And that’s just the beginning. Instead of predicting what you will do, it helps discover what you could do, making life ever more expansive. Potentiation over prediction. If an intelligence like this were to solve my neck pain, I would receive genuine recommendations instead of paid ads. I most certainly would’ve found an ideal pillow by now, instead of still being on the market after nearly two months (I am about to return another pillow, again). It could also be a diagnostic partner. It might say: “I’ve noticed your neck pain flares up on Wednesdays, which correlates with high screen time and low sleep score. Let’s explore a 10-minute stretching routine, a blue light filter for your monitor, and maybe that breathwork app you downloaded six months ago but never opened.”
Let’s get even crazier. What if we can be fed just the right Substack essay to open our lives? The right hobby we can fall in love with? The right people to befriend? The right city to live in? The right relationship advice? The right health tip? It’s a very possible future, in which everyone has a personalized serendipity machine for intentional living.
For the last three months, a friend and I have been tinkering in this problem space, working on a personal intelligence experiment we named Hue. As we used it ourselves and shared with friends, a few types of outputs stood out.
With every prompted or unprompted query, it was impossible to predict what the agent would uncover from our data. It became obvious that the element of surprise is the true hallmark of understanding and resonance.
The quality, ingenuity, and wow factors will only accelerate as datasets scale and the way LLMs engage with them become more expansive. The true power is the ability to transform raw materials into artifacts for personal myth-making that satiate our endless curiosity and quest for interestingness.
When a personal intelligence has relevant data, it can act as your shadow to interact with others who have questions your data can answer. If you are a foodie with a reputation, you can charge for recommendation requests at a price point too low for your effort but enough to justify putting your AI on the job. Plus, you’d get to gate-keep what’s public and what isn’t.
Personal AI splits into two distinct roles: thought partnership (helping a person think through what they care about) and automation (handling what they’d rather not think about at all). The design question is where each boundary sits — and whether the tool makes that boundary legible to the user. ai-ux
