AI is generic until you tell it who you are. It fills the blanks with whatever is loudest, and so do we. This is a talk about writing down what you value so a decision stops defaulting, and writing down who you are so a machine stops guessing. Same move, two domains.

Line starts with what she stopped measuring: leaving a corporate career, taking a year out, and coming back without a scoreboard. From her own project data, not from theory, she built five filters for deciding what work to take on and what to continue. If you don't write down what you value, you default to whatever is loudest. The same thing happens to a machine.

She then shows, live, the personal knowledge graph she has built between herself and the tools she uses every day. If most of the skill base expires by 2030, what doesn't expire is knowing what you know and what you value.

In this talk she will unpack:

• The five filters she uses to decide what work to take on, and why energy is a real metric, not a soft one
• What AI assumes about you, and why generic output is not neutral output
• How to build a middle layer between you and your tools, so a machine stops guessing
• Three files to write about yourself, and why you should let AI interview you to write them