I end by observing that LLMs currently really lack ownership. Though it wasn't my initial motivation for the essay, my current guess is that ownership is one of the most AI-resistant skills you can develop.

Frame 4: Ownership as project managing poorly-scoped work. This is about turning fuzzy work into concrete, trackable actions while maintaining a clear view of the right end-state.

Frame 3: Ownership as justified autonomy & trust. Building this is not about having every answer, but recognising gaps in your knowledge and being well-calibrated on when to check in versus when to move forward.

Frame 2: Ownership as default intellectual labour. When something needs to happen – whether giving feedback or solving a problem – you proactively shoulder more of the thinking work that's needed to go from observation to action.

Frame 1: Ownership as manager leverage. It's about making interactions with your manager efficient and making yourself legible. Treat the manager time you get as the valuable resource it is, and get as much out of it as you can.

Ownership is clearly important for career progression, but it's hard to articulate what it looks like or how to improve it. In the post I describe four different ways of thinking about ownership, what it looks like to do well, and how to improve.

Great prompt engineers are good prompt engineers who mutter "skill issue" to themselves whenever they get a bad response, before editing the prompt and trying again.

Good prompt engineers:
- give specific examples
- provide relevant context
- know how to specify exactly what they want
- lean into the strengths of LLMs
- use different models for different situations
- ask LLMs for help improving their prompts

Because a whistleblowing function that won't get used is no function at all. Read the full post: open.substack.com/pub/lawsen/... 7/7

• Clear reporting procedures that they *already* know
• A process that feels normal rather than exceptional
• Protected channels that they and others can justifiably trust. 6/7

Imagine working in an AI company at crunch time. The pressure to stay quiet is going to feel enormous when your evidence isn't conclusive and raising concerns could delay an exciting launch. At minimum, I’d want someone in that situation to have: 5/7

Without this clarity, you end up with an implicit norm of "if you trust your colleague, why would you mention it?" But this means:
• People who are friends with colleagues will self-censor
• The *felt* seriousness of sharing information becomes much higher 4/7

As a teacher, I experienced a system where reporting concerns was frictionless and expected. Contact details were on everyone's lanyard, and the process was regularly reinforced. This made reporting feel like a normal part of the job. 3/7

In my new post, I argue that whistleblowing systems must be universally known and psychologically easy to use – not just technically available – if we’re going to rely on them. 2/7

What Claude projects are you using? Any clever setups I should know about?