Meta has built a series of what it calls rapid deployment structures at its New Albany, Ohio campus. According to data center tracking firm Cleanview Energy, Meta has built or is constructing six large structures at the Ohio site. Five of the facilities measure approximately 125,000 square feet each.

According to Cleanview, local permit records indicate construction began earlier this year, while satellite imagery suggests the facilities were completed far more quickly than conventional data center buildings.

The speed difference is of course enormous. Traditional structures at the same campus reportedly required between two and three years to complete. By contrast, the new tent-like facilities moved from permitting to deployment within months.

Meta CEO Mark Zuckerberg previously disclosed plans to use weather-resistant structures to accelerate deployment of data center capacity. Similar facilities are now reportedly being developed at a Meta site in Tennessee.

The tent-based facilities reveal the pace of competition among tech companies: data center construction is now arguably as important as advances in AI models.

A Growing Urgency

While the tent-like structures themselves have attracted attention, the larger story is the growing urgency surrounding AI infrastructure. Building AI systems requires vast quantities of graphics processors, networking equipment and electrical power, and demand for those resources has increased so quickly that construction timelines are a competitive disadvantage.

The challenge extends beyond buildings. Meta is also investing in dedicated power generation to support its AI expansion. The Ohio facility is supported by a nearby 200-megawatt power installation using modular gas turbines. The company has also pursued off-grid energy arrangements that allow facilities to operate independently of local utility infrastructure. Such approaches reduce dependence on grid interconnections, which can take years to secure in some regions.

The use of on-site generation is becoming common across the AI sector as operators seek faster deployment schedules. Obtaining electrical capacity has become a real roadblock facing large AI projects.

Cleanview estimates that approximately 2 gigawatts of behind-the-meter data center capacity is currently available in the US, with another gigawatt expected to come online this year. But the pace is quickening: if current projects remain on schedule, total capacity could reach roughly 13 gigawatts by the end of 2027.

The 13-gigawatt figure shows the gargantuan scale of investment now flowing into AI infrastructure. That amount of generating capacity is comparable to the output of multiple large nuclear power facilities and exceeds the electricity consumption of some major cities.

Meta has already indicated that it intends to spend as much as $145 billion on data centers and related capital projects. That level of investment is creating a new phase of AI development in which traditional approaches are being replaced by faster, more flexible alternatives designed to keep pace with escalating demand.

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The accord, disclosed on Friday in an amended regulatory filing, positions the aerospace giant as a sudden power player in the artificial intelligence (AI) hardware leasing market.

Under terms of the agreement, SpaceX will provide Google with approximately 110,000 NVIDIA Corp. graphics processing units (GPUs), alongside central processors, memory, and associated infrastructure. The arrangement features a reduced-rate capacity ramp-up period through September, with full monthly payments kicking in from October 2026 and running through June 2029.

The contract includes strict performance clauses. If SpaceX fails to deliver the committed GPU access by Sept. 30, 2026, Google retains the option to either terminate the contract immediately after a one-month grace period or accept fewer GPUs at a reduced fee. After Dec. 31, 2026, either company can exit the deal with 90 days’ written notice. Google will retain all ownership and intellectual property rights for its content, AI models, and data.

A Google Cloud spokesperson confirmed the arrangement to CNBC, characterizing it as a temporary measure to accommodate unprecedented enterprise growth.

“This is a short-term, timely agreement to ensure we have bridge capacity to meet surging customer demand for our agent platform, Gemini Enterprise, which has been even higher than we expected,” the spokesperson said.

By pivoting to lease its vast data centers, SpaceX is successfully monetizing the workflows originally built for Grok. The financial boost is expected to strengthen SpaceX’s narrative as it seeks a public market valuation exceeding $1.75 trillion in next week’s offering.

“Securing AI compute has shifted from building to renting, with even the largest infrastructure owners leasing third-party GPUs to absorb demand they cannot provision in time,” said Mitch Ashley, vice president and practice lead for Software Lifecycle Engineering and AI-Native Software Engineering at The Futurum Group. “Accelerator access is becoming a commodity sourced from outside the hyperscaler buildout. Capacity procurement is now a recurring decision, and the contract terms prove it: ramped pricing, delivery deadlines, and short exit windows. Buyers must manage compute as a hedged supply line, pricing delivery risk and optionality into every commitment.”

The transaction marks a stark role reversal from five years ago, when Google supplied networking resources to support SpaceX’s Starlink satellite internet.

