The order requires the Public Utility Commission of Texas (PUCT) and the Electric Reliability Council of Texas (ERCOT) to conduct a comprehensive review of every data center seeking interconnection to the ERCOT grid. Projects that fail to meet the state’s new verification requirements could be denied access to the grid.

Texas has become a preferred destination for hyperscale data centers because of its abundant land, competitive energy costs, tax incentives and light regulatory environment. Those advantages have helped the state to challenge Virginia as the nation’s largest data center market. But the explosion in AI development has also driven unprecedented growth in projected electricity demand.

ERCOT’s interconnection queue currently contains more than 1,800 projects with approximately 474 gigawatts of requested capacity, according to the governor’s office. That total exceeds five times the state’s record peak electricity demand, with data centers accounting for roughly 90 percent of the proposed new load.

Regulators Collecting Detailed Information

Regulators have been instructed to collect detailed information on each project’s projected annual and peak power demand, reliance on the ERCOT grid versus on-site generation, expected water consumption, cooling systems, ownership structure and financial incentives received from state or local governments.

Developers must also outline measures intended to reduce impacts on surrounding communities, including noise, lighting, traffic and emergency response planning.

Legislators have expressed frustration that relatively few developers participated in previous state surveys designed to gather data on electricity and water usage, limiting regulators’ ability to assess the cumulative impact of large AI facilities.

Water has become a high-profile issue as data center construction accelerates. While cooling systems consume significant amounts of water, there are also indirect water demands associated with electricity generation. Several Texas communities already face persistent drought conditions, heightening concern over projects that require major water resources.

The pause applies only to projects seeking connection through ERCOT. Developers pursuing behind-the-meter power generation can continue moving forward. That approach has become common among hyperscale AI operators, with companies deploying dedicated natural gas generation rather than waiting for traditional grid connections.

Some projects have turned to combined-cycle gas turbines, while others are evaluating aeroderivative turbines or temporary mobile gas generators to speed up deployment.

The governor’s order has delayed ERCOT’s Batch Zero review process, the first stage of the state’s revised large-load interconnection framework. The postponement could affect project schedules and financing decisions while developers consider the additional compliance requirements.

Polling has shown many Texas residents oppose data center construction in their communities, citing concerns over electricity costs, water availability and local environmental impacts. Several local governments have attempted to restrict data center development through zoning changes or temporary moratoriums, although some efforts have faced legal challenges.

Despite the pause, some experts say the audit could ultimately benefit responsible developers by distinguishing viable projects from speculative proposals that have inflated interconnection requests.

The post Texas Pauses Data Center Grid Approvals Amid Mounting Power Concerns appeared first on Techstrong IT.

That migration was not a global search and replace. Authentication models changed. Permission scopes changed. Default property sets and filtering behavior changed. Some cmdlets mapped cleanly; others required a different implementation. For teams running identity and Microsoft 365 operations through PowerShell, this meant discovering dependencies, rewriting code, validating permissions and retesting production workflows.

Microsoft gave customers unusually extensive preparation. The company had been signaling the move away from the legacy modules since 2021. Before retirement, it ran temporary outage tests between January and March 2025, allowing tenants to observe failures before the final shutdown. Microsoft also released a tenant-specific Entra recommendation showing legacy module activity, including the operations used and when they had last run.

That report exposed a problem larger than the module retirement. If Microsoft’s telemetry could identify your organization’s automation dependencies more reliably than your own records, the migration problem was not just code compatibility. It was the absence of a dependable automation inventory.

For organizations in that position, years of notice could still end in an urgent discovery and rewrite effort. The lesson is not that DIY scripting was a mistake. It’s that business-critical automation had become an internal product without being managed as one.

The Vendor You Didn’t Mean to Become

A useful distinction is crossed when scripts stop being personal tools and become part of service delivery. Onboarding depends on them. Offboarding depends on them. Group membership, mailbox administration, access requests, compliance evidence and recurring maintenance may all pass through them. At that point, IT operations is no longer merely authoring scripts. It is operating software on behalf of users.

Look at what the team now owns:

• A Product: The automation portfolio and the outcomes it delivers
• Users: Employees, service-desk agents, auditors, application owners and other teams that rely on those outcomes
• Service Expectations: Requests continue to be fulfilled at an established speed and level of reliability
• A Security Boundary: Credentials, permissions, execution hosts, service accounts and administrative interfaces
• A Maintenance Obligation: Modules, APIs, authentication methods and target systems continue to change

None of these responsibilities disappears because the product was assembled incrementally. A workflow written to solve one ticket category may have been entirely rational. So was the next one. The operating model emerges from the aggregate: Dozens or hundreds of individually reasonable automations become a de facto service platform that the business now relies on.

