Future-of-Work

TL;DR Start with What Does Expertise Mean When AI Can Pass Any Exam? - the credential crisis Then What It Means to Be Expert in 2030 - where the speculation goes next The through-line: expertise is shifting from reference knowledge to judgement, accountability, and taste Read in order What Does ‘Expertise’ Mean When AI Can Pass Any Exam? What It Means to Be Expert in 2030 The Architect vs The Builder Taste Is the New Scarcity The Automation Paradox Career and pipeline Agent-First Architecture: The Engineer as Curator The Junior Developer Pipeline Problem Will AI Kill Coding Jobs? The Meaning of Work in an Age of Abundance Related Trust series - accountability when agents act on your behalf Securing AI Agents - the liability side of delegated work Related Reading AI Dev Tooling: A Reading Path for 2026 - how the tooling changes the practical skill equation day to day Four Futures: Reading the Signals - the broader economic scenarios these evolving roles exist within The Free Intelligence Era - why intelligence abundance reshapes demand for human expertise The Next Decade of AI - longer-horizon thinking on where expertise and AI diverge or converge

The “is the software engineer dead” genre has been running long enough that you can predict most of the takes before you read them. The interesting interviews are the ones where the person being interviewed is in a position to know something the rest of us do not. Boris Cherny, the creator of Claude Code at Anthropic, is one of those people. Sky News got him in front of three charts and asked him to react. ...

TL;DR Every time AI automates a specific task, the monetary value of doing that task falls - the scarce resource shifts from execution to the judgment of what is worth doing at all Historical precedent holds: Deep Blue did not kill professional chess, calculators did not kill accountants - automation raises the value of the thinking above the automated layer The new hierarchy of work puts judgment first (irreplaceable), direction second (human but scalable), and execution last (increasingly commodity) Judgment is constrained opinion - it requires trade-off awareness, skin in the game, pattern recognition, and willingness to be wrong - none of which AI can replicate The economic inversion means hiring shifts from paying for output to paying for prevention: the bad decisions not made, the features not built, the wrong paths not taken The automation paradox is quietly reshaping what we pay for. ...

TL;DR The work AI now automates - boring tickets, bug hunts, boilerplate - was the unspoken apprenticeship that turned juniors into seniors The skills that work built (pattern recognition, systems intuition, taste, calibration) are built by doing, not by reading - and that doing is now cheapest to delegate The new apprenticeship shifts toward reading over writing, debugging agent output, earlier architectural decisions, and deliberate practice of things agents do badly There is a coordination problem: individual organisations rationally skip junior investment in the short term, but the senior pipeline thins industry-wide a few years later If you are starting out today, optimise for proximity to a great senior engineer above salary, title, or any other variable The views in this post are my own personal reflections on the industry as a whole, written in my own time. They are not about any specific employer, team, or colleague, past or present. ...

TL;DR When AI handles execution, the jobs that survive are those built on judgement, empathy, embodied skill, accountability, and taste - none of which AI can fully replicate The five durable categories are: human relationship professionals, AI wranglers and system architects, skilled trades, creative producers, and human trust and accountability roles Skilled trades (plumbers, electricians, HVAC engineers) are particularly resilient because general-purpose robots that handle unpredictable physical environments remain a hard unsolved problem AI wranglers - people who set objectives, constraints, and guardrails for AI systems - are a new and growing category driven by regulation like the EU AI Act and NIST’s AI Risk Management Framework The meta-pattern is interpretation vs execution: AI excels at execution, so high-value humans are those who bring judgment and responsibility to the question of what should be built and why Assume AI is “everywhere” - what still needs actual humans? ...

TL;DR An “emergency debate” framing the case that AI agents will displace large parts of the workforce inside a 24-month horizon Contributors disagree on speed but agree the direction is settled - the question is which sectors move first, not whether they move Near-term pressure is on roles built around predictable, repeatable cognitive work; durable roles cluster around judgment, taste, and accountability Education and training systems are slower to adapt than the technology, which creates a real workforce mismatch in the meantime Worth watching as a snapshot of the 2025 conversation - useful frame even where you disagree with the specific predictions About This debate explores urgent questions about AI’s impact on employment and the workforce. Contributors discuss the timeline for AI-driven job displacement and the societal preparations needed to adapt to rapid automation. ...