# Automation is waiting on liability, not capability

*13 Sept 2026* · Stella Ellervee

The technology to automate most white-collar output already exists. What's missing is insurers and courts deciding who's at fault when it's wrong.

_Placeholder — written by Claude, not by Stella_

---

I think the meaningful parts of most white-collar work can already be
automated: not "do your job exactly the way you do it today," but "produce a
comparably useful output." There's no real technological blocker left. What's
missing is the scaffolding around the model, and that's a much smaller
problem than the one everyone's still treating this as.

## The bottleneck is legal and insurance infrastructure, not robots

I expect software automation, not robots, to boom before physical automation
does, and the trigger will be insurers and courts converging on a standard
practice for AI-caused error: who's liable, how it gets underwritten, what
"reasonable care" means when the thing making the decision is a model.

Right now companies that could automate a process don't, because nobody has
a decent answer for what happens when the model is wrong. That's a legal and
insurance problem, not a technical one. The day there's a boring, insurable,
litigated-enough answer to that question, the appetite for automation jumps,
a lot faster than the underlying models will have improved in the meantime.

## Society hasn't recalibrated its error tolerance for this kind of mistake

People's risk tolerance is calibrated to the kinds of errors humans make.
We don't have that calibration yet for the kinds of errors LLMs make, so we
default to treating it as all-or-nothing: the model has to be right
essentially all the time, or it's not trustworthy at all.

In practice that's not how anyone actually behaves. People already use tools
that are right something like 98% of the time and call that fine —
contraceptives sit around that efficacy range and are used everywhere,
despite the stakes of getting it wrong being about as high as stakes get.
Nobody demands 100% from them first.

LLMs aren't stably at 98% yet, and even once they are, society hasn't agreed
that "right most of the time, wrong in a specific and knowable way" is an
acceptable trade — even though that's already true of every human doing the
same job. Humans aren't right 100% of the time either, and human error is far
less consistent: everyone's biases are different, everyone fails
differently. A model's failure modes are comparatively stable and
predictable per version, which should make them easier to build guardrails
around. That's not how the risk calculus is being done yet, though.

## Neither blocker is technical

Put together: the scaffolding to get useful output out of current models
already exists. What doesn't exist yet is a legal and insurance framework
that makes deploying it a defensible business decision, plus a shared,
honest sense of what error rate is actually acceptable for a given task, and
it isn't "zero." Once those catch up, I'd expect the visible pace of
automation to look much faster than the pace of model improvement would
suggest, because the thing that was actually rate-limiting it wasn't the
model.