xAI is running its 550,000-GPU Colossus fleet at roughly 11% model FLOPs utilization. An internal memo from infrastructure lead Michael Nicolls confirmed the figure and set a target of 50% — a number that sits at the high end of an industry range where even the best operators, Meta and Google, are only achieving 43–46%. For a company that built the world’s largest AI supercomputer in 122 days, the software stack is still catching up to the hardware.

Every few months the same headline resurfaces: DevOps is dead. In 2026, the claim has more surface credibility than before — job boards are brutal, the title is disappearing from org charts, and AI is eating the work that once defined the role. None of that means the function is gone. It means the floor dropped out and the ceiling went up.

The mechanics are straightforward. AI systems built into cloud platforms now handle what junior and mid-level DevOps engineers spent most of their time on — infrastructure provisioning, pipeline configuration, alert triage, rollback decisions. That work isn’t being done by fewer people out of choice. It’s being done by machines out of necessity, because the scale modern systems operate at makes human-in-the-loop intervention a bottleneck. Netflix deployed 4,000 times daily last quarter. Amazon pushes a change every 11.7 seconds across global infrastructure. No headcount solves that. Automation does.

Not dead — but under serious pressure, and some of its foundational assumptions are eroding fast.

Agile was designed around the scarcity of working software. Writing code is slow, human attention is finite, and iteration is expensive. Its rituals — sprints, standups, story points, velocity tracking — exist to manage that scarcity. AI coding assistants like GitHub Copilot, Cursor, and Claude Code dramatically compress the time from intent to working code. When a sprint’s worth of boilerplate takes an afternoon, the sprint cadence starts to feel like bureaucracy.

ServiceNow has expanded its platform into manufacturing with a suite of AI-native capabilities covering the full value chain from sales and configuration through quality, warranty, and factory-floor operations. The announcement, made alongside the unveiling of Industrial Connected Workforce and ServiceNow EmployeeWorks for manufacturers, positions the company directly against the fragmented stack that characterizes most industrial enterprises.

The core argument is structural. Manufacturing AI investments have accelerated over the past two years, but quality data, warranty claims, order workflows, and shop-floor processes typically run on separate systems with no shared governance layer. ServiceNow’s pitch is that AI cannot deliver end-to-end execution when the data it needs is distributed across siloed tools—and that a single platform with unified workflows changes that calculus.

Systal Technology Solutions has been named a Leader in the 2026 Gartner Magic Quadrant for Managed Network Services, marking its third consecutive inclusion in the report and reinforcing its position within the global managed services landscape. The recognition reflects how enterprise network environments continue to evolve toward highly complex, multi-vendor and hybrid cloud architectures where operational stability, automation, and security integration are becoming inseparable priorities. In this context, Systal has been highlighted for its approach of acting as an embedded extension of enterprise IT teams, supporting both day-to-day operations and longer-term transformation initiatives.

The manufacturing sector’s AI problem has never been a shortage of tools — it has been the absence of tools that understand what manufacturing actually is. Infor and AWS are now pushing a direct answer to that gap, announcing a collaboration to deploy industry-specific AI agents built natively on AWS infrastructure, targeting discrete and process manufacturing enterprises that need to move from pilot programs to production at scale.

The core argument is that generic AI fails on the shop floor. Manufacturing workflows involve bill of materials hierarchies, vendor pricing cycles tied to annual model changes, returns processing chains, and procurement-to-payment pipelines that off-the-shelf models cannot navigate without substantial domain grounding. Infor’s position is that it supplies the industry-specific intelligence while AWS provides the enterprise infrastructure — Amazon Bedrock AgentCore, Amazon Bedrock, and Amazon SageMaker — to run it at mission-critical reliability.

Kyndryl (NYSE: KD) has been recognized as a Leader in three categories of the 2026 ISG Provider Lens™ Mainframes — Services and Solutions Report: Mainframe Technology Consulting, Mainframe as a Service (MFaaS), and Application Modernization Services. The triple recognition from ISG, an independent technology research and advisory firm, positions Kyndryl among the top-tier providers supporting enterprises running mission-critical mainframe workloads through a period of compounding modernization pressure.

In the Mainframe Technology Consulting quadrant, ISG cited Kyndryl’s strategy and assessment-led methodology, its targeted infrastructure optimization approach, and its hybrid cloud integration focus. The report highlighted the role of Kyndryl Bridge and agentic AI-enabled automated discovery in delivering operational insights across discrete engagements and multi-year transformation programs alike. For MFaaS, ISG noted Kyndryl’s large global outsourcing footprint and the breadth of its consumption models, which range from customer-owned environments to shared platforms including zCloud and C4i, alongside continued investment in AI-driven autonomy and observability. In Application Modernization Services, ISG recognized the company’s end-to-end approach spanning structured assessments, iterative implementation, and hyperscaler alliances, again underpinned by Kyndryl Bridge and its governance and agentic AI capabilities.

We are likely in the early stages of another productivity boom.

You can feel it before you can fully measure it. Not in the official statistics yet, not neatly captured in GDP releases or quarterly productivity reports, but in the way work itself is beginning to change. Tasks that used to take hours now collapse into minutes. Layers of friction around research, drafting, analysis, coordination, and execution are starting to thin out. The shift is still uneven, still messy, but it is becoming difficult to ignore.

Hardware asset management — knowing what physical devices the organization owns, where they are, who has them, what software is installed on them, and when they need to be refreshed or retired — is foundational to almost every other IT function. Security teams need accurate asset inventory to understand their attack surface. Support teams need device configuration data to resolve issues efficiently. Finance teams need asset records for depreciation and insurance. Procurement teams need lifecycle data to plan refresh cycles.

Low-code and no-code platforms arrived with a promise that has been partially delivered and significantly oversold: that business users without programming backgrounds could build the software applications they needed without depending on IT development teams. The partially-delivered part is real. Workflow automation tools like Power Automate, Zapier, and Make have genuinely enabled business users to build integrations and automations that previously required developer time. The oversold part is the claim that this capability extends to applications of arbitrary complexity.