Nemesis follows hubris

When you think you’ve got it right, something will come along to show you how wrong you are.

More on principles

I believe in being principle driven, but have often struggled to explain why it works. Now I have some direction for that explanation. It comes out of the mathematics of chaos and the idea that simple rules can create complex patterns. So, good principles are simple rules that set out how a pattern will develop but don’t define or constrain it. John Cutler’s “Start together” is a good example. It sets a pattern of working collaboratively, involving different perspectives, creating shared understanding, but it doesn’t say who should be involved, how they should work, etc.

Team history

Do team’s remember what they’ve done? Do they ever think about what wouldn’t have happened if they hadn’t done it? Maybe ‘lifetime shipped value’ should be remembered more. Maybe it shouldn’t always be about the next big win, but accumulated small wins over time.

The end of classical statistics

I was reading some articles on how LLM’s are doing things that classical statistics says they shouldn’t be able to. The articles didn’t go into the implications for the field of statistics but it made me think, wouldn’t it be cool if AI led to a new understanding, a new paradigm for statistics. Classical statistics would be history, just like classical geometry, and whatever comes next would be part of a new way of seeing the world. That’s the kind of unintended consequence that AI could lead to that isn’t really about AI but about the world it creates.

Psychological Safety Grows-up

Although I’m not really a fan of maturity models, this is post from Paul Byrne is interesting for helping to show how much further there is for teams to go.


Investment in teams and work is about more than time and money. Mostly, it’s about people’s attention that you need to get them to invest.

Zero-to-one problems

Ha! Zero-to-one problems are easy. You should try minus-ten-to-one problems.