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Nvidia is reportedly preparing to invest up to $3 billion in Lancium, a Texas-based company developing large-scale data centers for artificial intelligence workloads.

by Shailja Jha
August 9, 2026
in Tech
Reading Time: 6 mins read
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Weekly Technology News
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Data Center Firm Switch Confidentially Files for U.S. IPO, Bloomberg Reports

Data center operator Switch has confidentially filed for an initial public offering in the United States, Bloomberg News reported, according to people familiar with the matter, in a move that could position the company to capitalize on surging demand for infrastructure supporting artificial intelligence.

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The reported filing comes as data center companies attract growing investor interest amid the rapid expansion of AI applications. Technology companies are spending heavily on computing capacity, driving demand for facilities equipped to support increasingly powerful and energy-intensive workloads.

Switch operates large-scale data center campuses designed to serve hyperscale customers, enterprises and technology companies. Its infrastructure provides the power, cooling, security and connectivity required to operate high-performance computing systems.

Data Center Firm Switch to File Confidentially for IPO - Bloomberg

The company’s potential listing could become one of the most closely watched U.S. IPOs involving digital infrastructure. Earlier reports have indicated that Switch could seek to raise billions of dollars through the offering and potentially command a valuation of tens of billions of dollars, although the size and timing of the deal could change.

A confidential filing allows Switch to work with regulators and prepare for a public offering without immediately disclosing detailed financial information. Companies often use the process to assess market conditions and investor demand before deciding whether to proceed with a listing.

The potential IPO comes at a time when investors are increasingly looking beyond chipmakers and software companies to businesses providing the physical infrastructure needed for the AI boom. Data centers have become strategically important as AI models require vast amounts of computing power, electricity and specialized cooling systems.

If Switch proceeds with the offering, its IPO could provide a significant test of public-market appetite for data center operators and help establish valuation benchmarks for the broader digital infrastructure sector.

The company has not publicly confirmed the reported filing. Details including the offering size, valuation and timing are expected to emerge as the listing process advances.

OpenAI Flags Possible Critical Cybersecurity Risk in Upcoming Model, Tightens Controls

OpenAI has raised concerns over a potentially critical cybersecurity risk linked to one of its upcoming artificial intelligence models, prompting the company to strengthen safeguards before the system is made more widely available.

The warning highlights the growing challenge AI companies face as increasingly capable models become better at performing complex technical tasks, including activities related to cybersecurity. While advanced AI can help defenders identify vulnerabilities and strengthen digital infrastructure, the same capabilities could potentially be misused to discover or exploit weaknesses.

OpenAI has reportedly classified the risk as serious enough to require additional controls around the model’s development and deployment. The company is expected to introduce tighter monitoring, access restrictions and additional security measures designed to prevent the system from being used for harmful cyber activities.

The move reflects a broader shift in how leading AI companies assess powerful models. Rather than evaluating systems solely on their ability to generate text, code or images, developers are increasingly examining whether models could independently carry out sophisticated tasks that previously required significant human expertise.

Cybersecurity has emerged as one of the most sensitive areas. AI models can analyze large amounts of code, identify potential vulnerabilities and assist with debugging and security research. However, similar capabilities could also lower the technical barrier for malicious actors attempting to exploit vulnerable systems.

OpenAI flags possible critical cybersecurity risk in upcoming model, tightens  controls

OpenAI’s decision to tighten controls before the model’s broader release suggests the company is taking a more cautious approach as AI capabilities advance. Restrictions could include limiting access to certain capabilities, increasing automated monitoring and requiring additional safeguards for high-risk applications.

The development also underscores the difficulty of balancing innovation with safety. As AI models become more capable, companies must determine not only what these systems can accomplish but also what could happen if those capabilities are misused.

For OpenAI, the latest warning demonstrates that cybersecurity risks are becoming an increasingly important part of the company’s model-development process. It also signals that future AI releases may come with stronger restrictions when their capabilities create potentially significant real-world risks.