The Google contract represents SpaceX’s second massive infrastructure deal since its February merger with xAI, Elon Musk’s AI venture, which valued the combined entity at $1.25 trillion. In May, Anthropic signed a separate three-year, $45 billion deal to utilize computing power at SpaceX’s Colossus data centers in Memphis, Tenn. Combined, the Google and Anthropic agreements are projected to generate roughly $26 billion annually, totaling over $70 billion across their lifespans.

The massive revenue stream arrives at a critical juncture for Musk. SpaceX’s prospectus revealed that its AI segment recorded a heavy $2.5 billion operating loss in the first quarter on just $818 million in revenue, driven by a whopping $7.7 billion capital expenditure investment in AI infrastructure.

Furthermore, xAI’s flagship chatbot, Grok, has struggled to gain market share while facing intense scrutiny and government probes over deepfake controversies.

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Overall, the trip was a vivid illustration of Huang’s strategy of evolving NVIDIA from a top chip supplier to a vendor serving many aspects of the AI sector. For South Korea, the trip was an acknowledgment that it is already a leading supplier of AI infrastructure.

The most significant announcement was a multi-year agreement with SK hynix to jointly develop future memory technologies and secure long-term supply for NVIDIA’s upcoming AI platforms. The partnership covers future memory products intended for NVIDIA platforms, including Vera Rubin systems, RTX Spark PCs and Jetson Thor robotics platforms. The agreement offers NVIDIA greater visibility into future memory availability while providing SK hynix predictable demand as development cycles and manufacturing costs continue to rise.

“This partnership reinforces the necessity of co-design in the HBM [high bandwidth memory] supply chain,” Brendan Burke, Research Director at Futurum, told Techstrong.it. “As HBM capacity becomes the primary constraint on GPU scaling, Nvidia’s move to lock in SK Hynix for next-gen products signals a shift toward more integrated chip designs. This reduces supply risk while ensuring their roadmap for training and inference clusters keeps pace with the compute requirements of the next generation of LLMs.”

The agreement also expands beyond memory production. SK hynix plans to use NVIDIA software platforms including CUDA-X, PhysicsNeMo and Omniverse to accelerate chip development and factory operations. The company is creating digital twins of semiconductor manufacturing facilities and exploring AI-driven optimization of production processes and autonomous factory equipment.

“NVIDIA has relied on SK Hynix as its main memory provider and this agreement appears to extend this key relationship,” Gil Luria, Head of Technology Research at DA Davidson, told Techstrong.it. “While NVIDIA is also likely to incorporate memory from Micron and Samsung as well, it is likely to keep the biggest portion of its purchasing from SK Hynix.”

Agreements Across Sectors

While the SK hynix partnership addresses a key supply-chain challenge, NVIDIA’s other Seoul announcements focused on expanding AI infrastructure across South Korea.

The company unveiled a major collaboration with NAVER to expand sovereign AI capabilities. NAVER plans to increase AI infrastructure capacity beginning with a 55-megawatt deployment and ultimately scale toward gigawatt-class AI facilities. The project will use NVIDIA’s DSX AI factory platform and expand operations at NAVER’s GAK Sejong data center.

NAVER also plans to use NVIDIA technologies to advance future versions of its HyperCLOVA X models (which is NAVER’s flagship LLM that is similar to ChatGPT), support agent-based AI services and develop a Seoul World Model built from local spatial and street-view data.

The partnership demonstrates growing interest among governments and enterprises in sovereign AI infrastructure that allows organizations to operate AI services while maintaining strict control of data and regulatory compliance.

NVIDIA also expanded its relationship with LG Group, targeting robotics, mobility and AI data centers. LG Uplus plans to build a large-scale AI data center designed to support NVIDIA GPU infrastructure. The deployment is expected to provide computing resources for AI model training and deployment across LG businesses spanning electronics, automotive systems, cloud services and robotics.

The companies are also exploring future AI data center architectures, including cooling systems and facility design. As AI workloads continue to devour ever more energy, these areas are big differentiators for both hyperscale and enterprise operators.

Physical AI was a key theme of Huang’s visit. NVIDIA announced expanded cooperation with LG and Doosan Group around robotics and industrial automation. Doosan Robotics plans to integrate NVIDIA technologies including Isaac Sim, Isaac Lab, Cosmos and Jetson Thor into its robotic platforms. The companies are evaluating applications ranging from industrial automation to humanoid robotics.

