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Staff Too Scared of the AI Axe to Pick It Up — The Register / Forrester

• Forrester's AIQ metric — a measure of individual and organisational readiness for AI — shows adoption is lagging badly, and the reasons are telling
• Two culprits: employees aren't trained well enough, and there's an ambient anxiety about job loss that turns people away from the tools altogether
• My take: anxiety is lack of clarity — people fear AI substitution because they haven't mapped what they actually do every day, let alone identified which parts AI could touch
• The exercise I'd recommend before any AI training: write out your full task pipeline as if you were handing it to an intern — inputs, outputs, sub-tasks, decision points, all of it
• Then ask three questions for each task: is it repetitive? Is it unfulfilling? Can AI do it well? Only when you get three yeses should you consider delegating it
• Most people will find AI touches maybe 5–10% of their work — and that realisation alone does more to reduce fear than any company-wide AI rollout

The Ground Is Shaking — Why Designers Must Flip The Script on AI — UX Collective

• Peter's article is one of the best things I've read on this topic — he frames the core question not as "what can AI do?" but "why are we doing this in the first place?"
• The concept at the centre: Vygotsky's "more knowledgeable other" — the figure who can see both where a learner is and where they need to get to, and who scaffolds the gap
• Silicon Valley's message to designers right now is: AI is your MKO — let it guide you
• Peter's argument, and mine: it should be the other way around — we are the masters of purpose, goal, and constraint — AI is the skilled executor, not the director
• Language is our current interface with machines, but not everything we conceptualise is linguistic — spatial thinking, embodied experience, tacit knowledge — AI can have theoretical knowledge about gravity, but it will never feel it
• The choice isn't whether to use AI — that's settled — it's whether you define the parameters or just accept the outputs — whether you build the floor or keep asking why the ground is shaking

A Human Approach to Agentic AI — UX Collective

• Christine's experiment: using a multi-agent AI system to write a book — editor in chief, sales and growth, voice, product, reader advocate — all as sub-agents receiving context and iterating
• I find this genuinely fascinating as an experiment in approximating human team work with AI
• But I'd push back on one thing: at what point does the context engineering required to replicate a human editor in chief become so large that you'd have been better off with an actual person using AI?
• There's an asymptotic relationship here — the more you try to replicate what a human does, the more documentation you have to keep feeding the model as the work grows
• My real question: how does the output compare to a human collaborator who is also using AI? That comparison is the one worth running

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