limits and into emerging data center locations.
• Route diversity is becoming strategic, with terrestrial and subsea network providing greater resilience and flexibility for future digital workloads.
Australia’s fiber build-out is getting a second wind – and this time, artificial intelligence (AI) is changing the rules of the game. In the same week, Vocus and SUBCO announced new terrestrial and subsea cable projects demonstrating that Australia’s connectivity investment cycle is picking up speed. But this is about more than adding fiber. AI, cloud and data center expansions are changing where capacity is required and creating demand for networks with diverse, resilient routes that can withstand outages, congestion and other disruptions.
Vocus announced that it will extend its terrestrial fiber footprint from eastern into northern Australia with a new 4,000km route connecting Brisbane, Townsville, and Darwin. The route expands the Australian Digital Infrastructure platform that the company announced in June 2026, to support increased demand from hyperscaler, neocloud, and enterprise customers. With a capacity for up to 3,456 fiber strands, the build is designed to accommodate future demand as AI and data center investment spreads beyond the capital cities. This highlights that terrestrial fiber will carry the next wave of data center and AI growth, particularly as demand expands beyond Australia’s major metropolitan hubs.
SUBCO is taking its build offshore, with three new 24-fiber pair systems planned to connect Australia with key markets across the Indo-Pacific and North America. APX North will link Sydney to Japan via Guam, and APX East-2 will add a new route from Guam to Los Angeles. APX West, the shortest of the three will connect Perth with Singapore and extend into Indonesia through Jakarta and Batam. SUBCO aims to complete all three systems by 2029. Together, they will add significant capacity and more route diversity between Australia, Asia, and the US. For carriers, hyperscalers and other high-capacity network customers, the extra capacity matters less than the resilience, redundancy, and connectivity options that diverse routes provide.
The connectivity race will not be won by whoever lays the most fiber, but by whoever lays it where demand is heading. Taken together, the Vocus and SUBCO announcements signal a broader strategic shift in Australia’s connectivity market; the focus is moving from adding capacity to building infrastructure around future digital demand. AI, cloud and data center growth are fueling demand for high-capacity networks, and route diversity and access to multiple international gateways now matter just as much. This turns terrestrial and subsea networks from connectivity assets to strategic digital infrastructure. As AI and data centers create new traffic corridors, the providers that anticipate emerging data center hubs and build diverse routes ahead of demand will be best placed to capture future connectivity growth.
An artist’s conception shows several Sophia Space satellites in orbit. (Sophia Space Illustration)
Sophia Space and SLI, an aerospace leasing venture based in Washington, D.C., say they’ve agreed on a $300 million asset-financing framework that will support the creation of a 10-satellite constellation for high-performance computing.
The details of the arrangement are as notable as the bottom line: Sophia Space will build the satellites, leveraging the startup’s patented TILE technology for modular in-space data processing. SLI will purchase the satellites under the terms of a financing agreement.
“SLI as the lessor will purchase the satellites from Sophia, hold title to the assets, and lease them to the end user on a long-term basis in exchange for fixed monthly or quarterly payments,” Max Yergan, the company’s senior vice president for investments, explained in an email. “Full control and operational responsibility for the assets sit with Sophia and the end user, who will determine between them how operations are handled.”
Sophia’s satellites are designed to deliver on-orbit edge data services for a wide variety of applications. “The demand we are underwriting exists today,” Yergan said. “Earth observation, weather and supply-chain analytics, disaster response and defense ISR [intelligence, surveillance and reconnaissance] users all face the same constraint now: They collect far more data than they can bring to the ground, and its value decays while it waits to be downlinked. Processing on orbit addresses that directly.”
The constellation’s first launch is targeted for as early as 2028. SLI would pay out funds linked to development and launch milestones, all the way through verification that the on-orbit network performs to pre-agreed standards.
The arrangement is laid out in a non-binding letter of support. “The non-binding nature is a reflection of where we are in the process, and is typical of large asset financings,” Yergan explained. “It sets the commercial framework so both parties can commit resources while definitive documentation is negotiated.”
Leasing arrangements are often seen in aviation and the maritime industry, but this is a relatively new concept for satellite ventures.
“This is the first time that this approach has been applied to this kind of constellation, but not the first time for in-space assets,” said Gareth Zundel, SLI’s senior vice president for communications. “In December 2025 we announced the acquisition of two AscendArc satellites that will be offered to operators on leasing terms. Then, in March this year, we did a similar deal with ReOrbit. We do believe, however, that we are the first leasing company to specialize in the space sector.”