Doosan Enerbility is also exploring how its power-generation technologies, including gas turbines, fuel cells and small modular reactors, could support future AI data centers.

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Google has signed a three-year agreement with distributed energy platform Voltus to develop a 100-megawatt virtual power plant across the PJM Interconnection region, an agreement that enables access to grid capacity without waiting for new power plants or transmission infrastructure.

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However, CIOs have realized that custom connectivity solutions better fit their mission-critical needs in terms of availability, cost, and ongoing management. And no surprise, legacy systems are being replaced: food delivery operators rely on tablets to stand up new locations quickly without the IT overhead, cardiac diagnostics clients now deploy handhelds to provide easy, continuous monitoring to patients post-surgery, and logistics managers have come to depend on mobile scanners to maintain real-time accuracy across globally distributed port inventory. 

As a result, that calculus has fundamentally shifted to custom solutions. While commodity, off-the-shelf (OTS) devices continue to appear cost-effective on a balance sheet, they carry a “massive hidden tax” that is increasingly difficult to ignore. For the global enterprise, this tax erodes ROI by tens of millions of dollars, stemming from market fragmentation, supply chain opacity, and inadequate product lifespans. Today’s CIO must recognize that the competitive edge no longer belongs to those who buy off-the-shelf, but to those who treat wireless connectivity as a custom-engineered asset.

The TCO Flip: Exposing the “Sticker Price” Fallacy

The primary allure of commodity hardware is the low initial capital expenditure (CAPEX) required to acquire these devices. Theoretically, all necessary design work is complete and vetted. Yet, for the enterprise, the sticker price is a deceptive metric that ignores the longer-tail realities of the hardware lifecycle.

When you deploy ten thousand devices across a global footprint, the unforeseen costs of consumer-grade hardware aggregate with terrifying speed. We are seeing a “TCO Flip,” where the operational savings of a unified, durable platform eclipse the higher initial investment of a custom solution. 

With consumer-device technology, the actual cost to the enterprise lies not in the hardware but in the operational costs of global deployment. A worldwide implementation of enterprise mobility comprises internal and external teams responsible for validating everything from engineering and quality to packaging, regulatory certifications, compliance recertifications, and continued support. As one medical device company told me, “ … we spend over $30M USD annually validating products globally, internally, and with regulators, but only spend $8M annually on hardware.” 

Consider the following two most significant issues faced by consumer devices deployed en masse within an enterprise:

The Myth of Repairability at Scale – consumer devices are increasingly designed as “black boxes” or sealed units with glued components that are nearly impossible to service. In an enterprise setting, this leads to multiple “rip and replace” cycles. When a battery fails or a screen cracks in a specialized industrial environment, the lack of a modular repair path results in the entire unit being decommissioned. A custom solution, conversely, can be designed with enterprise-grade repairability in mind, allowing for component-level swaps that extend the asset’s life by years.

Geographic Tax and Regulatory Friction – one of the most significant “hidden taxes” is the cost of adapting fragmented OTS hardware to diverse global markets. Regional regulatory hurdles, ranging from the FCC in the US to the European Commission in Europe and various certifications across APAC, often require different SKUs of the same “standard” consumer device.

For the CIO, this triggers a cascade of inefficiencies:

1. SKU Sprawl: Managing a “mixed fleet” of region-specific devices creates massive “shadow IT” overhead.
2. Duplicative R&D: Internal teams waste months testing and certifying different versions of the same application for slightly different hardware iterations.
3. Inventory Bloat: Organizations must maintain separate stockpiles of replacement parts for each regional variant, which inflates storage costs and complicates logistics.

Custom solutions mitigate this by allowing for a unified, “global-ready” platform. By architecting the hardware to be compliant across multiple jurisdictions from day one, enterprises can achieve a level of supply chain harmony that commodity hardware cannot.

What’s more, a unified custom platform offers enterprises near-unlimited scalability to enter new markets quickly. This saves enterprises months, if not years, of time, making the savings for custom device TCO astronomical. That kind of competitive advantage cannot be matched by a consumer device you can get at “Best Buy.” 

The 5G-Advanced Inflection Point

Beyond the ROI of customer vs. off-the-shelf devices, enterprise connectivity has reached a technical crossroads where “one-size-fits-all” hardware can no longer keep pace with enterprise ambition. With the arrival of 5G-Advanced, the capabilities of wireless networks have moved far beyond simple “fast internet.”

