Sharon Park in the morning is not a dramatic place. There’s a duck pond, a stand of oaks that go gold too briefly in November, and a loop I’ve walked enough times that my legs know it better than my eyes do. It is, in other words, exactly the kind of place where a person starts talking to himself. Not out loud. In the productive, low-grade way — turning a sentence over, arguing with an idea from the day before, checking a thought against something you believe about yourself.
I think in five years I’ll be doing that walk with something else along. Not a search engine. Not another chatbot trained to know a little about everything and a lot about nothing in particular. Something closer to a second set of eyes on my own life — a reasoning engine, lean and mostly private, that has actually read the things I’ve written and doesn’t need me to explain who I am before it’s useful.
Here’s the distinction that matters, and it took me longer than it should have to see it clearly. The AI industry has spent years in an arms race over how much of the world a model can hold — more facts, more languages, more of the internet compressed into weights. That race will keep going, and somebody else can have it. What I want is smaller and stranger: a model that knows comparatively little about the world and quite a lot about me. My core values document. The portfolio spreadsheets. Fifteen years of blog posts. The half-finished notes for the I-280 project, sitting in a folder, waiting for someone — or something — to ask the right question about them.
I spent a career in payments infrastructure, which means I spent a career thinking about a very specific kind of trust: the kind where a stranger’s system has to make a judgment call, in milliseconds, about whether to say yes. Fraud models don’t work because they know everything about commerce. They work because they know an enormous amount about one account, one pattern, one person’s ordinary Tuesday — enough to notice when Tuesday stops being ordinary. That’s the architecture I keep picturing, aimed inward instead of outward. Not a system trying to know the world. A system trying to know me, well enough to notice when I’m drifting from what I said I cared about.
I can already feel the shape of the mornings this would change. Right now, when I sit down to look at RMD requirements against the tax picture, I’m doing the translation myself — pulling numbers into a story I can actually feel the weight of. A reasoning engine grounded in my real holdings wouldn’t just run the scenario. It would know that I don’t want the scenario dressed up as a spreadsheet; I want it dressed up as a conversation, unhurried, the kind you’d have over lunch with someone who already knows the whole situation. And on the mornings when I sit down to write, instead of staring at a blinking cursor and a blank page that has no idea I exist, I’d be handing a draft to something that has actually read my last two hundred posts and knows the difference between the sentence I’d write and the sentence I’d cut.
None of this is especially exotic technology. Apple and Google are already building toward it — Neural Engines fast enough to do real reasoning on-device, retrieval systems that can reach into your own files instead of the entire internet, fine-tuning that’s getting cheap enough to personalize rather than merely customize. The more interesting story here isn’t privacy, though privacy is real. It’s architectural: what happens when the expensive, impressive part of the system — the part that knows everything — becomes optional, and the cheap, personal part — the part that knows you — becomes the whole point.
What I don’t yet know is what this will cost me. A tool that reasons this well about my own life is also a tool I could lean on instead of doing the leaning myself, and there’s a version of this future where the walk around Sharon Park stops being mine and starts being a conversation with something that finishes my sentences a little too well. I’d want some way of knowing, plainly, what it’s drawing from and what it’s guessing at — less a nutrition label than a kind of honesty I could check against, the way you’d check a fraud model’s confidence score before you trusted it with a yes.
But most mornings, I think I’d take the trade. Not because I want to think less. Because for thirty years I’ve been collecting the raw material — the notebooks, the portfolios, the half-built essays — and it would be something, finally, to walk beside a mind that had actually done the reading.