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.
Human Advantage
While everyone else gets drunk on AI productivity theater – more emails! shinier dashboards! faster everything! – the teams actually winning are doing something radically different: they’re getting more human, not less. They recognize AI can assist and accelerate work, but it can’t replace sound judgment, real curiosity, or ethical discernment. What separates high-performing teams is their ability to use AI as a starting point, not a crutch. They ask sharper questions. They challenge assumptions. They make connections across functions and domains. One standout example: a SaaS customer experience head introduced “AI experiments” to sprint retrospectives, where team members shared weekly AI tests – successful or failed. This created a low-stakes, high-learning environment. People started volunteering ideas, sharing small wins, and building on each other’s discoveries. The result? Teams became more creative and collaborative, not just efficient. As AI handles the mundane stuff, the distinctly human skills – judgment, curiosity, ethics – become the actual competitive moat.
— Fast Company, 6m, #leadership, #ai, #teamwork
Probabilistic Era
The age of predictable software is ending. While traditional tech operates on reliable functions – input X produces output Y every time – AI has shattered this model entirely. Your users can now ask anything (infinite input space) and get unpredictable responses that change each time. This creates a fundamental mismatch: users expect consistent results but pay real costs for inconsistent outputs. Success now requires embracing uncertainty through “Minimum Viable Intelligence” – finding the sweet spot where models stay capable while meeting market expectations. Teams must transition from engineering to experimentation, treating each model update as a hypothesis requiring complete rethinking. Even “simple” improvements need statistical testing across user journeys, not binary pass/fail metrics. Organizations clinging to predictable dashboards will struggle while those embracing uncertainty will define the next era of technology.
Editors note: we believe that contextual briefs are far more powerful than dashboards here at Steady.
— Gian Segato, 12m, #ai-products, #engineering, #leadership
Vibe Coding Vibes
A seasoned developer reveals the telltale signs that code was generated by AI. It’s not the repetitive comments or excessive switch statements that give it away. It’s the complete disregard for existing project conventions and patterns that humans would naturally follow. When someone submits a pull request that reinvents the wheel (implementing HTTP fetching when there’s already a data layer, writing utility functions that exist elsewhere, or using classes in a purely functional codebase), you know they’ve been letting AI do the thinking while checking out mentally. The issue isn’t the AI assistance itself, but developers who’ve abandoned the principles of maintainable software development in favor of speed. We’ve spent decades establishing patterns and standards to build sustainable codebases, yet some developers are throwing these hard-won lessons out the window to speedrun development. We shouldn’t reject AI tools, but lets use them thoughtfully with better prompts, clearer descriptions, and adherence to existing project conventions.
— Alex Kondov, 3m, #engineering, #leadership, #quality
Feedback Debt
Your team isn’t slow because they’re bad at coding, they’re slow because they built themselves a highway to nowhere. When teams can’t verify changes locally and instead need to deploy to staging environments just to test a simple database query, they’re burning the equivalent of one full-time engineer’s worth of productivity daily. The math is brutal: 8 engineers x 10 extra minutes per verification x 6 attempts daily = 8 lost hours. That’s like paying for eight people but getting seven people’s work. The culprit? Technical debt that accumulates like interest on a credit card you forgot you had. Teams start with zero dependencies and gradually accept each compromise – “just this once we’ll skip the local setup” – until suddenly testing a GET endpoint requires a full deployment cycle. One engineer discovered colleagues spending 20-30 minutes per verification attempt when the same check could run locally in under a minute. The boiling frog effect means teams don’t notice their feedback loops getting slower until the pain becomes unbearable. Unlike code coverage, most teams don’t set targets for verification speed, so this productivity killer hides in plain sight until velocity grinds to a halt.
— Revontulet, 8m, #feedback, #productivity, #engineering
Teamwork for the AI Era
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