Coté

Our interview for this week is up, it’s with Josh Berkus: Whitney and Coté discuss with Josh Berkus (Red Hat, Kubernetes contributor) how liberal and fine arts degrees (philosophy, photography, sculpture, pottery) apply to tech careers. Berkus details how early hardware experience influenced his database performance work, noting hardware’s renewed relevance with AI and multi-arch computing. The conversation covers Sun Microsystems’ 1990s internet role, internal politics, and its MySQL/Postgres strategy.

CFOs boost tech budgets and squeeze headcount, AI art may not be copyrightable, and agents expose your structural chaos. Also, slide fails, secret soap operas, and surviving BigCo absurdity.

Links! Wastebook! Background changes afoot! Potsdamer Platz bei Nacht, Paul Paeschke, ca. 1929.Related to your interestsWhy your DIY Kubernetes stack won’t survive the era of agentic AI How tech chiefs gauge ROI on AI - AI uses: better search, preparing leases, and this: “When the system senses that ice is running low, it fires off an order to AI agents, which begin routing trucks to fulfill orders. The system also takes into account historical sales trends, weather analytics and other factors to anticipate each route’s ice requirements.

Potsdamer Platz bei Nacht, Paul Paeschke, ca. 1929. Related to your interests Why your DIY Kubernetes stack won’t survive the era of agentic AI How tech chiefs gauge ROI on AI - AI uses: better search, preparing leases, and this: “When the system senses that ice is running low, it fires off an order to AI agents, which begin routing trucks to fulfill orders. The system also takes into account historical sales trends, weather analytics and other factors to anticipate each route’s ice requirements.

Even the platform engineers need to hide Kubernetes to get their job done: Kubernetes adds another layer of complexity for platform engineering teams, introducing architectural complexities that require a deep understanding of containers, networking, storage, and cluster security protocols. While it has become the default runtime for modern applications, managing Kubernetes at scale alongside existing VM‑based workloads can overwhelm platform engineering teams. YAML sprawl, cluster life-cycle management, networking dependencies, and security controls consume time that should be spent improving the developer experience and can lead to costly human error.

Most organizations treat infrastructure as a series of projects to be “completed,” but successful platform engineering requires a permanent product mindset. In this episode, we explore why platform teams need dedicated product management to balance competing priorities—like security, cost, and developer experience—and why the “why” scales much better than the “what” in large enterprises. We also dive into the often-overlooked role of designers in creating platform tools that developers actually want to use.

Enterprise AI apps (or lack thereof), ROI surveys, CFO budget pivots, agile’s stubborn relevance, platform engineering vs. private cloud, Heroku’s freeze, IRS tech cuts, and Europe hedging on AI tools Peter Klúcik's The Hobbit illustrations. Related to your interests Where are the enterprise AI apps? Part n + 1 - AI capacity demand is high, but it’s still “early innings” for enterprise AI use.

This week, we discuss personal AI hype cycles, bottoms-up adoption, and “The Modern Stack” simplifying cloud. Plus, thoughts on new cars and the dogs that ride in them. See the traditional podcast listing for links and more.