Nvidia to Invest Up to $3 Billion in Stargate Data Center Developer Lancium

Nvidia is reportedly preparing to invest up to $3 billion in Lancium, a Texas-based company developing large-scale data centers for artificial intelligence workloads. The investment would further strengthen Nvidia’s position in the rapidly expanding AI infrastructure market.

Lancium is involved in Stargate, a major AI infrastructure initiative designed to build massive computing facilities capable of supporting next-generation artificial intelligence systems. The project requires enormous amounts of computing power, electricity and specialized infrastructure as demand for advanced AI services continues to grow.

For Nvidia, the potential investment represents a move beyond its traditional role as a supplier of AI chips. The company has become one of the most important players in the AI industry through its graphics processing units and data center technologies. By investing directly in infrastructure developers, Nvidia could gain greater influence over how and where its hardware is deployed.

Nvidia to invest up to $3 billion in Stargate data center developer Lancium,  the Information reports | Reuters

The reported deal also highlights the increasing importance of data centers in the global AI race. Training and running advanced AI models requires massive computing clusters containing thousands of specialized processors. These facilities also need reliable electricity, sophisticated cooling systems and high-speed networking equipment.

Lancium has been developing infrastructure specifically suited to energy-intensive computing operations. Its involvement with Stargate makes it a potentially important partner as technology companies seek to rapidly expand AI capacity across the United States.

The proposed investment comes at a time when Nvidia is benefiting from unprecedented demand for AI computing hardware. Technology companies are spending billions of dollars building infrastructure to support increasingly powerful AI models and applications.

If completed, Nvidia’s investment of up to $3 billion could give the chipmaker a deeper role in the development of Stargate’s physical infrastructure. It would also signal how semiconductor companies are increasingly moving toward partnerships and investments that cover the entire AI computing ecosystem, from processors to the massive facilities required to operate them.

Apple Says Mac Users in China Can Connect to Alibaba’s Qwen AI Service

Apple has said that Mac users in China can connect to Alibaba’s Qwen artificial intelligence service, marking a significant development in the company’s approach to AI in the Chinese market.

The move gives Mac users access to Alibaba’s Qwen AI capabilities, which can assist with tasks such as generating and processing text, answering questions, coding and handling other AI-powered functions. Qwen has become one of China’s leading artificial intelligence platforms as domestic technology companies compete to develop increasingly advanced AI models.

For Apple, the development is particularly important because China remains one of its largest and most important markets. The company has been expanding its artificial intelligence strategy globally, but bringing some AI features to Chinese users has presented additional challenges because of local regulations and requirements surrounding generative AI services.

Apple says Mac users in China can connect to Alibaba's Qwen AI service |  Reuters

Working with a Chinese technology company such as Alibaba could allow Apple to provide AI-powered experiences while adapting its services to the regulatory environment in the country. Rather than relying entirely on its own AI models, Apple can use partnerships with local providers to expand the functionality available to customers.

The development also highlights the growing importance of Chinese AI companies in the global technology industry. Alibaba has invested heavily in Qwen, developing models capable of handling a wide range of tasks and competing with other major AI systems.

For Mac users, the availability of Qwen could provide a convenient way to access advanced AI tools directly through their devices. It could also increase the usefulness of Macs for users who rely on AI for work, research, programming and content creation.

Apple’s approach demonstrates that its AI strategy may vary significantly across different regions. Regulatory requirements, local partnerships and market conditions can influence which AI technologies are available to users.

As competition in artificial intelligence intensifies, Apple’s collaboration with Chinese AI providers could become an increasingly important part of its strategy for maintaining its presence in China while expanding access to new AI-powered experiences.