What often does not emerge with it are the capabilities a deliberate software provider would build around that service: Named ownership, release management, dependency tracking, test environments, operational support, life cycle policy and a funded plan for maintenance. Their absence creates future costs that are difficult to see and plan for. This is the trap of the accidental software vendor.

Scaled Automation Reduces Work, but Someone Must Own Maintenance

Automation can remove large amounts of manual operational work. That is one of its central strengths. However, automation does not eliminate the need to own and maintain the system that performs the work.

Google’s site-reliability engineering literature draws a useful distinction here. It defines toil as work that is manual, repetitive, automatable, tactical and liable to scale with the service. Writing durable automation can be engineering because it produces a lasting improvement. Manually running a script for every request can still be toil.

The same distinction applies to maintenance. Updating a workflow deliberately, with tests and a release process, is engineering. Rediscovering undocumented dependencies after a vendor cutoff, then rewriting them under deadline pressure, is reactive operational work. The problem is not that scripts require maintenance; all software does. The problem is maintenance without inventory, ownership, capacity or a predictable process.

A useful cost model for automation therefore includes more than the time required to write the first version. Each production workflow also creates recurring obligations around:

• Dependency monitoring
• Authentication and credential changes
• Permission reviews
• Testing and release management
• Execution logging and incident investigation
• Documentation and succession
• Retirement when the workflow is no longer justified

A five-line script may remain a five-line script. Its operational footprint can still be much larger.

The Platform Functions Someone Has to Provide

Once automation becomes part of service delivery, several platform functions have to exist somewhere. An organization can build them internally, buy them or combine the two. Ignoring them does not remove the requirement; it simply leaves the requirement informal.

Know What You Run and Who Owns It

When a module is retired or an API changes, that’s a bad time to be asking, “Where might we still be using this?”

Avoiding this scenario requires two things. First, the organization must discover the automation already spread across repositories, servers, task schedulers and individual teams. Then it needs a reliable way to keep the resulting inventory current.

For each production workflow, the team should be able to identify its owner, execution environment, identities, permissions, major dependencies, operational status and business purpose. A central automation platform can help maintain that technical record for the workflows managed through it, connecting scripts with their targets, credentials, schedules, access controls and execution history.

Without that visibility, every deprecation begins as a search exercise. With it, the team can identify affected workflows, involve the right owners, assess the risk and plan the change before the deadline becomes an incident.

Controlled Execution and Evidence

Consider an approved service request waiting for an administrator. The service desk may manage the request correctly, but a human still reads the ticket, opens an administrative console and re-enters the context. The integration layer between the request system and the infrastructure is a person.

That arrangement also separates approval from execution. A ticket can show who requested and approved a change. It does not necessarily show which commands ran, under which identity, against which objects or with what result. For audit and incident response, execution evidence matters as much as approval evidence.

A governed execution layer connects the two. It can constrain inputs, apply policy, run a known version of a workflow and record the result without requiring every operator to hold the underlying administrative rights.

Privilege Boundaries

Ungoverned automation often inherits permissions from the people and machines around it. Administrators receive standing rights because a recurring task needs to run. Scripts contain credentials or depend on broadly privileged service accounts. Execution hosts become sensitive infrastructure without being treated as such.

That matters because attackers increasingly exploit legitimate identity and administrative processes rather than introducing conspicuous tooling. In its 2025 incident-response report, Unit 42 described one case in which an attacker progressed from initial access to domain administrator in under 40 minutes using built-in tools and social pretexts. The relevant question is not whether PowerShell is safe or unsafe. It is whether execution is bounded, attributable and designed around least privilege.

A mature automation model separates the ability to request an action from the privilege required to perform it. It centralizes secrets or avoids them through managed identities, restricts who can run which workflows and leaves an execution record.

Release Management and Continuity

Every automation portfolio has dependencies, even when nobody has documented them. Modules change. APIs are versioned. Authentication libraries are retired. Target-system behavior changes. A production workflow therefore needs some combination of version control, testing, change review, rollback and maintenance ownership.

It also needs to survive personnel changes. When one administrator understands the cross-system workflows and nobody else can safely modify them, that person is part of the runtime architecture. Their departure does not merely create a staffing gap; it can leave business-critical software without a maintainer.

Microsoft gave customers years to prepare. A departing workflow owner may give you only two weeks. A sustainable automation model must be able to survive either scenario.

Build, Buy or Standardize Deliberately

A managed automation platform is not the only way to address these requirements. A sufficiently mature organization can build an internal platform around source control, CI/CD, orchestration, secrets management, role-based access, logging, testing and support. For some teams, this is worth serious consideration.

