# Scaffolding Labor
> Some occupations exist only because a technology is unfinished. They form fast, employ many, then compress into a small expert remnant. Data work is inside that pattern now.
Author: Kariaa Research
Published: 2026-07-26
Category: Insights
Reading time: 12 min read
Canonical URL: https://www.kariaa.com/reports/scaffolding-labor
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For about seventy years, the word *computer* named a job rather than a machine.
It was held by people, most of them women, who performed arithmetic in
organized teams for observatories, insurance offices, artillery ranges, and
eventually spacecraft. The category was created by a technology that generated
more numbers than anyone could handle, and it was ended by a technology that
handled them. Neither event was about the ability of the people involved. The
occupation existed in the gap between what a machine could produce and what a
machine could process, and it lasted exactly as long as the gap did.
## The category the gap creates
**Some jobs are not displaced by a technology. They are manufactured by its
immaturity and then withdrawn by its completion.** This is a narrower claim than
the general one about automation, and the narrowness is what makes it useful. A
blacksmith was displaced by a technology that had nothing to do with
blacksmithing. A telephone operator was created by the telephone and ended by
automatic switching. The same system produced the job and removed it, and it did
both for the same reason, which is that the system was incomplete in a specific
place and then was not.
Call this scaffolding labor. It has a recognizable profile. It appears suddenly
and in volume. It is accessible relative to the sophistication of the system it
supports, because the whole point is to do at scale what the system cannot yet do
at all. It is often the entry point into an industry for people the industry
would not otherwise have hired. And it is load-bearing right up to the moment it
is not.
The Harvard College Observatory started employing women as computers under
Edward Pickering from the 1880s, to classify stellar spectra arriving faster than
the observatory's astronomers could handle. They were paid a fraction of what a
male assistant earned, and the work was understood as clerical. Williamina
Fleming developed a classification scheme. Annie Jump Cannon built the system
still in use for stellar classification. Henrietta Swan Leavitt found the
period-luminosity relation in Cepheid variables, which is the measuring stick
that established the scale of the universe and made Hubble's work possible.
That is the second thing worth noticing about scaffolding labor. The work is
categorized as routine because the system needs a great deal of it, cheaply, and
the categorization is frequently wrong about what the people doing it are
actually capable of.
## The compression
**The end is rarely a collapse. It is a compression, and what survives it is
different in kind from what preceded it.** Automatic telephone exchanges did not
eliminate every operator at once. They removed the common case, which was
connecting a local call, and left the exceptions: emergency calls, complex
routing, assistance. The residual role required more judgment, employed far fewer
people, and was less accessible than the job it descended from.
The Glenn detail captures the transition better than any statistic. By 1962 the
electronic computers at NASA could produce orbital figures, and the figures were
not yet trusted. Katherine Johnson was asked to run the numbers by hand and
confirm them. At that moment the human computer's job had already changed from
producing the answer to certifying it. That is what the remnant phase looks like:
the same person, a smaller category, and a task defined by the machine's residual
unreliability rather than by its absence.
## What survives compression
Three properties predict which parts of a scaffolding category persist, and they
are consistent across every case above.
**Judgment about edge cases survives.** The machine handles the distribution's
centre and fails at its tails, and the tails are where the remaining work is.
This is why the residual role is always harder than the average case of the
original job, and why headcount and difficulty move in opposite directions.
**Verification survives longer than production.** The last thing a maturing
system gives up is a human confirming that its output is correct, because trust
in the system lags its actual reliability by years. Johnson checking the
trajectory is the canonical example, and it is a durable position for exactly as
long as the trust gap persists.
**Taste survives, and is never described as a skill until it is scarce.**
Compositors were paid for typesetting and valued, in the end, for knowing what a
page should look like. That knowledge was invisible while the mechanical part of
the job dominated, and became the entire job once the mechanical part left.
## The counter-case, stated at full strength
**The historical record does not support a straightforward story of destruction,
and the strongest evidence against the pessimistic reading comes from the same
cases.**
Movable type ended the professional copying trade within a few decades. It also
created typefounding, presswork, composition, proofreading, publishing,
bookselling, and eventually a reading public large enough to sustain professional
authorship. The number of people employed making books rose enormously. The
specific job of copying by hand disappeared, and the industry it belonged to grew
by orders of magnitude.
Modern banking produced a sharper version of the same effect. After automated
teller machines spread through the United States, the number of bank tellers did
not fall as everyone expected. It rose, because ATMs made branches cheaper to
operate, banks responded by opening more of them, and the teller's role shifted
toward sales and relationship work. The machine took the transaction and the job
absorbed something else.
Both cases share a mechanism worth stating explicitly: automation lowered the
cost of the output, demand for the output was elastic, and the volume increase
outran the labor saving. Where that condition holds, employment rises. Where it
does not, it falls. It is an empirical question about a specific market, not a
matter of temperament, and the honest answer for any given case is usually that
nobody knows in advance.
## Where data work sits
Data annotation and evaluation is scaffolding labor by the definition above. The
category did not exist at scale twenty years ago. It was created by a technology
that required enormous volumes of labelled examples, it grew quickly, it is
accessible relative to the systems it supports, and it is load-bearing right now.
The structural question is which phase it is in, and there is a reasonable
argument that the answer is already changing. The early demand was for volume,
because the binding constraint was quantity of general data. As systems become
competent on common cases, the value of another common-case example approaches
zero and the binding constraint moves to the cases the system still gets wrong.
Those cases require someone who genuinely knows the domain, because identifying a
subtly wrong answer in medicine, law, or advanced mathematics requires being able
to produce the right one.
That is the compression, arriving in the usual shape. Fewer people. Higher bar.
Better paid. And a category that was an accessible entry point becoming something
that requires credentials in the domain, which is close to the opposite of what
it was.
We do not exempt ourselves from this. Kariaa operates data work, and the analysis
above describes our own position as much as anyone's. Treating it as a permanent
category would be a forecasting error. Treating it as worthless because it is
transitional would be a different error, since the transitional phase of every
previous case was where a large number of people entered an industry that would
not otherwise have taken them.
## What this implies for anyone inside it
The pattern does not predict a date, and anyone claiming otherwise is selling
something. It does predict a shape, and the shape has practical consequences.
**The peak is not a signal of durability.** Every one of these categories was at
maximum employment and maximum apparent stability shortly before compression.
Volume of demand tells you about the current gap, not about how long it stays
open.
**The exit is into the system, not away from it.** Human computers became
programmers and analysts. Compositors who understood layout became designers.
Operators moved into the telephone companies' other roles. In each case the
people who transitioned well were the ones who had learned how the surrounding
system worked while doing the routine part, and the ones who had treated the
routine part as the whole job were the ones stranded.
**The remnant is worth aiming at.** Verification, edge cases, and judgment are
where the compressed category concentrates, they pay better than the volume
phase, and they are visible in advance because they are the parts of the work
that are currently annoying rather than repetitive.
The scaffolding metaphor holds in the way that matters. Scaffolding is not fake
work. Nothing gets built without it, it carries real weight, and the people on it
are doing something difficult. It also comes down when the structure stands, and
the ones who did well out of it were the ones who spent the time learning the
building.
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*This report was produced by **Kariaa Research**. All data, resources, and
analysis are proprietary. For questions, contact
[research@kariaa.com](mailto:research@kariaa.com).*