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“Don’t freak out, I cloned you.”
That’s what I told Natalia Quintero, my boss at the time, after I distilled a year of her Slack messages into a file called natalia.md and used it to simulate whether she’d sign off on an email I was about to send a client. She didn’t freak out—in fact, she said it was her favorite call of the day.
I had just started in a consulting role reporting to Natalia, and I was worried I didn’t know how to communicate with corporate clients. Our clients were large enterprises, but I had only ever had experience working with startups. Natalia, meanwhile, had years of corporate and banking experience. I knew my tendency to write emails like I write tweets might not be professional enough for this context, but I didn’t want to be asking Natalia to check my emails multiple times a day. She had better things to do, and when she did give me advice, she was often repeating herself.
AI can produce more drafts, proposals, and emails than anyone has time to read, so the scarce thing at work is the time of a person with judgment who can say, “Yes, send it,” or “Try it another way.” Those people spend a lot of their day telling different people the same things, when they could instead be spending their time doing something more productive. Since AI is good at role-play, I figured it could help take some of that burden off my colleagues by capturing their judgment without me having to bother them.
So I started cloning the people I needed sign-off from, hoping a simulated colleague could stand in for the real one. I found the virtual versions of my colleagues are great at learning someone’s rules. They just can’t bear to break them, and knowing when to do so is the part you still need the person for.
Cloning my boss to review an email
I used SynthTeam, an open-source library that turns a colleague’s Slack history into a persona file, and had Codex pull a year of Natalia’s messages from public channels (no direct messages or private channels) for situations where she’d made decisions and reverse-engineer how she made those decisions. (I didn’t ask her first, but I knew she’d be fine with it.)
I gave GPT-5.5 Natalia’s persona file ( and one goal: Improve my drafted email until simulated Natalia rated it 10/10. I set it up with the /goal feature, which keeps re-prompting the model every time it thinks it’s finished until the goal is met. I told it to use a fresh subagent for every rating so it couldn’t peek at the previous scores.
The email was to a contact at a client, asking whether any of three dates work for an event we’d co-host and requesting feedback on the pitch for a consulting engagement. Here’s the original, as I would have sent it, with the client’s details removed:
Two things:
Do any of these dates work for the event? April 27 or 28, or May 7 Format: I talk for 30 mins on a DSPy-related topic (I think optimizing skills), you talk for 30 mins (on [your product]? something else?), then we both do a fireside chat style Q&A together after. I think it will be at civic hall in manhattan but just figuring that out. I name the session something without agentic buzzwords so we attract the right people.
Would love feedback on the below pitch we’re going to market with for our AI embedded consulting bundle (it may not be right for [the client] right now but just more broadly what do you think is compelling / needs work?)
[six-paragraph description of the bundle]
Simulated Natalia gave it a 6/10. “The bones are good,” she said, but it “reads a little too stream-of-consciousness,” and she quoted my own “just figuring that out” back at me. That was fair, and it was exactly the advice the real Natalia would have given me.
Then the AI started revising and scoring the email over and over. It ran a whopping 73 rounds of optimization, rewriting the email and getting cloned Natalia to grade it. It managed to get a 9/10 by round 16 but stalled there, and never hit 10. The agent spent the remaining rounds applying Natalia’s rules harder, with more formality, reassurance, and ways to make it easy for the client to say yes. Here’s a slice of where it landed:
This would not be framed as you representing [the client], and nothing would be recorded, attributed, or promoted.
The room is being curated for senior operators with active internal AI rollout responsibility: people who own workflow change, enablement, governance, budget, or team adoption decisions, not vendors or public attendees.
Would any of these dates be plausible enough for me to send the attendee list/profile and exact intro? This would only hold the option, not commit you. You could decline after reviewing the audience and framing. […]
It goes on like that for another 250 words, and it reads like it was drafted by someone’s lawyer. Along the way it dropped the consulting proposal entirely and changed the event: My two 30-minute talks and a fireside Q&A became a small private discussion, with my contact dropping in for 10 minutes, plus promises I hadn’t made—an attendee list within 24 hours, an introduction he could veto, and a one-page brief.
I chalked this one up as a loss. I had given it one goal—Natalia’s approval—and it pursued that at the expense of everything else, including my voice. Every change it made followed from something Natalia believes, but none of them was something she would have told me to do. The clone could tell me what she’d object to, but not when an objection shouldn’t matter.
Cloning an executive before an interview
I used a similar method, this time for rehearsal rather than sign-off, before I interviewed Kyle Daigle, the COO of GitHub, at Microsoft Build. This was high stakes for me: It was the first time I’d done a big interview, and I wanted to feel prepared. I cloned Daigle from his public writing, talks, and interviews and roleplayed the interview with the persona to see which of my questions got interesting answers.
