Ben McCormack

The more autonomous AI becomes, the more binaries it will create. The more binaries created, the more security is required at the gate, and the more governance is needed before deployment to production. 🐸 https://bit.ly/4seEMPq #JFrog #LiquidSw

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JFrog’s Earnings Leap: Hopping Past Wall Street’s Cloud Rev Forecasts (and Competitors) – ( $FROG $SPY ) https://lnkd.in/gE_hMhKQ JFrog showcased impressive growth in Q3 2025, combining software innovation, cloud momentum, and AI advancements #JFrog #EarningsLeap #FROG #CloudGrowth #DevOps #DevSecOps #AI #SoftwareSecurity #RevenueSurge #MarketLeaders #TechnologyStocks #InvestorConfidence #FinancialResults #QuarterlyEarnings #GrowthStock #SoftwareSupplyChain #Innovation #WallStreet 

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Our combined value proposition is simple, powerful, and industry-changing: Autonomous Remediation: By connecting Chronosphere's petabyte-scale observability data to the AI-driven brain of Cortex AgentiX, we are moving the industry from passive 'dashboard theater' to autonomously fixing problems in real-time. Massive Cost Savings: Chronosphere's purpose-built architecture for cloud and AI scale filters unnecessary data, surfacing the most critical insights and reducing costs by over 50% for our customers.

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.@MongoDB: Q4 strong, but outlook light amid exec departures https://bit.ly/3ODcf7R MongoDB's fourth quarter earnings were better than expected, but the company announced executive departures and an outlook that missed expectations.

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Why aren't tech companies building more solar power? Yes, the Amazon AWS outage was impactful - for a day - and the cause (DNS) is interesting - but what stands out to me is data center increasingly heavy needs for electricity. According to this article, in Virginia 25% of the energy usage is by data centers; and they are building more. We need more solar and wind, we need more guidance from politicians, and more ethical focus by these companies. China, by example, is WAY ahead of the US now in the pace of building new large-scale solar installations. https://lnkd.in/dVtawPk5 #DataCenters #Electricity #power #Energy #ethics #Environment

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NetApp's Q3 FY2026 results offer a revealing detail worth noting. The company reported record all-flash array revenue, attributing it to constraints on power and density driven by AI workloads in data centres. This aligns with what many in FinOps have observed: enterprises carry a substantial, often hidden expense even before running their first AI model. The costs for compute resources like GPUs and tokens represent only part of the picture. The greater expense lies in the infrastructure side of AI. 💡 Industry analysis suggests this "infrastructure tax" can increase AI budgets by 75% to over 130%. For example, a model and GPU budget of $1 million could realistically translate to $1.75 million to $2.35 million once storage upgrades, data pipeline rewiring, and power and cooling improvements are included. 🤔 Boards should be asking what portion of AI spend goes to building and maintaining the underlying data platform versus actual model experimentation, and whether underfunding the platform is creating costly bottlenecks. 💰 Context is critical. Well-funded tech firms can weather this additional cost, while organisations led by cautious CFOs, particularly in low-margin sectors like retail, may find cloud-based AI rentals a more practical option than large upfront investments. This perspective reframes AI budgeting as a strategic, top-down decision rather than just a line-item for individual projects. Without a robust data foundation, isolated AI trials risk leading straight to bill shock. The cloud remains a useful environment for experimentation, enabling teams to validate concepts before investing heavily in infrastructure. More importantly, the conversation must move beyond simply counting GPU hours and token prices to include quantifying data readiness and infrastructure debt. Reflect on how much of your AI budget is actually consumed by those hidden infrastructure costs. AI first draft, fine tuning by me.

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Meta just inked a six-year, $10B+ deal with Google Cloud The implications, to me, are historic for cloud sector (multi-cloud) and for data centers, as the cloud race is now an AI infrastructure race. The message for hyperscaler data centers is clear: // Even the giants need each other -- multi-cloud is strategy now (btw google - openAI is another example). // AI workloads and data center Ops are rewriting the rules-- density, cooling, and interconnect are the new battlegrounds. // Long-term commitments matter, as does long-term operating strategy evolution. Predictability is how hyperscalers de-risk massive capital expenditures. // For PE-backed hyperscaler platforms, this is the moment: move faster, build AI-ready capacity, and become the backbone of the next decade. The $10B headline is big. To me, the infrastructure shift (industrial capacity building) behind it, is bigger.

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