However, that is a platform-engineering program, not simply a larger collection of scripts. It requires product ownership, technical standards, maintenance capacity and an explicit service model. In this case, the organization is choosing to remain the platform vendor and funding the role accordingly.

A commercial platform changes the allocation of responsibility. Depending on the product, the vendor may provide the execution environment, access controls, workflow publishing, audit trails, scheduling, integrations, version support and an upgrade path. The customer may still own process design, testing, policy decisions and vendor governance. Buying a platform does not eliminate all work and risk. It can, however, turn a set of internal capabilities into supported product functions that someone else maintains.

Working with a software vendor also gives the team access to support informed by problems encountered across many customer environments. When a workflow fails or a platform change creates an unexpected issue, the vendor may already recognize the pattern, know the common failure modes and have a tested path to resolution.

The decision is no longer ‘DIY versus platform’ when you consider that DIY eventually means creating the platform yourself. Most organizations will continue to use PowerShell either way. The decision is which layers they want to engineer and maintain themselves:

• The PowerShell logic that expresses their business and administrative processes (yes!)
• The runtime that executes it
• The access model around it
• The interfaces through which users request it
• The evidence and observability it produces
• The compatibility work required when Microsoft changes the underlying platform
• Other automation workflows

This is the build-versus-buy question in operational terms.

The Deprecation Test

When the next Microsoft retirement notice arrives, look at the first work your team has to do.

If the announcement triggers a search for undocumented automation, missing owners, embedded credentials, unknown dependencies and workflows that must be reverse-engineered under a deadline, your team is still carrying most of the platform responsibility. The retirement has become an emergency product-maintenance project.

If the affected workflows are already inventoried, owned, centrally executed and observable, the work is more likely to remain bounded. Your team assesses the guidance, validates its workflows, schedules the change and closes it. A managed platform will not eliminate every migration task, but it can turn an open-ended discovery and rewrite exercise into controlled change management.

Several organizations we work with sit somewhere between those two conditions. When determining requirements, we’ve found that asking which platform responsibilities your team intends to keep owning can provide clarity. Responsibilities you retain should be funded and governed as product responsibilities. The rest should sit with a platform designed expressly for that purpose.

The post Adopt an Automation Platform, or Become One? The Hidden Cost of DIY Automation appeared first on Techstrong IT.

A cloud-native checkout service slows down. Application latency rises. Database commits become erratic. One dashboard shows elevated I/O wait, another shows a pod restart and a third shows a recent volume attach event. Every panel may be accurate, yet none tells the complete story.

This is the weakness in much of today’s AI-for-observability approach. A chat interface layered over disconnected cloud dashboards does not create root-cause analysis. It may summarize symptoms elegantly while missing the infrastructure change that caused them.

For enterprise cloud storage, the better AI-first approach is an evidence graph: A connected operational view of customer requests, Kubernetes workloads, database operations, cloud storage resources and infrastructure changes.

Cloud Storage Incidents Cross Too Many Boundaries

A storage incident may begin with an application release, a database failover, a node drain, a noisy neighbor, a replication lag, a network-path issue or a cloud storage bottleneck. Kubernetes adds another layer of complexity: Pods are short-lived, operators reconcile resources in the background and persistent volumes may remain healthy while a workload is slow or repeatedly recovering.

Cloud storage capacity, IOPS and latency dashboards are necessary but insufficient. They show that a resource is under pressure. They do not reliably establish whether that resource is the cause, the victim or simply adjacent to the incident. AI should not be asked to guess the difference.

Dashboards Show Signals, Evidence Graphs Show Causality

A cloud monitoring dashboard answers, “What is this metric doing?” An incident responder needs the harder answer: “What changed, what did it affect and what evidence supports that conclusion?”

OpenTelemetry is a useful foundation because traces, metrics and logs can carry shared resource context. Enrich signals with cloud and Kubernetes identity, such as region, cluster, namespace, workload, pod, node and persistent-volume identifiers. Ingest selected cloud, Kubernetes and operator events as structured data, then attach change identifiers from GitOps or CI/CD.

Names help people, but names are reused. Prefer stable workload, pod and volume identifiers for correlation, then preserve the cloud region and the time window in which the relationship existed.

Give AI an Investigation Package, not Unlimited Cloud Access

When a cloud storage SLO breaches, the assistant should receive a constrained evidence package:

• The affected cloud service, route, SLO and incident window
• Representative traces that crossed the latency threshold
• Workload, pod, node, database and storage identities from those traces
• Relevant volume, CSI, cloud and operator events
• Recent deployments, scaling, failovers or maintenance changes
• Approved runbooks and policy boundaries for proposed actions

This turns “Find the root cause” into a defensible task: Explain which evidence supports or contradicts each likely hypothesis. A cloud node upgrade, for example, becomes credible only when it aligns with volume attachments, database recovery activity and customer-facing latency. The assistant should show that chain, identify uncertainty and state what would disprove it.