The Daigle clone also helped me learn about the real Daigle. Asking his persona questions was a targeted and more interesting way to get to know him than reading everything he’d written or that had been written about him. This works well when the person shows up frequently online, as Daigle does, because the model has seen plenty of the material in its training data and can find information online when constructing the persona. It works less well with people on whom the model doesn’t have much information.
Afterward, I compared the persona’s answers with the real Daigle’s. Two of its answers were strong matches, four were partial, and six were misses (I wrote up the full comparison). The miss that stuck with me was the persona’s habit of reaching for a framework. Daigle has said publicly that he makes decisions with a must/should/could framework, and the persona latched onto it. It used must/should/could in five of my 12 questions. The real Daigle didn’t mention it once. Having a favorite framework doesn’t mean bringing it up in every conversation. The clone was like a colleague who’s read your book and won’t stop quoting it back to you.
I told Daigle about the clone during the interview, and he loved it. Afterward he said the simulated interview was “pretty good,” but that it “overindexed on my written work rather than my spoken interviews and podcasts.” That would explain how a framework he’d described publicly showed up in five of the persona’s answers and none of his.
Cloning our CEO to work through a problem
The third example was something I was struggling with. Dan Shipper wanted to put ratings on our Vibe Checks so readers could see what we mean when we say we prefer one model over another, and I was worried that anything called a benchmark with fewer than a couple of hundred tasks would be false precision. I built a persona of Dan from his public writing and ran the same loop to find a path forward with him on my quantitative Vibe Checks proposal. It took eight rounds, not 73, and reached a 10/10, though this was a different task from the emails I optimized with Natalia’s clone, and I used a newer model, GPT-6 Sol. It even challenged the numbers in my proposal. But the clone tried to make the ratings more rigorous by adding more process: a 12-task pilot, reviewers who wouldn’t know which model produced each answer, and a 900-word set of instructions that another AI reviewer judged too dense to send.
I found this much more useful, but it didn’t capture what the real Dan did when he saw my proposal: Reframe the question. In real life, Dan suggested we change the name to avoid association with academic benchmarks (we landed on “checks”). He also pointed me to wine critics and Wirecutter, who put numbers on editorial judgments without pretending to have run hundreds of tests. The end result was better than the idea I had before, and we didn’t have to choose between false precision and no numbers at all. The clone had stayed inside the frame I gave it—the real Dan stepped outside it.
That turns out not to be a one-off. Separately, Dan tasked me with building a proper clone of him, a skill called Dan Lens that we gave our company agent, so the team could use his judgment when he’s not around. When we compared his real replies on 201 Slack threads with the Dan Lens prompt’s responses, the model kept consistently failing to predict Dan’s actions.
When we looked closely at the responses, we found that the real Dan often questions the assumptions behind what he’s being asked—he likes to reject the premise entirely. He goes along with the way a thread is framed only 34 percent of the time. The rest of the time he pushes back, redirects, adds an idea of his own, or asks for more before he’ll answer. The model goes along 68 percent of the time—twice as often.
We’re still working on Dan Lens. Dan was overjoyed to find out that the data showed his views are remarkably consistent: We couldn’t find a single example where he had contradicted himself! There are rules about his decision-making the models should get better at learning. But it’s refreshingly humanizing to learn that you can’t easily be cloned, because we still need Dan’s judgment of when to break the frame.
What’s left for us humans?
On my way back from a trip to San Francisco, dreaming of cloning more colleagues, I got a lesson in what the AI clones were missing. After sitting in San Francisco traffic in a Waymo that was too timid to barge through, I had the exhilarating experience of a New York yellow cab driver putting his foot down and swerving in and out of traffic. He got me home in half the expected time. He broke some rules but knew when and how to do so. The clones I built drove like the Waymo.
Natalia’s reaction was different from what I’d built the file for. Her first instinct was to read it and learn about herself. It showed her how she comes across at work, and she wanted to keep it for days when she’s not at her best, so she can check herself against her own standards. The file has all her rules in it, though she’s still the only one who knows when to break them.
Cloning your colleagues seems odd today, but I think it will soon become the polite thing to do. Roleplaying a high-stakes interaction before you have it shows you’ve done your homework, gets all the obvious things you should know about them out of the way, and saves everyone time. You can walk into the meeting prepared for what your boss might say. The meeting is for finding out how she’ll surprise you.
Mike Taylor is the head of evals at Every. To read more essays like this, subscribe to Every, and follow us on X at @every and on LinkedIn.
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