Tags: a Texas-based company developing large-scale data centers for artificial intelligence workloads.Apple Says Mac Users in China Can Connect to Alibaba’s Qwen AI ServiceBloomberg News reported.Data center operator Switch has confidentially filed for an initial public offering in the United StatesNvidia is reportedly preparing to invest up to $3 billion in LanciumNvidia to Invest Up to $3 Billion in Stargate Data Center Developer LanciumOpenAI Flags Possible Critical Cybersecurity Risk in Upcoming ModeltechnologyTechnology Newstechnology updatesTechstoryTightens Controls
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How Businesses Use Mobile Proxy Servers for Localized Data Collection

by Rohan Mathawan
October 9, 2026
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Engineering teams building market intelligence pipelines frequently deal with rate limits and manipulated pricing data. Target platforms actively manage incoming traffic using security tools like Web Application Firewalls and TLS fingerprinting. Relying on commercial server nodes for data extraction often leads to interrupted sessions. To solve this, businesses are moving their infrastructure toward authentic cellular networks. Using mobile proxy servers allows them to collect accurate, localized web data reliably. TL;DR: Core benefits of mobile proxy servers for data extraction Carrier-grade trust: Mobile networks use CGNAT, masking your automated traffic alongside thousands of real smartphone users, resulting in maximum IP trust scores. Organic IP shifts: Security systems are adapted to mobile networks where IP addresses routinely change across local cell towers. A dynamic IP is completely natural, provided your geographic region and device footprint remain consistent. Dedicated vs. shared hardware: Dedicated modems allow manual IP rotation via API but require a 5 to 10-second pause while the physical hardware reboots. Shared proxies offer fixed-interval rotation without interrupting active users. Maintaining stable access: Premium infrastructures use the VLESS protocol to route proxy connections as standard HTTPS browsing. This helps maintain connections in environments with strict local ISP filtering. Why localized data extraction requires authentic connections Companies extract localized web data to see information exactly as it appears to a regular user in a specific region. Pricing and availability change based on the viewer's location. A consumer searching a travel aggregator from London sees different flight options than a user querying the exact same route from Tokyo. Teams historically built scraping infrastructure on data center IPs. Today, anti-fraud engines maintain strict databases of commercial cloud subnets. When a firewall detects consumer-level requests coming from a corporate server, it ruins the connection's trust score. Real users do not browse from cloud data centers. The target platform simply responds with CAPTCHAs or HTTP 429 errors. How mobile proxy servers maintain stable access To ensure stable data collection, automated traffic needs to match the normal network patterns of genuine human users. Mobile proxy servers accomplish this by routing requests through real 4G LTE or 5G modems equipped with authentic SIM cards. This works through Carrier-Grade NAT (CGNAT). Mobile carriers do not assign a unique public IP to every smartphone. Instead, they push the data streams of thousands of subscribers onto the internet through a single shared IP address. Banning a CGNAT mobile IP means disconnecting hundreds of real retail consumers in that area. To avoid this collateral damage, security engines treat mobile carrier ASNs with extreme leniency, granting them trust scores between 90% and 99%. Infrastructure Type Network Source CGNAT Shielding Average Trust Score Algorithmic WAF Reaction Data Center Commercial Cloud Providers No (1:1 routing) 20% - 40% Immediate block, CAPTCHA, or rate-limit for consumer endpoints. Static Residential Home Internet Providers Rare (Usually 1:1) 70% - 85% Reliable for steady sessions, but flagged during abrupt traffic spikes. Mobile (LTE/5G) Real Cellular Carrier Networks Yes (Thousands to 1) 90% - 99% High tolerance; blocking is mathematically prohibitive due to collateral damage. IP rotation: Why dynamic addresses are natural for mobile proxy servers Many operators believe that keeping a static, unchanging IP address is the only way to maintain a healthy session. They assume an IP rotation during a collection task immediately triggers security algorithms. In reality, the internet is heavily adapted to a mobile-first world. When genuine consumers browse on their smartphones, they commute across a city and experience brief signal drops. These physical events force their hardware to re-authenticate with the network. The mobile carrier then assigns the device a brand-new IP address from the regional pool. Target platforms inherently expect this behavior. Consequently, an IP address changing within the bounds of a specific city or county is not an automatic red flag. What actually triggers anti-fraud systems is inconsistency in the digital footprint. As long as the regional origin and the device identity remain