You’re reading The Steady Beat, a weekly pulse of must-reads for anyone orchestrating teams, people, and work across the modern digital workplace – whether you’re managing sprints, driving roadmaps, leading departments, or just making sure the right work gets done. Curated by the team at Steady.
Beyond Hype
The viral demos of AI building complete apps in hours are crashing into enterprise reality. While some companies boast AI writing half their code, most engineering organizations are discovering that large, complex codebases demand an entirely different playbook. Four industry veterans – including Kent Beck and Bryan Finster – paint a sobering picture: enterprises are repeating familiar mistakes, deploying expensive AI tools without training while developers inherit “thousands of lines of spaghetti code” from vibe-coding experiments. The real culprits? Missing context, absent workflows, and the curse of tribal knowledge walking out the door. One recent study shows AI dev tools actually slowing engineers down – not because the tech is flawed, but because organizations demand results without providing training. The fix isn’t more AI; it’s spec-driven development, shared context, and treating AI adoption as a systematic discipline rather than a magic wand. As Beck notes, misaligned incentives create a brutal paradox: “Coders could go faster? Oh, you mean you’ll fire half of us?” The bottom line: AI amplifies what you already have, good or bad. Without fixing your delivery bottlenecks first, speeding up code generation just creates technical debt at an unprecedented scale.
— Aviator, 5m, #ai coding, #enterprise engineering, #technical debt
Brain Drain
When AWS went down for most of Monday morning, taking banking, gaming, and half the internet with it, the culprit was predictably DNS – that perennial villain of infrastructure outages. But here’s the real plot twist: it took AWS engineers 75 minutes just to figure out what was broken. For a company that literally runs the cloud, that’s like a Formula 1 pit crew taking an hour to find the lug wrench. The deeper story? After 27,000+ layoffs and a hamfisted return-to-office mandate that sent senior engineers packing, AWS has lost the tribal knowledge that turns “everything’s on fire” into “check that weird system in the corner – it did this in 2019.” As industry observer Corey Quinn notes, this isn’t about old technology – it’s about new people trying to maintain complex systems without the institutional memory of those who built them. When you trade experienced engineers for “frugality,” you don’t save money; you just defer the cost until DNS decides to remind everyone why experience matters. The chickens, as they say, are coming home to roost – and they’re bringing cascading failures with them.
— The Register, 6m, #engineering, #leadership, #infrastructure
Trust and Teamwork
I had the privilege of giving a talk at the University of Maryland last week about what actually makes teams perform in our AI-soaked era. The keynote zeroed in on the digital team leader caught in the crossfire – you know, that product manager or engineering lead getting squeezed between C-suite demands for “faster with less” and their team’s desperate need for clarity and breathing room. Three pillars emerged: trust that’s actually earned (remember that old formula: credibility plus reliability plus intimacy, divided by ego?), the Goldilocks zone between micromanagement and anarchy, and context that flows like water instead of trickling down through endless status meetings. The USS Santa Fe story stole the show – a failing nuclear sub transformed when the captain stopped barking orders and started sharing context, asking crews to announce their intentions instead. We also called BS on “workslop,” that AI-generated fluff flooding our Slack channels and docs. Sure, AI can amplify great teams, but only when humans bring the insight and intent. Without those fundamentals, you’re just automating mediocrity at scale.
— Steady, 5m, #leadership, #ai, #teamwork
Beyond Architects
Netflix built one of tech’s most resilient architectures without a single person titled “Architect” for years—and that’s precisely the point, argues Matthew Hawthorne, a veteran engineer from Netflix, Twitter, and Comcast. Drawing from 25 years in the trenches, Hawthorne dismantles the conventional wisdom that good architecture needs formal Architects. His core insight: effective architecture isn’t about perfect technical solutions dreamed up in isolation, but about purposefully trading today’s problems for better problems tomorrow. At Netflix, engineers who proposed ideas also did the work, avoiding the white-collar/blue-collar split that plagues many organizations. When the company eventually introduced “Architects” who weren’t on-call and spent time brainstorming instead of doing operational work, the disconnect became obvious. The best architectural decisions come from those closest to the pain—not from those with distance from day-to-day struggles. Hawthorne’s most provocative observation: coding ability and architectural skill are entirely independent. He’s worked with brilliant system designers who wrote atrocious code, and expert programmers who couldn’t design coherent systems. The secret isn’t more process or better titles – it’s unifying people around practical solutions that work within real constraints, not theoretical perfection.
— The Pragmatic Engineer, 18m, #architecture, #engineering, #leadership
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