A picture is worth 10K words - but only those to describe the picture. Hardly any sets of 10K words can be adequately described with pictures. --Alan Perlis
Y = the contribution of the system they work within
[XY] = the interaction of the individual with the system
8 represents some measure of productivity, e.g., rate of errors, millions of dollars in profit, whatever you're measuring
The person who can solve for X is competent to rate people on their performance.
What to do instead of (destructively) rating people?
Build better systems for doing the work, make their work easier, give them psychological safety and job security so they can relax and enjoy their work and share better methods with each other.
CTRL+F “Deming” - Thank you. Organizations with vitality reason about the operation of the whole system, not simply the performance of actors in isolation.
Not sure what you guys are trying to say here but I have seen, with my own eyes, large enterprise software systems that end up being so fragile it makes you laugh. The incumbents in this space make dinosaurs look like spring chickens by comparison.
There are millions upon millions of dollars being spent on stupid, outdated software that simply does not work. The main thrust of my original comment was that - "you thought open source was impressive? wait until the rise of AI powered open source". I think the highly arcane world of enterprise software is similar to being a travel agent in the mid to late 90s.
I think that if you have the skills to manage a software project of any complexity then you, alone can create what teams are doing today.
I think AI can characterize proprietary data formats and assist by writing parsers, providing helpful error messages, and pointing out inconsistencies and gaps in the specification.
AI can eliminate the need for so much workforce. Rather than training existing workers, it can become the existing workers.
ChatGPT is already more competent than 1/2 of the workers at my last job. Case in point - two teams were independently assigned a particular upgrade task. Since the project managers used different terms they didn't realize they were doing double work. Large language models could already disambiguate that kind of thing way back then, it is only getting better.