Keep AI Outside the Cloud Safety Loop

AI can summarize evidence, rank hypotheses, draft incident updates and recommend runbooks. It should not independently fail over a cloud database, cordon a node or resize production cloud storage.

Use staged autonomy:

• Observe: Produce an evidence-backed incident brief.
• Recommend: Propose approved cloud runbooks and the expected blast radius.
• Bounded Action: Execute only reversible, pre-approved actions after deterministic checks.
• Governed Action: Require explicit approval for high-impact cloud changes.

The safety checks must remain deterministic. Is this production? Is the error budget exhausted? Does the action violate a disruption budget? Is backup status current? Is the command in an approved runbook? The agent can propose. The cloud platform must decide.

Start With One Cloud Storage Failure Pattern

Do not model the entire cloud estate first. Pick one recurring failure pattern: Database write latency after node maintenance, PVC mount delays during autoscaling or replication lag after a topology change.

The success metric is not how fluent the AI explanation sounds. It is whether the system surfaces the right cloud evidence faster, eliminates bad hypotheses earlier and recommends only actions the platform can safely permit.

AI-first cloud observability is not about giving a model more dashboards. It is about giving cloud engineers a trustworthy chain of evidence. Before adding another observability copilot, ask: “Can your cloud platform prove why the AI believes a storage action is safe?”

The post Stop Asking AI to Read Cloud Storage Dashboards. Give It a Change Graph appeared first on Techstrong IT.

What is Multi-Cloud Connectivity?

Multi-cloud is when businesses use multiple cloud providers to deliver services across their technology stacks. Often, this involves using software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS) solutions from different providers.

However, simply using multiple cloud applications simultaneously provides less value. For data consistency and to unlock features such as automation, apps should communicate between cloud environments.

For instance, let’s imagine you are running a customer-facing application that uses multiple cloud platforms. You use Amazon Web Services (AWS) to power the main back end, Google Cloud to run data analytics workloads and Microsoft Azure for identity management.

These tools become even more useful when integrated. Data flows from the back end into Google Cloud for analysis, while Azure ensures authentication on all your platforms.

What is Hybrid Cloud?

A hybrid cloud integrates two or more forms of infrastructure. This could be a combination of an on-site data center or a private cloud (or both), with a public data center.

Organizations choose hybrid clouds for a variety of reasons. Hybrid clouds help improve compliance, as sensitive information can be stored on private clouds or on-site servers, improving data security.

Another advantage of a hybrid cloud is scalability. As your needs grow, you can ramp up cloud-based capabilities without needing to invest in any new hardware.

It should be noted that hybrid clouds and multi-clouds aren’t necessarily separate solutions. A multi-cloud can also function as a hybrid cloud.

The Pros and Cons of Multi-Cloud Networking

Multi-cloud networking can bring many benefits to your business. However, this solution isn’t right for all organizations. We’ve listed some of the pros and cons below to help you make a more informed decision.

Pros

Improved Reliability and Reduced Downtime

With a single-cloud approach, you risk losing access to all services in the event of an outage. Customers might not be able to carry out important conversions, such as signing up for your site or making a purchase.

A multi-cloud approach increases redundancy. If one cloud experiences downtime, other clouds will continue to run. This means you’ll still be able to benefit from some (albeit reduced) functionality.

Easier Migration Between Providers

Vendor lock-in is a bigger problem with a single-cloud approach. When infrastructure and functionality are offered by a single provider, switching vendors is a significant effort.

For instance, when all your information is in one place, migrating becomes a bigger challenge, as transferring data can be highly expensive. Organizations often stick with their current providers simply to avoid the expense.

Switching vendors is often easier with a multi-cloud approach. There’s less upheaval, as you’re swapping one aspect of your existing infrastructure.

Greater Flexibility and Choice

With a single-cloud provider, you’re reliant on the services within a provider’s ecosystem. While providers often offer flexibility, you’re still limited to the options on offer.

Multi-cloud connectivity gives the option to pick and choose between providers to find a solution that closely fits your needs. You can also distribute applications strategically across different clouds to improve performance and reduce costs.

For example, many organizations are adopting secure access service edge (SASE) to bring networking and security into a single, unified service. Here, one provider handles security and another deals with networking. This helps to ensure the best possible network performance and security.

Easier Regulatory Compliance

Data privacy legislation often mandates certain data handling practices, especially for highly regulated industries. For instance, the European Union’s General Data Protection Regulation (GDPR) requires data for EU citizens to be stored safely and securely. It puts certain restrictions on transferring information outside the EU.

