The Techstrong.IT stories enterprise IT leaders actually read.
Volume 341, Issue 3 · 20 articles from 7 issues
An email power user returns to Outlook for Mac after years of testing alternatives that buckled under volume.
Alan Shimel, managing four Gmail accounts plus a Microsoft account with over 1,000 daily emails, tested numerous AI-era email applications including Spark Desktop and Canary.
Most alternatives consumed excessive memory and crashed under his workload. After years of using browser tabs instead, he returned to Outlook for Mac and found it does not blink at multiple accounts and deep archives.
The integrated Copilot and calendar functions proved genuinely useful rather than decorative, illustrating that established products sometimes need time to become what users originally wanted.
Apache Spark 4.2 targets AI workloads with native vector search, geospatial support and Python streaming.
Apache Spark 4.2 introduces first-class AI capabilities including Metric Views for consistent business intelligence, native vector similarity functions for embedding operations, and geospatial data types.
The release enhances PySpark with Arrow-optimized execution enabled by default and real-time streaming support in Python.
Auto Change Data Capture and improved data pipeline management help maintain feature consistency for AI models across training and serving.
Storage market pressure drives a Qumulo and Cisco bridge-to-cloud approach for extending workloads without migration.
Flash prices have surged over 400% annually due to AI hyperscaler demand, while hardware lead times stretch to 26 weeks.
Qumulo's Cloud Data Fabric lets organizations extend existing NAS workloads into public clouds through Cisco UCS infrastructure, using NeuralCache predictive prefetching to mask latency.
Three deployment patterns address different needs: cloud archive for legacy data, cloud-based hot edge for primary cloud storage, and hardware reuse for existing equipment. A unified namespace enables zero-copy AI access without expensive data duplication.
Assuming network automation needs dedicated software teams instead of skilled network engineers is a costly mistake.
The article debunks the assumption that network automation requires dedicated software development teams rather than network engineers with automation skills.
It warns that building custom provisioning platforms in-house wastes millions and years while creating engineer lock-in risk when key developers leave.
Internal builds struggle with brownfield legacy infrastructure, inconsistent naming, and edge cases that divert engineers from strategic work.
A 2025 survey found 92% of organizations report automation is built by network engineers, not software developers. Purpose-built platforms like Nautobot handle foundational complexity, while genuine custom effort should focus on integrations connecting IPAM, CMDB, change management, and vendor APIs.
Every industry now runs on IT, pushing boards to demand AI-driven automation and multi-cloud resilience within months.
Digital technology has become fundamental to operations across all industries, not just tech companies. CIOs face mounting pressure to deliver automated IT operations, observability and resilience because outages directly hit revenue, customer retention and operational safety.
After major cloud outages, organizations are reconsidering heavy public cloud dependence, adopting multi-cloud approaches and building private data centers for greater control over data, AI systems and compliance.
Banking, healthcare, energy and retail exemplify the trend, where always-available digital services make technology performance inseparable from business strategy.
IT leadership has consequently shifted from a support function to a strategic partner shaping enterprise decisions.
US public support for AI data centers has collapsed, driven by lopsided community deals rather than anti-AI sentiment.
Alan Shimel examines the rapid shift in American attitudes toward data center development, with public support falling from 43% in August 2025 to 21% by spring 2026.
He frames this as an "anti-deal revolt" rather than opposition to AI, since host communities receive minimal employment benefits while absorbing environmental and utility costs.
Examples from Broadview, Montana; Richland Parish, Louisiana; and Memphis's Boxtown neighborhood show infrastructure concentrated without equitable compensation.
The root problem is misaligned incentives among tech companies, utilities, politicians and investors who profit while residents bear externalities.
Shimel concludes that public acceptance is an infrastructure requirement, not a public-relations problem.
IBM, Oak Ridge, and Cleveland Clinic used quantum computing for a first molecular simulation aiding fusion fuel.
Frank Vizard reports scientists from IBM, Oak Ridge National Laboratory, and the Cleveland Clinic used quantum computing to calculate molecular configurations of a liquid salt for nuclear fusion fuel, described as the first known molecular simulation aided by quantum computing and combined with classical AI.