Custom solutions allow CIOs to bake mission-critical functionality directly into their hardware, rather than relying on an OTS device to handle the load. Two key features illustrate this shift:

• Network Slicing: In a smart factory or a hospital, not all data is created equal. Custom hardware allows an enterprise to programmatically “slice” the network, ensuring that life-critical medical monitors or Autonomous Mobile Robots (AMRs) have guaranteed, dedicated bandwidth, while guest Wi-Fi or administrative traffic takes a back seat.

Deterministic Performance: Unlike consumer applications, where a “re-buffering” icon is a minor annoyance, industrial automation requires deterministic latency. Custom-engineered wireless assets deliver sub-millisecond precision, ensuring that a robotic arm on an assembly line receives its “stop” command exactly when expected, every time.

Beyond Terrestrial Limits: NTNs and 5G Sidelink

As we look toward the horizon, the definition of “wireless” is expanding beyond the reach of traditional cell towers. The emerging role of Non-Terrestrial Networks (NTNs), including satellites and high-altitude platforms, as well as 5G Sidelink (device-to-device communication), is becoming a requirement for public safety and industrial V2X (Vehicle-to-Everything) applications.

Consumer devices are rarely equipped to handle these specialized frequencies or peer-to-peer communication protocols out of the box. A logistics company operating in remote mining sites or a utility provider managing thousands of miles of rural infrastructure cannot wait for the consumer market to catch up.

Custom-built hardware allows these enterprises to integrate NTN capabilities today. This ensures that a field worker is never “off the grid” and that autonomous vehicles can communicate directly with one another, even in the absence of a cellular base station, drastically reducing the risk of collisions and operational bottlenecks.

The Strategic Mandate: From Procurement to Architecture

This transition from “buying” to “architecting” is more than a change in procurement strategy; it is a fundamental shift in how the CIO adds value to the organization.

Historically, the hurdle to custom hardware was the cost of producing custom units in volume. Large Original Design Manufacturers (ODMs) were only interested in orders of millions of units. Today, a new class of custom solution vendors has emerged. These partners provide the transparency, supply chain visibility, and agility that the enterprise requires. They can handle “medium” volumes (tens of thousands of units) that are massive for a single enterprise but ignored by consumer giants.

The shift to custom wireless provides three immediate strategic advantages:

1. Supply Chain Sovereignty: By owning the design and the relationship with the component manufacturers, CIOs gain visibility into exactly where their hardware is coming from, mitigating the risks of geopolitically motivated supply chain disruptions.
2. Extended Product Lifecycles: Custom solutions can be “future-proofed” with modular components, enabling 7–10 years of service and dramatically reducing the annual depreciation cost of the hardware.
3. Security by Design: When you build the device, you control the root of trust. You can eliminate “bloatware,” harden the firmware, and ensure that the device’s hardware-level security matches your enterprise’s rigorous standards.

Conclusion

In the previous decade, wireless was a commodity. In the next decade, it will be the primary differentiator of the efficient, AI-enabled enterprise. The “hidden tax” of commodity hardware – the repair costs, the regulatory delays, the data staleness, and the lack of longevity – is no longer just a line item on a budget; it is a barrier to innovation.

For the CIO, the message is clear: The most expensive solution you can buy is the one with the lowest sticker price that fails to meet your mission-critical needs. The time has come to stop adapting your business to fit the limitations of off-the-shelf devices and start building the connectivity solutions that your future demands.

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A recent nationwide Compass Poll conducted by Embold Research suggests that concerns over electricity costs are fueling much of the opposition. In contrast, the survey suggests a potential path forward: data centers that generate a substantial portion of their own power through on-site renewable energy are viewed more favorably.

The poll, conducted in May among 2,244 registered voters, found that opposition to local data center development has climbed dramatically over just a few months. In December 2025, 52% of voters opposed a data center being built in their area. By May 2026, that figure had increased to 70%, while support fell to 21%. Offering even more resistance to the tech industry, the rise in strong opposition grew from 36% to 58% during the same period.

Electricity costs are driving voter anxiety. Nearly three-quarters of respondents said they believe a nearby data center would increase local power prices, with 59% expecting electricity bills to rise markedly. Only a small minority believed a data center would have little effect on energy costs.