Working with a single-cloud provider can make compliance more difficult. If your provider doesn’t offer EU data centers, you may need to lay additional safeguards in place to protect data.

With multi-cloud, you can choose a provider whose servers are located in the EU. This means fewer data transfers and easier compliance with EU law.

Cons

Increased Operational Complexity

Multi-cloud networking is often more complex than single cloud. All vendors are different, with a preference for certain APIs, dashboards and other varying factors. This requires more expertise to ensure coordination between apps. Cloud-native complexity can cause mistakes that lead to performance or security issues down the line.

This is less of a concern when working with a single provider. All apps use more consistent interfaces and tooling models. This allows organizations to set up more quickly with reduced scope for error.

More Challenging Data Governance

A single-cloud provider brings a unified approach to data governance. With multi-cloud, different cloud providers have their own methods of storage, managing databases and applying metadata and other cataloging information. This makes it harder to apply consistent data management policies across your organization.

Identity and access management (IAM) becomes equally challenging. Different IAM systems mean permissions often require manual remapping between cloud environments. This can make mapping policies a more complex process that often takes time and can lead to mistakes.

An Expanded Threat Landscape

A multi-cloud network potentially means a larger attack surface for cybercriminals compared to a single cloud.

As we’ve reflected, a multi-cloud approach can be more complex. Mistakes during integration can lead to vulnerabilities that attackers can exploit. What’s more, different firewall and networking configurations can create inconsistencies with your security policies.

Businesses will need to take additional steps to ensure a secure cloud-native architecture. Regular risk assessments combined with security management tools are needed to ensure a secure environment.

Is Multi-Cloud Right for You?

Multi-cloud connectivity brings many benefits to businesses. As we’ve explored, though, it also poses some potential challenges.

Ultimately, think about your business’ unique circumstances. If you’re a smaller business lacking in-house expertise, a single-cloud network might be the way forward. If you’re a larger organization, want to avoid vendor lock-in and have the necessary specialized teams, multi-cloud might be a strong option.

The post A Guide to Multi-Cloud Connectivity and Networking appeared first on Techstrong IT.

Overall, the specification refresh demonstrates how NVMe continues to evolve beyond a high-performance storage protocol into a broader platform supporting enterprise infrastructure.

The latest release spans all 11 NVMe specifications, including updates to the NVMe 2.4 Base Specification and the NVMe Management Interface (NVMe-MI) 2.2. The revisions are wide ranging. They focus on improving storage virtualization, preparing for post-quantum security requirements, enhancing quality of service and simplifying device management for enterprise, cloud and AI deployments.

Among the most significant additions is native SSD virtualization for locally attached PCIe drives. The new PCIe Exported NVM Subsystem Migration capability allows physical NVMe SSDs to expose virtualized storage resources directly to the host. This reduces the complexity handled by hypervisors and virtual machine managers.

The enhancement is designed to simplify live migration of VMs between servers. Instead of requiring storage reconfiguration whenever workloads move to new hardware, the exported virtual storage subsystem preserves storage identifiers throughout the migration process. The advantage is that VMs keep operating without changes to the underlying storage configuration, reducing disruption and admin overhead.

By embedding virtualization capabilities within the SSD itself, the updated specifications also offload part of the storage management burden from software layers, creating a cleaner separation between physical hardware and virtual infrastructure.

Preparing for Quantum

Security receives a major upgrade with the addition of support for post-quantum cryptography. The specs now accommodate quantum-resistant encryption algorithms approved by NIST. This provides a path for companies to prepare storage systems for future cryptographic threats.

Although quantum computers capable of breaking today’s encryption algorithms have yet to emerge, many enterprises are beginning long-term planning for quantum-safe infrastructure. Incorporating support for standardized post-quantum algorithms enables NVMe storage vendors and enterprise customers to begin future-proofing storage security without waiting for hardware redesigns.

Performance management also gets an update. A new controller-based rate limiting capability enables admins to establish limits for both input/output operations per second (IOPS) and bandwidth directly within NVMe controllers. The feature allows cloud providers and enterprise IT teams to define predictable storage service tiers while maintaining consistent quality of service across shared infrastructure.

The consortium also expanded device management capabilities with several improvements. New voltage monitoring functions provide additional visibility into SSD health by detecting electrical anomalies that could affect reliability or performance.

Another enhancement simplifies device recovery by allowing hosts to restore an NVMe subsystem to its factory-default configuration. The upgrade streamlines troubleshooting while helping admins maintain consistent configurations across large storage deployments.

The post NVM Express Updates NVMe Standards for AI, Virtualization and Security appeared first on Techstrong IT.

