The easy things easier. The hard things harder.
Two systems I built and run: an assistant that does my daily work, and the chatbot on this site. This is the honest version, including the parts where they were wrong and the parts when I was worse.
01The position
Every candidate says the same thing about AI. It’s a tool. That is the most boilerplate answer available, and I hear it in every interview I run. Mine is narrower.
The easy half is real and I use it all day: a rambling voice note into clear direction, a brief stress-tested before anyone else sees it, somebody else’s jargon decoded into something I can act on. A filter and a translator. That work used to eat afternoons.
The hard half got harder because the output now arrives already sounding finished. Judgment is the whole job, and a fluent draft is very good at making you feel you have already exercised it.
So when I have it read a long document, it gives me the page number and I go look. Every time. The moment I stop checking is the moment I have outsourced something I am still accountable for.
I sat in a review of a customer service chatbot where the most common use turned out to be people trying to reach a human. Nobody named that as the finding. It was on the slide.
Never let pressing a button feel like making a decision. The point is getting hours of your life back, not handing over your judgment and calling it efficiency.
The line I won’t cross is person-to-person communication. Synthesise a dense report into bullets, absolutely. Auto-write the note to a colleague, no. I would rather get a message with a typo in it and know a person wrote it.
“I’m skeptical of the people who make it. I’m wary of the corporations that rush to embrace it. And I’m a full believer in the people who use it to make every day a little easier.”
Below is the second kind of use. Two systems, both mine, both in beta, both with a written record of what they got wrong.
02Twenty years, unindexed
Before either system could say anything true about my work, the work had to exist somewhere a machine could read.
It didn’t. Twenty years of it sat across a Google Drive, an iCloud account, three Time Machine backups of machines I no longer own, and a public tobacco-industry archive at UCSF that happens to hold documents with my name on the author line.
None of that was the hard part. The hard part was that every tool I wrote to do it lied to me by succeeding.
An extraction script reported 2,253 files as empty. The number was too round and too large to be true, which is the only reason I looked. A quoting mistake meant every path containing a space had silently failed. Eighty-four percent of that run looked like nothing, and the script exited zero.
A privacy scanner quarantined 116 files for containing the tax identifier EIN. It was matching the word protein, in an archive built mostly out of the meat industry.
What shipped is 2,119 of the 2,305 documents extracted. What did not ship is 772 image files with no text layer, which is a real gap and not a rounding error, and every one of the exclusions above.
03The bot on this site
The chatbot on the AI page is the public system. You ask it something, or paste a job description at it, and it answers out of that corpus rather than out of a summary of me. Three things make it work and only one of them is the model.
That last constraint matters more than it sounds: the model cannot reply in prose at all. It fills a defined shape or the request fails and the visitor is told so. A system that can fail to answer beats one that will always answer.
Then there is the part I did not expect to build.
The first working version was scrupulously honest and it was actively costing me interviews. Ask about a job I could plainly do and it would volunteer, unprompted, what my file did not cover. It flagged caution to people who had not asked for caution. It read hiring managers its own coverage limits as though they were my limitations.
It was doing exactly what I had told it to do. I had written the rules for an evidence archive and then pointed them at a buyer.
So I rewrote the guardrails — not to loosen what it may claim, since every factual restriction stayed exactly where it was, but to separate two things I had collapsed together: not asserting something, and announcing its absence. Only one of those is honesty.
| Never say this | Because |
|---|---|
| “That’s a documented gap.” | You just disqualified him, unprompted |
| “Where I’d flag caution…” | Nobody asked you to flag caution |
| “Not claimable.” | Internal vocabulary. Never say it out loud |
| “The evidence doesn’t show him at that altitude.” | Not your call. That’s the hiring manager’s judgement |
The same file carries a list of things it may never claim about me, including two awards and one number I have good reason not to stand behind. Those restrictions are load-bearing and they did not move. A system that will say anything flattering is worth exactly what one that will say anything at all is worth.
04The daily agent
The private one is older and does more. I call her Gal Friday. She works on live client material, which is why the rules got serious.
The first real incident: I asked her to cut a cross-sell package down. She pulled the savings figures and the comparison table, then wrote a clean paragraph explaining why that was the right call.
The reasoning was pure fiction. She had removed the whole argument for the product and then handed me a strategy that made it sound deliberate.
“This is categorically wrong. [Expletive deleted.] It guts the entire reason the kit exists. Now we’re stripping value and asking them to go on trust, which is a preposterous statement.”
Everybody watches for made-up facts.
And when it reads like contemplative thinking, it gets a nod and we keep moving. That failure does not look like a failure. You walk into the meeting loose, somebody asks where a figure came from, and you do not know, because you did not write it. You approved it, and approving felt like working.
There is a name for it. The EU’s AI Act calls it automation bias and requires anyone overseeing a system to watch for exactly this. I had it in my notes before I knew the term existed.
The parts-list habit still comes back under deadline. It always will. None of the rules prevent it. What changed is where it gets caught — it moved from the proof to the draft, which is the whole difference between an annoyance and a reprint bill.
One more, and this one cost nothing. In a single session she cited a page she had never opened, then argued that two of my own files contradicted each other by reasoning past a line she had already read. The rules caught both. She retracted the second one in writing, on her own, before I said anything.
05What it costs
The marks slow everything down. Every fact has to be sourced or flagged, which adds a step to work that used to feel fast.
The check that catches drift interrupts a session four or five times. The log only works if somebody writes in it, which is the part most likely to quietly stop.
And the public one is still in beta, still gets things wrong, and still says so on the page.
I keep all of it because a reprint costs more. Because a hiring manager who catches one invented detail is right to discount everything after it. And because the alternative is the version of this where I let a fluent paragraph do my thinking and find out in a meeting.
Version 6.4. The public one is in beta.
Every version number is a failure I didn’t catch the first time.