Differing Views on Renewables

Even among the largely negative views, the Embold survey found that attitudes become more favorable when data centers generate a substantial share of their own electricity on-site. Approximately four in ten respondents said they would be more likely to support a local facility if it produced its own power rather than relying entirely on the electrical grid.

Support grew further when respondents were told the on-site generation would come from renewable sources such as solar or wind power. Among Democrats, the share who said they would be more likely to support a local data center increased from 33% when the energy source was unspecified to 52% when renewable energy was identified. Independents showed a similar shift, rising from 13% to 23%.

Republicans moved in the opposite direction, with 48% saying they were more likely to support a data center with unspecified on-site generation, compared with 32% when the generation was specifically described as renewable.

Renewable generation also eased concerns about utility bills. When voters were asked to consider a data center powered partly by on-site renewable energy, the share expecting electricity prices to increase fell from 73% to 46%. The percentage who believed prices would rise “a lot” dropped from 59% to 23%. Meanwhile, the share of respondents who expected little or no impact on electricity prices increased from 12% to 33%.

Oppose Nearby Data Center

Opposition to data centers is becoming a broader national issue. Separate polling conducted by Heatmap Pro and Embold Research found that 71% of Americans oppose a data center being built near where they live. The survey also found resistance cutting across political affiliations, age groups, and geographic regions.

This resistance is translating into real world results. According to Heatmap Pro data, at least 20 proposed data center projects were canceled during the first quarter of 2026 following public backlash, eliminating more than $41 billion in planned investment and several gigawatts of anticipated electricity demand.

Additional research from Gallup points to environmental concerns as another major driver of opposition to data centers. Respondents cited worries about electricity consumption, water usage, pollution, and quality-of-life impacts. Gallup found that 71% of Americans opposed local AI data center construction, compared with 53% who opposed a nuclear power plant being built in their area.

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Supabase

Round: $500M Series F (post-money valuation $10.5B) | Sector: Cloud / Database Infrastructure

Supabase has raised $500 million in a Series F led by GIC at a $10.5 billion post-money valuation, with Stripe doubling down and Salesforce Ventures joining the cap table. The open-source Postgres platform is explicitly positioning the new capital around agentic infrastructure, a recognition that AI applications are quickly becoming the dominant consumer of managed database services. With total funding now past $1 billion, Supabase is no longer the scrappy alternative to hyperscaler databases. It is becoming a primary destination for them.

DriveNets

Round: $410M Series D | Sector: Networking

DriveNets closed a $410 million Series D led by Bessemer Venture Partners and Atreides Management, with AMD joining the round as a new strategic investor. The Israeli networking software company builds Ethernet fabric for large-scale AI clusters and has been cash-flow positive since 2025, with over $1 billion in secured business. AMD’s presence on the cap table signals where this category is headed: heterogeneous AI infrastructure that does not assume a single accelerator vendor.

Cyera

Round: $300M Series G | Sector: Cloud Data Security

Cyera raised $300 million in a Series G led by Evolution Equity Partners, with Sequoia, Greenoaks, Lightspeed, Coatue, Accel and Sapphire Ventures all returning. The data security posture management company now sits at roughly $1.9 billion in total equity raised, a valuation profile that reflects how much enterprise spend is moving toward classifying and protecting the unstructured data feeding AI systems. For CISOs, the round is a reminder that the data layer is becoming the most expensive part of the security stack.

Quobly

Round: €115M Series A (approximately $133M) | Sector: Semiconductors / Quantum Computing

French quantum startup Quobly secured €115 million in a Series A led by Bpifrance, STMicroelectronics and SEALSQ, with Air Liquide’s ALIAD also participating. The company is building silicon-based quantum processors using standard CMOS manufacturing, an approach designed to make quantum hardware scalable inside existing semiconductor fabs. For enterprise IT planners, the round is a useful marker on the industrialization curve, even if commercial deployment remains several years out.

Folio Photonics

Round: $8M Series A | Sector: Data Storage

Folio Photonics closed an $8 million Series A co-led by Material Impact and The O.H.I.O. Fund to scale its multi-layer optical disk archival technology, with target media costs starting around $3 per terabyte. The Ohio-based startup is actively engaged with hyperscaler partners as it transitions from R&D into productization. Cold storage economics rarely make headlines, but optical archive is one of the few pathways that can absorb the long-tail data growth AI workloads are generating without rewriting cloud cost models.