The two companies say Starmind satellites will be powered by NVIDIA’s Vera CPUs and Rubin GPUs. SpaceX boss Elon Musk explained the NVIDIA tie-up by simply saying “they are the best.” NVIDIA says its Space-1 Vera Rubin module can deliver up to 25 times the AI processing performance of its H100 GPU.

The scale of the undertaking is nothing if not ambitious. SpaceX earlier this year petitioned the Federal Communications Commission (FCC) to deploy as many as one million orbital data center satellites between roughly 311 and 1,243 miles above the Earth. The AI satellites would exchange data using high-speed laser links. The SpaceX AI network would dwarf today’s existing satellite constellations, which number about 15,000 in total. Prototype testing for Starmind is scheduled for 2027 with mass production set for the following year.

“Our goal over time is to launch 100 gigawatts of compute to space each year. If operated continuously, the generation resources used to support 100 gigawatts of compute could generate approximately one-fifth of the annual power production in the United States, which was 4.4 thousand terawatt-hours in 2025,” said SpaceX in its May filing. “We expect space-based compute to massively increase AI compute scale while also improving token economics.”

AI satellites in orbit have an obvious attraction in their ability to take advantage of non-stop, free solar power. They also circumvent the limitations of terrestrial AI data centers, among which is increasing public opposition.

NVIDIA, for its part, last month signaled its AI computing chips are robust enough for space use when it became part of Firefly Aerospace’s moon mission slated for later this year. The NVIDIA Jetson AI module will be used to process images on board the spacecraft, eliminating the need for massive data downlinks to Earth.

Behind the scenes, technical challenges also are being addressed. Late last year, researchers at Germany’s JMU successfully demonstrated an AI altitude controller for satellites in orbit. SpaceX’s FCC filing for a wide range of orbital altitudes suggests maneuverability is in the cards for Starmind satellites.

Similarly, a lightweight heat dissipation device made from a composite material combining carbon fiber and graphite makes planar antennas 47 percent lighter. It was developed with satellite communications in mind through a joint effort by several Japanese companies. Heat dissipation in the vacuum of space is a major challenge that can affect a satellite’s potential longevity. There’s no atmosphere to cool things down.

Meanwhile, back on Earth, analysts suggest that satellite direct-to-device (D2D) services may be on the cusp of a boom beyond existing emergency applications. As multiple LEO constellations scale toward full operational capacity beyond 2026, direct satellite connectivity is expected to become a standard feature on many mobile computing devices. If Musk’s vision becomes fully realized, Starmind will be reachable by phone.

The post SpaceX and NVIDIA Team Up For Starmind Orbital AI appeared first on Techstrong IT.

The rise of no-code platforms has helped address many of these challenges. By enabling users to build applications, automate workflows and configure processes without extensive coding knowledge, no-code technologies are making software development more accessible across the organization. Business users no longer need to wait for scarce development resources to make changes or create solutions that improve productivity and efficiency.

This shift is creating significant opportunities for innovation, but it is also exposing organizations to a new challenge. As software creation becomes easier and more widespread, the volume of software change increases dramatically. The question organizations should now be asking is not whether they can build software faster, but whether they can manage the risks that come with accelerating change.

Faster Software Development Does Not Automatically Create Better Business Outcomes

One of the reasons no-code platforms have gained such momentum is that they address a genuine business problem. Traditional development processes can be slow, resource-intensive and heavily dependent on technical specialists. No-code tools help remove these barriers by enabling more people to participate in creating and improving software.

The benefits are clear. Business teams can respond more quickly to changing requirements, automate repetitive tasks and improve processes without lengthy development cycles. Organizations become more agile and are often able to deliver innovation at a pace that would have been difficult to achieve using traditional approaches alone.

However, speed should never be confused with success.

The reality is that software can be developed quickly, deployed successfully and still fail to deliver the business outcomes organizations expect. A workflow may function exactly as designed but create unintended consequences elsewhere in the business. An application may automate a process while introducing new inefficiencies. A seemingly minor change may affect integrations, reporting or operational activities in ways that were never anticipated.

This is where many organizations encounter difficulties. They focus on the speed at which software can be delivered but spend less time considering how those changes affect the wider business. The result is that software quality becomes reactive rather than strategic, with problems often discovered only after they begin affecting users, customers or business operations.

The Real Cost of Software Failure Is Rarely Technical

When software issues occur, organizations often focus on the technical problem itself. How quickly can the issue be fixed? Which system failed? What caused the disruption?

While these questions are important, they rarely reflect the true cost of software failure.

In reality, the biggest consequences are usually commercial and operational rather than technical. An issue affecting a customer-facing application can disrupt service delivery and damage trust. Problems within an ERP or finance system can delay critical business processes. Workflow failures can reduce employee productivity and create operational bottlenecks that affect multiple departments.