synchronized, the session remains valid. Modern automation relies entirely on consistent device and location profiles rather than rigid IP addresses. Choosing the right mobile proxies: Dedicated vs. Shared hardware When integrating cellular infrastructure into a pipeline, engineering teams must decide between two distinct types of mobile proxies. This choice directly impacts hardware control and session persistence. Dedicated proxies Dedicated mobile proxies provide an operator with exclusive, single-tenant access to a specific physical modem and SIM card. This grants unrestricted bandwidth and absolute programmatic control over the connection. Operators can trigger an IP change precisely when their workflow demands it via an API call integrated directly into their Python or Node.js scripts. Developers need to handle the rotation delay. When the API command executes, the physical modem drops its connection to the local cell tower and renegotiates a new one. This reset takes 5 to 10 seconds. Scripts require deliberate pauses (e.g., time.sleep(10)) during this window. Sending payloads before the modem re-authenticates causes timeout errors. Shared proxies Shared mobile proxies allow multiple independent users to route their traffic through the same physical 5G/LTE modem simultaneously. Because the connection is multiplexed, the IP address on a shared port rotates at a strict, automated interval typically every 5 or 30 minutes. Manual rotation is disabled because resetting the network would drop the connection for everyone sharing the hardware. Shared ports provide cost-effective CGNAT trust for stateless tasks. Shared networks provide excellent, cost-effective CGNAT trust for stateless tasks where session persistence is irrelevant. Handling deep packet inspection with VLESS While mobile proxy servers manage external routing, businesses sometimes face interference from their own local Internet Service Provider (ISP). Some local ISPs use Deep Packet Inspection (DPI) to monitor outbound traffic. Standard proxy protocols have recognizable cryptographic handshakes that DPI systems often restrict. To resolve this, premium mobile infrastructure supports the VLESS protocol. Rather than generating a custom proxy certificate, VLESS borrows the cryptographic signature of a highly trusted, unrelated domain during the TLS handshake. Local DPI tools analyze this outbound data flow and see standard HTTPS web browsing. This allows data teams to work without local network interruptions. Digital identity and environment setup High-trust IP addresses lose their value if a script logs into an account using a flagged VoIP phone number. The same happens if a localized payment uses a card that mismatches the proxy's geographic location. Operators pair dedicated mobile IP connections with clean browser profiles. They use tokenized payment cards matching the proxy's billing region and real residential phone numbers for SMS verifications. Sourcing these components from different providers slows down infrastructure deployment. Platforms like CyberYozh App consolidate these tools. Teams deploy dedicated mobile IPs, issue virtual cards, and rent local ISP numbers from a single ecosystem. Checking the complete setup through a built-in fraud scorer ensures a highly trusted profile before data extraction begins. Real-world application A market intelligence firm monitored retail pricing across European e-commerce platforms using data center proxies. Within days, the target platform recognized the commercial subnets, applied rate limits, and served manipulated HTML. The firm restructured its pipeline. They replaced their data center nodes with CyberYozh dedicated mobile proxies physically located in the target countries. Because the requests originated from genuine 4G/5G mobile gateways shielded by CGNAT, the firewall perceived the traffic as authentic mobile shoppers. The Python script held a single mobile IP for a persistent session, extracting the localized pricing data. Once a geographic sweep was complete, the script fired an API rotation call, paused operations for 10 seconds while the physical modem reset its radio link to the local cell tower, and seamlessly resumed collection on the next batch of URLs. By aligning their geographic footprint and accounting for the physical realities of hardware rotation, the firm restored stable access to their target data. Final thoughts on mobile proxy infrastructure Basic data collection methods struggle against modern traffic analysis. Because international platforms look closely at connection behavior to protect their localized data, relying on commercial server networks often leads to unstable access. Authentic LTE and 5G cellular hardware solves this problem. Carrier-Grade NAT and API rotation align the traffic with natural network behavior. This approach secures high IP quality and enables reliable data extraction at scale. As businesses rely more on location-specific data, the technology behind reliable web access is becoming an important part of modern data collection. For more practical guides on emerging technology, online infrastructure, and digital tools, explore Kemotech.

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