The Weekly Funding Pulse tracks the most significant IT infrastructure, cloud, semiconductor, networking, and ITSM funding rounds each week. Coverage is curated for enterprise IT professionals and decision-makers.

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Three Amazon engineers took their criticism of the company’s AI strategy directly to Seattle policymakers this week, arguing that a massive expansion of data centers is occurring at the same time thousands of Amazon employees are losing their jobs.

The employees appeared before Seattle City Council committees as city officials considered a one-year pause on new AI data center projects. Echoing pushback across the US, their point was that tech companies are pouring extraordinary sums into AI computing capacity while communities and workers absorb the consequences.

The engineers pointed to Amazon’s plan to spend roughly $200 billion on capital projects this year, with much of that investment tied to AI infrastructure and data centers. At the same time, Amazon has eliminated more than 30,000 corporate positions since October as CEO Andy Jassy continues to reduce management layers and streamline operations.

The workers argued that local governments should have a greater role in determining how AI infrastructure is developed, particularly as data centers consume increasing amounts of electricity and water.

Amazon Employees for Climate Justice

The engineers are members of Amazon Employees for Climate Justice, a group that has repeatedly challenged the company’s environmental and AI-related policies. During the hearings, they called for stronger oversight of data center projects, including greater transparency around ownership structures, reporting requirements for energy and water consumption, and stronger commitments to renewable power.

Among the ideas raised by the engineers was a requirement that data center developers provide more renewable energy resources than their facilities consume. They also called for greater transparency around the companies behind proposed projects and their long-term impact on local infrastructure.

Seattle is now considering how to manage growing interest from developers seeking to build AI-focused facilities in the region. City officials recently advanced a proposal that would temporarily halt permits for new data centers while regulators develop a framework governing future projects. The measure was approved unanimously by the City Council’s Land Use and Sustainability Committee.

The debate emerged after four data center developers approached Seattle’s utility provider with proposals involving five major data center facilities. Public opposition has already altered those plans, with two developers reportedly withdrawing their proposals.

Seattle is far from alone in confronting these questions. Across the US, state and local governments are examining the impact of data center growth, and in some cases working to create guidelines. The push for regulation comes as tech companies engage in what is likely the largest infrastructure spending cycle in industry history.

Amazon, Microsoft, Alphabet and Meta are collectively committing hundreds of billions of dollars to AI-related capital investments. Microsoft alone is expected to spend approximately $190 billion this year as demand for AI services continues to accelerate.

For critics, the issue extends beyond environmental concerns. Some employees view the simultaneous expansion of AI data centers and reduction of corporate workforces as evidence of an unfortunate shift in priorities inside major tech companies.

Amazon responded by stating that employees are free to express their views and emphasized that the company currently has no plans to build data centers within Seattle city limits. The company also pointed to its ongoing efforts to improve energy efficiency, increase the use of carbon-free power sources, and achieve its goal of becoming water positive by 2030.

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Who Let the Dogs Out?

We frequently see vendors pitch edge hardware that looks great on a data sheet but falters in unpredictable environments. Fortinet addressed this practical reality by partnering with Parsec Technologies to develop the Bloodhound and Pitbull emergency connectivity kits. The key engineering choice here is the elimination of external antenna deployment. Both kits feature antenna elements built directly into the lid and sides of the rugged carrying cases. In practice, this means establishing a secure network requires nothing more than opening the case and flipping a power switch. This design shift significantly reduces deployment failure points, especially when the person on site is a stressed first responder rather than a network engineer.

The smaller of the two units, the Bloodhound, is engineered for scenarios where local cellular infrastructure remains intact but fixed line connectivity is dead. Inside the case sits a FortiExtender Vehicle, a ruggedized gateway device originally designed to handle the extended temperature ranges and vibrations of automotive deployments. The Bloodhound relies on a built-in 5G LTE radio to establish a secure connection back to a remote cloud or a centralized corporate firewall. Because cellular radios require less power than satellite transceivers, the internal battery pack can sustain operations for approximately 40 hours. This capability makes it an effective tool for temporary voting locations, pop-up medical clinics, or branch offices experiencing a localized fiber cut.