The work targets tritium production, a key fusion barrier. The Trump Administration has prioritized quantum via executive orders and a summit, while the NSA and Army launched Quantum Eagle.
Quantum investment topped $40 billion in 2024 across pharma, finance, defense, and energy, though it threatens cryptocurrency security as early as 2029, prompting some bitcoin miners to pivot to AI infrastructure.
Data centers face rising regulatory pressure over power and land even as AI investment keeps surging.
James Maguire reports the data center industry faces growing regulation alongside surging AI demand. QTS Realty Trust withdrew its appeal for the roughly $100 billion Prince William Digital Gateway in Virginia after procedural rezoning issues and local opposition over land, power, and water.
New Jersey approved a dedicated electricity tariff for facilities of 50-plus megawatts, requiring payment for at least 85% of requested service for 10 years and subjecting data centers to curtailment before residential customers.
Despite hurdles, TeraWulf secured a 20-year deal with Anthropic worth about $19 billion for its Kentucky facility, showing continued AI capacity commitment.
Evidence graphs, not dashboards alone, let AI reliably diagnose cloud storage incidents.
Cloud storage incidents span applications, databases, Kubernetes, and infrastructure changes, making isolated dashboards insufficient for root-cause analysis.
The article advocates evidence graphs that connect traces, metrics, logs, events, and deployment changes into defensible causal chains.
Organizations should give AI a constrained investigation package rather than unrestricted cloud access, using staged autonomy where AI observes and recommends while humans approve high-impact actions.
Success should be measured by faster evidence discovery and safer recommendations, not fluent explanations, and teams should pilot AI-first observability on one recurring failure pattern before broader rollout.
Multi-cloud strategies offer flexibility and redundancy but introduce operational complexity and security challenges.
Multi-cloud connectivity lets organizations use services from multiple cloud providers so data and applications work together across platforms.
Key advantages include improved reliability through redundancy, easier vendor migration, greater flexibility in selecting services, and simpler regulatory compliance by choosing providers in specific regions.
Organizations must also navigate increased operational complexity, more challenging data governance across different systems, and a larger security attack surface requiring additional protective measures and risk assessment.
Reference architecture diagrams hide the dependencies and failure chains that actually cause production cloud outages.
Cloud platforms are usually presented as orderly collections of services with predictable behavior, but production environments are messier, full of interconnected dependencies that never appear in diagrams.
The article argues best practices alone are insufficient; what matters is testing systems under real failure conditions.
Hidden dependencies extend past the cloud itself, as when SMS-based authentication chains pull in telecom providers and mobile carriers, making published uptime targets misleading.
Messaging systems concentrate complexity and often fail because of upstream or downstream problems rather than the broker. Rather than reasoning backward from uptime percentages, teams should design resilience around business impact and observed failure patterns.
The NVM Express consortium releases specification updates covering AI workloads, virtualization, and post-quantum security.
The NVM Express consortium released updates spanning all 11 NVMe specifications, including the NVMe 2.4 Base Specification and NVMe-MI 2.2.
Key enhancements include native SSD virtualization for PCIe drives, enabling simplified VM live migration without storage reconfiguration, and support for post-quantum cryptography algorithms approved by NIST.
Additional improvements cover controller-based rate limiting for IOPS and bandwidth management, voltage monitoring for SSD health detection, and factory-default configuration restoration. The updates position NVMe as a broader enterprise infrastructure platform beyond high-performance storage.
No-code platforms accelerate development but require strong quality governance to manage the risk of rapid change.
While no-code technologies enable faster software creation and broader organizational participation, speed alone does not guarantee business success.
Software can function perfectly yet create unintended consequences across operations, affecting customer-facing applications, ERP systems, and workflows.
The true costs of failure are commercial and operational rather than technical, including delayed revenue, damaged trust, and reputational harm.
Organizations that gain competitive advantage combine innovation with quality assurance, recognizing that safe change management enables faster long-term delivery than rapid, uncontrolled development.
Third-party data centers now host more enterprise workloads than corporate facilities for the first time, driven by AI capacity demands.
