Tag: ai
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The Five Barriers to Enterprise AI Adoption
AI is changing the role of the developer, shifting work from coding toward agent orchestration and higher-level architecture. While not the only area seeing results, developer productivity is where AI’s traction is most visible and measurable, particularly among individual developers, small teams, and greenfield shops free of legacy systems and layered processes. Enterprise adoption is…
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DeepSeek on the Horizon: Distillation, Defiance, and Clusters in Inner Mongolia
The industry is sitting on the edge of its collective seat as it awaits the release of DeepSeek V4 – which some are expecting next week. DeepSeek, you may remember, was the AI model that debuted a little over a year ago, rocking the AI world and tanking the entire market (not to mention giving…
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The 5 Tech Podcasts I Listen to (and You Should Too)
I listen to a lot of podcasts. Like many people, I listen in the car, in the morning while getting ready, at night before bed, and when doing things around the house. They run the gamut. Some are built around short stories, others take deep dives into individual songs, some are interview-based, others focus on…
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Infrastructure Is Eating the World: Circonomics and the Cult of “Bigger Is Better”
TL;DR: The market’s trillion-dollar AI boom is built on one assumption: whoever spends most on infrastructure wins. DeepSeek briefly exposed how fragile that belief is, and how quickly the system could unravel if it’s wrong. It could happen again. On the last Monday of January, markets lost over a trillion dollars in value. Nvidia alone…
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ChatGPT-5’s Short-Lived Simplicity: One Week of Clean Design
About a month ago I wrote about my frustration with ChatGPT-4o’s pull-down menu and its mess of mismatched models. I ended my post saying that I hoped ChatGPT 5 would address this. Last Thursday my prayers were answered. The pull-down was gone and with it the hodgepodge of models. In its place there was one…
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You Can’t Build an AI Strategy Without a Data Strategy
At their foundation, AI systems are massive data engines. Training, deploying, and operating AI models requires handling enormous datasets—and the speed at which data moves between storage and compute can make or break performance. In many organizations, this data movement becomes the biggest constraint. Even with better algorithms, companies frequently point to limitations in data…
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Why Storage Matters in Every Stage of the AI Pipeline
One of the companies that impressed me at AI Infrastructure Field Days was Solidigm. Solidigm, which was spun out of Intel’s storage and memory group, is a manufacturer of high-performance solid-state drives (SSDs) optimized for AI and data-intensive workloads. What I particularly appreciated about Solidigm’s presentation was, rather than diving directly into speeds and feeds,…
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Rethinking Monitoring: How Catchpoint Shifts Focus to the End User
At Cloud Field Day, I sat in on a presentation from Catchpoint, a company focused on digital experience monitoring. Their platform delivers real-time insights into the performance and availability of applications, services, and networks. What sets Catchpoint apart is how they’re reframing observability—moving away from infrastructure-centric monitoring and placing the focus squarely on end-user experience.…
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Fortinet CNAPP Review: AI-Powered Cloud Security and Composite Threat Detection
At Cloud Field Day 22, cybersecurity leader Fortinet shared its vision for managing the growing complexity of cloud-native environments. Their focus: enabling security teams to move faster, reduce alert fatigue, and make smarter decisions using AI-driven threat detection and automation. Navigating Modern Cloud Security Challenges In traditional data centers, firewalls protected predictable network chokepoints. But…