Rob DeMillo, Sophia Space’s CEO and co-founder, said the arrangement demonstrates how far the commercial space industry has come.
“Asset financing didn’t invent aviation or shipping, but it accelerated them at scale,” he said in a news release. “We’re doing the same for orbital computing. This approach with SLI signals that Sophia Space’s space infrastructure is mature enough to attract the capital structures that have historically built terrestrial infrastructure.”
The approach also gives Sophia Space — which is headquartered in Pasadena, Calif., but also has strong ties to the Seattle area — access to capital without diluting the equity held by current investors, including Unlock Venture Partners in Seattle.
“Sophia has the technology, the team and the vision. What had been missing was access to scalable, non-dilutive capital,” said Praveen Vetrivel, SLI’s CEO. “This framework provides it, giving them the capacity and flexibility they need to build the next layer of digital infrastructure.”
Each of the 10 satellites in the constellation will link together six of Sophia’s TILE (Thermal Integrated LEO Edge) modules, with four Nvidia Jetson processors on each module. That adds up to 240 edge computing servers in orbit.
“These 10 spacecraft are dedicated to this transaction,” Yergan said. “Sophia’s other programs and partnerships involve separate spacecraft and separate funding and are not directly affected by this facility.”
Sophia Space has previously said that it plans to start selling TILE systems and related components to customers in 2028. The company is also collaborating with Axiom Space, Armada and Kepler Communications on separate in-space computing initiatives.
In June, Sophia Space announced the conclusion of a $7 million financing round that brought the company’s total funding to $22 million. That round took advantage of an arrangement known as a Simple Agreement for Future Equity, or SAFE, in which investors provide cash to a startup in exchange for the right to receive stock later.
Amy Larsen DeCarlo – Principal Analyst, Security and Data Center Services
Summary Bullets:
As rapid advances in AI application development drive demand for more processing capacity, US-based cloud providers are investing heavily in building out facilities to support these deployments.
But not everyone is on board with expansion plans, with public criticism stalling some development efforts, forcing hyperscalers to pivot to new locations, often in more remote areas.
AI is changing the cloud landscape, creating the near-term need for a vast increase in processing power and storage space. Hyperscalers are responding with substantial facility construction plans. Just this year alone, Amazon Web Services, Google, and Microsoft Azure are pouring a total of $500 to $700 billion into extensions of their data center footprints. Given AI’s dominance in enterprise technology investment plans, this is a logical track. However, not everyone is on board with these aggressive development plans -and AI plays a role in that resistance.
Fifty-two percent of adults are more worried than excited about AI, according to results from a Pew Research Center survey of 3,488 adults fielded earlier this year. Those queried had trepidations related to AI about everything from job displacement and interference with human creativity to unreliable or even malicious output.
AI anxiety is translating into opposition to in-region data center expansion. A Gallup poll of 1,000 US adults conducted earlier this year, found that 71% of those surveyed are totally opposed to the building of new data center facilities used to support AI applications in their region. By comparison, 53% object to construction of a new nuclear energy plant – a perennially unpopular build in the US for decades.
Participants in the telephone survey cited several concerns related to the new data center expansion, primarily focused on resource consumption, cost, and quality-of-life impacts. Fifty percent said excessive resource requirements associated with these builds in areas like water and energy consumption along with secondary effects such as loss of farmland, wildlife, and deforestation are behind their resistance to facilities’ expansion in their areas. Twenty-two cited concerns about property values and increased traffic. Another 20 percent noted that new data centers might bring higher utility costs and cost of living expenses.
Localities are hearing and responding to this resistance to data center expansion. Due to regional complaints, more than $100 billion in facility buildouts was stopped or disrupted in just one quarter. Over 550 local governments have suspended new facility construction or stopped issuing new permits.
Industry observers warn that impeding expansion could have unintended harmful consequences, including hindering the establishment of effective cyber defenses against hostile adversaries and creating barriers to the development and deployment of technological innovations. But cloud providers have been adept at circumventing obstacles to expansion, finding locations in more remote areas that are more hospitable to new facility construction.
Hyperscalers and other cloud providers are targeting more rural areas in the South and Midwest, and more remote locales in states like Oklahoma, Maine, and Virginia. Nearly half of all new data center builds are in the south, with states like Texas being hot spots.
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