According to Uptime Institute's 2026 Global Data Center Survey, 46% of enterprise workloads now run in third-party environments while enterprise-owned facilities account for 44%.
AI infrastructure is reshaping facility design, with average rack density exceeding 11 kilowatts for the first time, and nearly a quarter of operators managing racks exceeding 30 kW.
Data center operators remain cautious about AI autonomy in facility control, with confidence declining year-over-year. Staffing challenges persist, particularly for electrical specialists and junior personnel, as companies struggle to find qualified candidates needed for complex AI infrastructure.
Scality introduces the ADI platform to unify diverse AI storage demands under a single namespace with autonomous management.
Enterprise storage must now accommodate conflicting requirements from training jobs, inference caches, RAG pipelines, and regulatory archives simultaneously, with power constraints replacing cooling as the primary design constraint.
Scality ADI addresses this challenge through a software-defined platform unifying four storage tiers via a single S3 endpoint, policy-driven lifecycle management, and Guardian-an AI agent enabling natural language operations while maintaining human-in-the-loop governance.
The solution emphasizes real-time power telemetry, open-code delivery for transparency, and outcome-based SLAs covering availability, throughput, protection, and consumption, targeting enterprises managing multi-petabyte environments.
AI systems require intelligent incident response automation to bridge the gap between detecting failures and mobilizing engineers.
While modern AI observability tools excel at detecting degraded system states through GPU metrics and database latency tracking, organizations struggle with converting these alerts into rapid human response.
The article argues that production AI systems often fail silently-remaining technically available while output quality degrades-making traditional incident notification methods inadequate. Effective mobilization requires persistent alerting, automated on-call scheduling, chat integration, and contextual information.
True operational reliability now encompasses output integrity, not just infrastructure availability.
Selector uses AI-native observability architecture to address hybrid network monitoring without traditional alert-heavy approaches.
Selector reimagines network observability by building AI as the foundational platform rather than a feature layered on existing tools. The company's multi-domain correlation engine, network language model, and operational digital twin enable teams to identify root causes across network, infrastructure, and application layers without manual rule configuration.
This approach addresses enterprise concerns about data security by supporting internally-hosted models, while serving major organizations including three Fortune 5 companies and global telecommunications providers.
Unlike competitors adding copilots to legacy systems, Selector's architecture inverts the traditional workflow, delivering actionable narratives and recommended next steps directly to engineers.
Publishers must coordinate to demand fair treatment from Google's AI-driven search, which uses their content without adequate compensation.
Major publishers including USA Today, Politico, and Reuters are considering blocking Google access due to significant traffic declines caused by AI Overviews that answer questions directly without directing users to original sources.
The article proposes a publisher compact requiring separation of search indexing from AI use, affirmative consent for AI content consumption, transparency on usage, and compensation for commercial AI applications.
Author Alan Shimel argues that coordinated action is essential since individual publishers cannot safely withhold content alone, and that Google's parasitic model will ultimately fail without continued investment in original reporting and research.
AI's unexpected impact on flash storage economics creates a structural crisis requiring hybrid architecture solutions.
While AI adoption was anticipated, its dramatic effect on storage economics was not. Flash storage has shifted from a premium tier to a baseline requirement, causing prices to surge and availability to tighten because NAND manufacturing is concentrated among few suppliers.
Organizations face a dilemma: all-flash architectures become prohibitively expensive while HDD-only systems cannot deliver required performance.
The solution is balanced flash-HDD architectures with intelligent auto-tiering that moves data between tiers based on access patterns, delivering performance and capacity without manual intervention or excessive cost.
Smart tiering strategies help enterprises navigate NAND shortages and build resilient storage systems.
With NAND prices climbing 246% year over year, enterprises face supply pressures requiring structural solutions rather than procurement fixes.
Effective storage design matches data to appropriate tiers, using flash for active workloads, disk for warm data, and object storage for archives, since roughly 80% of primary storage data becomes cold within two years.
Organizations that implemented intelligent tiering before the crisis gained immediate cost savings, while those treating storage as a single-tier problem face constrained choices. Building flexibility across file, block, and object storage, including hybrid and cloud options, creates resilience against supply disruption and future AI demand.
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