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The chain from "distill me" to "upload me" has two links that come apart: "distill a person into a narrow task policy" is an empirical question with 50 years of evidence behind it, while "upload the brain" and "is the result you" are open science and open philosophy.
The State of Brain Emulation Report 2025 (Zanichelli, Schons, Freeman, Shiu, Arkhipov) gives no mouse or human timeline. It says whole-brain single-neuron recording in mammals will likely stay extremely hard for the foreseeable future. Structure is ahead of dynamics:
| Milestone | Scale | Note |
|---|---|---|
| FlyWire whole adult fly brain (2024) | 139,255 neurons, ~50M connections | ~33 person-years of proofreading |
| Shiu et al. connectome model (2024) | same, on a laptop | predicted taste and touch responses only |
| MICrONS (2025) | 1 mm³ mouse cortex, >524M synapses | 1.6 PB, 7+ years |
| Mouse recording today (report) | ~1M neurons, ~1% of brain | 100M is the next-decade goal |
| Mouse connectome cost (E11 Bio, quoting Wellcome) | $7–21B, >95% proofreading | E11 targets 100x cheaper; a target, not a result |
Even the 302-neuron worm isn't emulated: in C. elegans 40–50% of synapses differ worm to worm, no simulation includes neuropeptides, and the report estimates ~8,400 worms' recordings would be needed against 284 today. A human connectome at 10 nm is 2–2.8 zettabytes of raw data.
The nearest real thing to "distill an upload to what's in distribution for my project work" is judgmental bootstrapping. Dawes 1979 summarizes it: linear models of 26 of 29 clinical judges beat the judges themselves (validity .31 vs .28), and models of admissions faculty beat the faculty (.25 vs .19). The model keeps the judge's stable policy and drops their day-to-day noise. The sting is that equal-weight models did about as well, so the judge's real contribution was choosing which variables matter and in which direction.
LLM-era versions measure attitudes, not work. Park et al. agents built from 2-hour interviews match people's GSS answers at 83–86% of their own two-week test-retest consistency (74% from demographics alone), but show no gain on economic games. Twin-2K-500 twins reach 88% of human test-retest yet are "more rational" than people: 4% of twins refuse a vaccine in an omission-bias item against ~45% of humans. Individual Maia chess models predict a specific player's moves well enough to identify them. I found no evaluation of an LLM clone of someone's professional judgment.
So what's "in distribution" for one person's project work is probably small: which features they attend to, which direction they lean on each, their taste vetoes, and their stated goals. A builder's own decision-queue answers are exactly that training set. That suggests a concrete test (build 3 in section 7): how often can a model predict the builder's queue answer from the item alone?
No major position says a narrow task policy is the self. My reading of each view applied to a "work-only distillate":
The bootstrapping result cuts against "the soul is what silicon runs better": what transfers and improves is the policy, and it improves precisely by discarding the variability that makes someone a person on a given day.
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