Many organizations continue to measure software failures in hours of downtime, yet downtime is often only a small part of the story. The wider consequences can include delayed revenue, frustrated customers, increased operational costs and reputational damage that persists long after the original issue has been resolved.

As software becomes increasingly central to how organizations operate, the cost of failure continues to rise. Every new application, workflow and integration creates another dependency that the business relies upon. This means that even relatively minor issues can have a disproportionate impact when they affect systems supporting critical processes.

The challenge is particularly significant in environments where software changes frequently. The more rapidly organizations introduce new functionality, the greater the need to understand how those changes will affect the wider business.

Why Software Quality Is Becoming a Competitive Advantage

The organizations that are gaining the most value from no-code technologies are not necessarily those making the largest number of changes. They are the organizations that can introduce change confidently because they have effective quality and governance processes in place.

This is where the conversation around software quality needs to evolve.

Historically, quality has often been discussed in technical terms, focusing on testing activities, defect management and system performance. While these remain important, they are ultimately a means to an end. The real objective is ensuring that software supports business outcomes without creating unnecessary disruption.

As software creation becomes more distributed across organizations, quality can no longer be viewed as the sole responsibility of IT teams. Business users are becoming increasingly involved in creating, configuring and managing applications, which means they also have an important role to play in ensuring those applications support operational requirements.

The organizations that recognize this shift are better positioned to innovate because they understand that quality and agility are not competing priorities. In fact, the ability to introduce change safely is often what enables organizations to move faster in the long term. Confidence in software quality allows businesses to embrace innovation without constantly worrying about the consequences of failure.

Looking Beyond the Technology

No-code platforms are transforming the way organizations develop software, and that is undoubtedly a positive development. They are helping businesses reduce bottlenecks, improve agility and empower employees to solve problems more effectively.

However, the success of no-code should not be measured solely by how quickly software can be created. It should be measured by how effectively that software supports business objectives and enables organizations to operate with confidence.

Functional software is important, but functional software alone no longer cuts the mustard. As software becomes increasingly embedded within every aspect of business operations, organizations need confidence that the applications and processes they create will continue to deliver value without introducing unnecessary risk.

The businesses that thrive in this new environment will not simply be those that develop software faster than their competitors. They will be the organizations that combine innovation with quality, allowing them to embrace change while maintaining the resilience, operational stability and customer trust that long-term success depends upon.

The post The Hidden Cost of Software Failure in a No-Code World appeared first on Techstrong IT.

The Federal Communications Commission (FCC) is developing a proposal that would prohibit imports of new models of Chinese optical transceivers, networking components that transmit data over fiber-optic cables linking servers throughout today’s data centers. The measure has not been finalized and could still be revised or withdrawn, but officials are aiming to publish the rule before the end of the year.

Optical transceivers have become an essential element of AI infrastructure. The devices convert electrical signals into optical signals and back again, allowing enormous volumes of data to move between servers at extremely high speeds. As hyperscale operators expand facilities to train and run LLMs, these components play a central role in enabling low-latency communication across thousands of GPUs and networking switches.

The proposed restrictions reflect growing concern within Washington that Chinese-made networking hardware could introduce cybersecurity and supply chain risks into the infrastructure underpinning the US AI industry. Officials reportedly fear that compromised equipment could provide opportunities for data theft or disruption of services supporting AI applications.

Follows Recent Restrictions

The proposal is the latest expansion of a US strategy to limit Chinese technology in critical infrastructure. Recent actions have included restrictions on Chinese humanoid robots and power inverters, while policymakers have also examined potential limits on Chinese open-source AI models. In sum, the approach extends beyond semiconductors to the networking, power and infrastructure technologies required to support AI deployment.

Unlike export controls aimed at advanced AI chips, the latest proposal focuses on the networking layer that connects data centers. Beyond servers and GPUs, AI facilities depend on fiber-optic networks, optical transceivers, switches, storage systems and power equipment operating as an integrated platform. Securing each layer of that infrastructure has become a growing priority as billions of dollars are committed to expanding US AI capacity.

Clearly, the initiatives will run into a key challenge: balancing security objectives with the practical realities of global tech supply chains. Chinese manufacturers account for a significant share of the worldwide optical transceiver market, and many operators rely on those components because of their availability and competitive pricing. Replacing those products could increase costs while complicating procurement for US companies building new AI data centers.

Additionally, enforcement may prove difficult. Components can move through distributors, subsidiaries and third-party suppliers, making it complicated to identify a product’s origin throughout the supply chain. In addition, some categories of networking and power equipment currently have limited alternatives capable of matching Chinese suppliers’ production volumes.

