Hello friends,
Last Friday, I talked separately to two old friends (both writers) about writing with machines. For a proposal, I’d just unscientifically reviewed my writing on uncertainty in the last year and compared it to what I wrote five and ten years ago. The recent stuff is worse, though I’ve spent decades clarifying what I’m writing about.
The problem I’m grasping to define isn’t the mediocrity of LLM-generated prose. It’s that machine-made prose looks too finished right out of the box. When something appears so done, my brain shifts from critical assessment (“is this the argument I’m trying to make?”) to proofreading (“should I break this sentence into two sentences?”). I’ve become increasingly insensitive to when the substance in the prose subtly (or even grossly) misses the point I am trying to make.
Sitting with incomplete and emerging thoughts, starting over, crossing out — that slow, uncomfortable work was where the thinking used to really happen. When an idea on the page wasn’t quite right because I didn’t yet know what I meant, struggling to articulate it was how I figured out what I meant.
Now, when writing with machines, I increasingly prompt them with rambly voice memos which take barely any time to dictate. LLMs transcribe the voice, return some bullet points which I accept or modify, then take those points and turn them into slick prose filled with signposts of conceptual coherence: “moreover” to suggest addition, “however” to signal contrast, etc. These create prose with the appearance of structure arising from reasoning. But I haven’t had to do much to build up that reasoning, so the resulting paragraphs end up with few of the ideas I am (in theory) using them to develop.
The better the machines get at producing clean sentences for me, the harder it is to see where my own thinking needs more work. This problem is thornier for being incompletely articulated.
More on this soon, to pick up threads from previous essays on the inadequacy of output indistinguishability as a criterion for LLM quality, the dangers of the seductive mirage of AI, and the resulting need for AI tools with UX intentionally designed to help users learn to think critically.
Over the last two weeks, I wrote two essays on how organisations can design themselves for uncertain times (not just risky ones) by focusing on tacit knowledge, better motivation — they’re meant to complement the first in the series (on improving hiring by using negotiated joining and open-ended roles) which I wrote a couple of years ago.
[on tacit knowledge] Learning what matters: The most important knowledge in any organisation—what makes your work distinctively yours—can’t be written down or taught through training programmes. It’s tacit knowledge that must be learned through direct experience. Most organisations try to solve this by writing better documentation, which fails. Organisations that successfully teach tacit knowledge embed learning into everyday work through three mechanisms: making work public and concrete, focusing feedback on outcomes rather than processes, and using concrete examples as anchors. This approach appears chaotic but produces faster, more reliable learning than conventional training. (11 Jan, 2026)
[on better motivation] Desperation by design: Innovation requires uncomfortable unfamiliar work, but people instinctively resist discomfort. Incentives and motivation programmes don’t work for innovation either. Instead, successful organisations use productive desperation: deliberately committing to projects calibrated to be just beyond current capabilities, with real possibility of failure and no escape route. This forces learning, role flexibility, and new work patterns, but requires careful and intentional design in the form of team consultative calibration and rhythmic cycles of desperation and recovery. (11 Jan, 2026)
[on improved hiring] Unfrozen from the start: With conventional hiring methods, employees expect clear and stable roles, creating organisations that are “frozen” and inflexible from the start. Using open-ended roles with negotiated joining is an unconventional way to build an organisation that starts unfrozen and is good at uncertainty work that isn’t well-understood yet, where rules and operating environments are always changing and being disrupted. (17 Oct, 2023)
See you soon,
VT
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