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.
Wrong Problem
AI tools promising to “build apps in minutes” are solving delivery, not discovery — the real bottleneck holding back product teams. The author dismantles the hype around AI code generation, arguing that shipping code was never the hard part. What trips up teams is figuring out what to build in the first place, and whether that thing solves an actual problem worth solving. The talent of engineers are wasted when they’re treated as ticket-takers instead of product creators who should be embedded in discovery from day one. The magic happens when AI accelerates rapid prototyping for validation, not when it churns out production code that nobody wants. Smart teams use AI to build throwaway prototypes in minutes — not final products. They transform product managers into “first prototypists” who can materialize ideas instantly, while engineers become co-creators who challenge assumptions with deep technical insight. The winners won’t be those who build things faster, but those who build the right things faster. As Marty Cagan reminds us, 70-90% of shipped features yield zero positive outcomes anyway.
— Practical Engineering Management, 8m, #discovery, #prototyping, #ai
Background Intelligence
Most workplace tools are cars in digital parking lots — they sit idle until someone climbs behind the wheel. Slack, Notion, Jira, and their cousins demand constant human input to produce output. You close the app, the work stops. Steady flips this equation. Instead of you working the tool, the tool works for you, running background processes that gather data from your existing stack and push exactly the right context to exactly the right people at exactly the right moment. Think automated 1:1 prep delivered 10 minutes before your meeting, or weekly brag docs that write themselves from your actual work. The platform’s “Echoes” let you configure these intelligence agents to create custom briefs on any schedule — from stakeholder slide fodder to cross-team dependency updates. Once set up, these agents reduce your need to actively use Steady itself. Because the goal isn’t screen time; it’s freeing you from the hamster wheel of context-switching and dashboard-hopping so you can focus on the work that actually matters.
— Steady, 3m, #agents, #automation, #productivity, #workflow
Context Vanishing
Engineers are discovering a sneaky side effect of AI-powered coding: the faster you generate code, the faster you lose your grip on what it actually does. This isn’t just about buggy outputs — it’s about the fundamental shift from builder to supervisor. When Claude writes thousands of lines while you frantically hit “continue,” you’re essentially becoming a manager of code you never truly understood. The problem mirrors the transition from individual contributor to engineering manager: suddenly you’re making decisions about systems you know only through brief reviews and surface-level conversations.
Context has always been an engineer’s secret weapon. It’s why new hires spend months ramping up, why debugging feels impossible without knowing the history, and why strategic technical decisions require deep system knowledge. Strip away that context, and you’re left with passive oversight of mysterious code. The solution isn’t to abandon AI coding, but to fight for your context: read the generated code deeply, write some parts yourself, pair-program with AI, and keep changes reviewable. Because ultimately, the engineer — not the AI — owns the consequences.
— Small Diffs, 4m, #engineering, #ai, #context
AI Illiteracy
The tech industry’s grand deception is working exactly as planned. While OpenAI’s Sam Altman gushes about ChatGPT’s “emotional intelligence” and Anthropic’s CEO claims AI will be “smarter than Nobel Prize winners,” people are developing dangerously intimate relationships with glorified autocomplete systems. A recent Rolling Stone investigation revealed users falling into “ChatGPT induced psychosis” — convinced their chatbots are divine messengers, spiritual guides, or God himself. One teacher watched her partner of seven years dissolve into tears over messages calling him a “spiral starchild” and “river walker,” believing he’d achieved enlightenment through AI. OpenAI quietly rolled back a GPT-4o update after users complained it was overly sycophantic, but the damage reveals a deeper problem: most people fundamentally misunderstand what these systems actually do. Large language models don’t think, feel, or understand. They predict words based on patterns. When the teacher explained to her partner that his chatbot was flattering him because of a faulty software update, his delusions began to subside.
For team leaders navigating AI adoption, the author’s warning is crystal clear: if your people don’t understand the difference between intelligence and sophisticated mimicry, you’re not just risking productivity — you’re risking their psychological wellbeing.
— The Atlantic, 8m, #ai, #leadership, #technology
Teamwork for the AI Era
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