China has opposed recent US technology controls and warned it would respond to actions that materially harm its interests. Washington, however, has encouraged allies to adopt similar supply chain safeguards, raising the possibility that restrictions on Chinese AI infrastructure components could expand beyond the US.

The post Trump Administration Weighs Ban on Chinese Data Center Equipment appeared first on Techstrong IT.

Our goal in these challenging economic times is to make it just that much easier for IT professionals to advance their careers.

Of course, the pool of available IT talent is still relatively constrained, so when one IT professional takes on a new role, it tends to create opportunities for others.

The ten job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the IT job market. In the meantime, for your consideration.

Greenhouse

Chime
Chicago, IL
Software Engineer, Infrastructure
$164,000 to $227,000

Varda Space Industries
El Segundo, CA
Site Reliability Engineer II
$133,000 to $170,000

Anduril Industries
Costa Mesa, CA
Senior Site Reliability Engineer, TS Clearance
$166,000 to $220,000

SpaceX
Hawthorne, CA
Sr. Site Reliability Engineer (Starlink)
$165,000 to $265,000

Axon
Seattle, WA
Sr. Site Reliability Engineer I
$134,250 to $214,800

Tenable
San Francisco, CA
Software/Site Reliability Engineer – FedRAMP
$131,500 to $175,500

onX
Bozeman, MT
Site Reliability Engineer III
$130,000 to $153,000

Carbon
Sunnyvale, CA
Staff Infrastructure Software Engineer
$209,600 to $314,400

XPENG
Santa Clara, CA
Senior Data Infrastructure Engineer
$174,720 to $295,680

fairlife, LLC
Chicago, IL
Sr. Infrastructure Engineer
$115,000 to $135,000

The post Ten Great IT Job Opportunities appeared first on Techstrong IT.

The pattern that shows up repeatedly is not a single bad actor. It is a sequence: incentives are approved before residents understand the scale, non-disclosure agreements limit what elected officials can say, and, in some states, the legal authority to say “no” is transferred away from the people closest to the project. By the time the public arrives, the deal is often already done.

What We Found

Part Two documents that sequence across five states. In West Virginia, House Bill 2014 created a “Certified Microgrid Program” that lets qualifying projects bypass Public Service Commission siting review and local zoning — a preemption disclosed to residents of Tucker County only after the Ridgeline project was already moving.

In Louisiana, HB 827 — now Act 730 — extended tax breaks worth billions to the data-center industry as Meta’s Hyperion campus ballooned toward a reported $50 billion commitment. A separate strand of reporting by Floodlight documented a state senator advocating for the project while quietly negotiating land sales to the utility building its power supply. The senator has denied wrongdoing.

In Minnesota, Google’s Project Skyway in Pine Island advanced under a code name while local officials weighed a $36.5 million incentive package the public had not yet seen. And in Pennsylvania, Senate Bill 939 would strip municipalities of the authority to require conditional-use hearings for data-center projects — a move opposed by the Pennsylvania Municipal League.

Why It Matters

The federal fight over who pays for the AI build-out gets most of the attention. The state and local fight is over something more basic: who decides. Preemption statutes, NDAs, code-named developments and incentive packages structured before public notice are not incidental features of the boom — they are the mechanism by which the deal gets locked in before the vote.

Part Two does not argue that every project is corrupt or that every rejection is wise. It documents a repeating sequence in which the public’s only remaining role is to react to a decision that has already been made.

Counter-Cases: When the Public Process Still Had Teeth

Not every community loses. In Franklin Township, Indiana, Google withdrew a proposed 468-acre campus after the Indianapolis City-County Council retained real authority over the rezoning and residents organized before the vote. In Chandler, Arizona, the city council rejected the rezoning unanimously despite an AI Infrastructure Coalition campaign led by a former U.S. senator. The common factor in both outcomes is not opposition. It is authority — a jurisdiction that had not already been preempted, and enough information delivered early enough for the public to organize.

Inside Part Two

• How West Virginia’s Power Generation and Consumption Act preempts local zoning — and how a Fundamental Data representative said the company helped write it
• The Louisiana sequence: HB 827 / Act 730, an NDA-covered land deal, a public-service commissioner and a state senator with property along the project route
• Pine Island’s Project Skyway and the pattern of code-named data-center developments
• Pennsylvania SB 939 and the fight over municipal conditional-use authority
• Indianapolis and Chandler as the counter-cases — what changes when the vote still matters
• How preemption, NDAs and incentive design combine into a repeatable playbook

Follow the Lobby Money is a continuing Techstrong investigation. Tips, disclosures and corrections: Email Us

Download the full report (PDF)

The post Follow the Lobby Money — Part Two appeared first on Techstrong IT.