When local infrastructure is completely wiped out by a hurricane or an earthquake, the requirements change drastically. This is where the larger, 70-pound Pitbull kit comes into play. The Pitbull handles true disaster recovery by integrating a Starlink satellite connection directly into the lid of the case alongside dual 5G radios that support major North American carriers, including AT&T FirstNet. Instead of acting as a simple bridge to a remote cloud, the Pitbull houses a full FortiGate Rugged 70G Next Generation Firewall. This local hardware allows the kit to process security policies, manage a built-in Wi-Fi 7 access point, and run the FortiOS policy engine right at the edge. The trade-off for this massive compute and satellite capability is power consumption, as the Pitbull delivers about eight hours of battery life when the Starlink connection is continuously active.

Logistics often represent the hidden failure point of disaster recovery planning. Emergency gear is useless if you can’t get it to the disaster zone quickly. Fortinet designed both kits to be TSA compliant for air travel, though the Pitbull requires a bit of physical effort due to its weight. To check the heavy case as standard luggage, a technician must remove the internal battery pack using a single screw. The main case can then go into the cargo hold while the batteries are carried onto the aircraft. It is a grounded, sensible solution to a mundane regulatory hurdle that would otherwise stall an emergency deployment.

Bringing It All Together

The value of these kits is not found in proprietary breakthrough components, but rather in the clever packaging of existing enterprise technology. The FortiExtender Vehicle and the FortiGate Rugged 70G are standard catalog items that network administrators already know how to configure and manage. By embedding these devices into specialized, antenna integrated enclosures, Fortinet allows organizations to extend their existing security policies to the edge of a disaster zone without administrative friction. Security should not be compromised just because a network is running on a battery in a parking lot. These rugged kits ensure that even in the worst operational conditions, the corporate security perimeter remains intact.

To learn more about Fortinet and their rugged disaster recovery solutions, make sure you check out their website at https://Fortinet.com. To see the entire Fortinet presentation from Mobility Field Day, make sure to check out their presentation appearance page here.

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The core issue is that cellular protocols were built for carriers who care about billing subscribers, not verifying endpoints. Pull a SIM from an authorized security camera, drop it into a rogue laptop, and the cellular core authenticates it without complaint. IMEI works like a MAC address, which means an attacker can clone the identity of a smart meter or industrial sensor and slip past standard network locks. We recently had a chance to check out one exciting new solution to this problem during Mobility Field Day 14.

Building Bridges

OneLayer is trying to solve this problem by bringing mobile and IT together. OneLayer Bridge does this by sitting between the cellular core and existing enterprise security tools. It integrates with Nokia and Ericsson cores and converts raw signaling data into IT-readable context. Instead of trusting identity strings that can be faked, it looks at what a device’s hardware actually does when connecting.

When an endpoint attaches to the network, its modem sends control requests that expose its true hardware capabilities. Those profiles are hard coded into the physical chip. Spoofing software can’t touch them. OneLayer checks the attach request against a device database in real time. If a device claims via IMEI to be a low-bandwidth smart meter but its radio behavior looks like a laptop, that mismatch gets flagged immediately.

To keep rogue devices off production systems, new connections land in a staging APN first. This staging APN is a quarantine environment inside the cellular core, isolated from the rest of the network. OneLayer runs hardware fingerprinting there and checks device posture against management databases or MDM tools. Once the hardware profile matches the expected identity, the platform triggers a reattachment and moves the device into the correct production APN.

Gateway to Security

The platform also looks past the cellular modem. In industrial environments, one router typically serves as a gateway for PLCs, IP cameras, and tablets. These are devices the cellular core never sees. OneLayer queries the router during staging via SNMP, NetConf, and SSH, pulling MAC and IP addresses for downstream equipment to build out the full picture.

That data flows directly into tools security teams already use like Palo Alto firewalls and ServiceNow CMDBs. When a validated device joins the network, OneLayer updates Dynamic Address Groups in the firewall. Security teams write identity-based policies for individual cellular devices instead of managing broad, high-risk subnets. When something suspicious turns up, such as a SIM swap, or an unexpected hardware change, the platform updates firewall rules to quarantine the device automatically.

Bringing IT All Together

A valid SIM is not a trusted device. Treating it as one leaves critical infrastructure exposed to anyone willing to swap a chip when no one is looking. Checking hardware behavior that can’t be spoofed, and holding devices in isolation until they pass, is how private cellular networks stop being a security black box.

To learn more about OneLayer Bridge and how it can help secure your cellular and mobile assets, make sure to check out their website at https://onelayer.com. To see their entire presentation from Mobility Field Day, head over to the presentation appearance page here.

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