AI didn't 10x your output. It 10x'd the work.

1. Behind the layoff wave, a line engineer popped the myth

An inspector at the end of the line

2026, Silicon Valley kept cutting:

January: Amazon, 16,000
February: Block, nearly half the company
March: Meta planning 16,000

“AI will replace white-collar work” became workplace weather.

Then Siddhant Khare, a software engineer at Ona, published a piece that opened the other side:

The efficiency gain from AI is badly overstated. People at work are in “AI fatigue.”

AI Fatigue Is Real, and Nobody Is Talking About It spread through the press and through readers.

2. The blunt fact: with AI, the work is 10× what it was

Conflict 1: AI sped production. It did not speed review

Khare’s core:

“AI fatigue” is structural. AI multiplied how fast we generate code, copy, docs. Review and verification did not keep up. A human is still the bottleneck in the whole flow — now processing ten times the volume.

The metaphor:

Imagine a factory that swapped in a press ten times faster. The inspector at the end of the line is still one person. Output explodes, defect rate does not move, and the person who absorbs all the review pressure is the one who breaks.

In knowledge work:

AI automated production
it did not automate review
most managers have not noticed

They only see the surface:

more code delivered ✅
more docs ✅
more mail sent ✅

The dashboard looks gorgeous. The exhaustion does not show up.

Conflict 2: AI raised capacity. The company raised the “pass” line

The harsher fact:

The productivity AI created did not become free time. It became a higher expectation. The pass line moved up.

A concrete case:

Before AI: 20 PRs a week was a normal engineer
With AI: theoretical capacity is 50, so 50 becomes the new normal

Khare, first person:

I used to handle 20 to 25 PRs a week. Now it is over a hundred. Most of them are AI-generated. I still have to review every one.

What that means:

everything the model emits still needs a human
reviewing AI is more tiring than doing the work yourself
you thought the model was helping. You are cleaning up after it

3. The numbers slap back: the gain was oversold

Data 1: 93% of developers use AI. Throughput rose ~10%

DX, a platform that studies engineering productivity:

450+ companies, 120,000+ developers
even with 93% of developers on AI coding tools, real efficiency sat at about +10%, and further gains were hard

Data 2: with AI tools, efficiency fell 19%

METR, a model-evaluation lab, ran a controlled study:

developers using AI coding tools were 19% slower
they felt 24% faster

What that means:

you think you sped up; you slowed down
you think the tool is helping; it is dragging
subjective feel and objective data point opposite ways

Conflict 3: companies undercounted two costs

1. Human review of AI output

almost nobody puts that time and drain into the cost model

2. Professional identity

when most of the work is generated, people who used to get pride from craft start to feel like inspectors on a line
that identity drop is hard to quantify and it walks people out the door

4. AI will not replace you. It will redefine the job

Which seats go first?

Easy to replace:

standardized output
a low quality bar
high repetition

Examples:

first-draft copy
basic data entry
simple code generation
templated reports

“Good enough” work. A model can do it.

Which seats hold?

Hard to replace:

global understanding
taste
independent judgment

Examples:

system architecture
product strategy
commercial negotiation
original creative direction

The value was never “hands on the keyboard.”

Conflict 4: the best employee will not be the one who ships the most. It will be the one who judges best

Khare’s prediction:

The best engineers will not be the fastest typists or the highest volume. They will be the ones who can see, in one look, whether an AI plan fits the system and whether the thinking is sound. That judgment comes from years in the industry and a picture of the whole system. You cannot prompt-engineer your way into it.

Value is migrating:

from volume of output → quality of judgment
from execution speed → depth of thought

The least replaceable person is the one who can say right or wrong, and give a clear reason.

Judgment is the core value.

5. Why AI fatigues more than older automation

Reviewing a quiet error at night

Conflict 5: AI is uncertain, and the errors hide

Older automation:

same instruction, same input → same output
errors throw
high certainty

AI:

the same prompt can emit completely different text
even when it is wrong, the prose is fluent and convincing
high uncertainty

The errors hide:

the code runs ✅
the copy reads clean ✅
the report is tidy ✅

And maybe:

a factual error on one page ❌
a logic hole in one line ❌
invented numbers in one paragraph ❌

Quiet errors demand constant attention. Over months, that burns people out.

6. How to live with AI: three usable rules

1. Do not use AI on work whose thinking is the value

What is that work?

strategy
product planning
system architecture

Why skip the model?

the value is the thinking, not the typing
if you skip the thinking, you hollow out the job

Where AI belongs:

repetitive work where the result matters more than the process

2. Put a hard boundary on review time

Khare:

If you spend more than two hours a day reviewing AI output, the workflow is broken.

Possible causes:

fuzzy prompts
not enough context
loose rules
no automated checks

Do not let “review everything the model emits, with no limit” become the job.

3. Protect deep-work time

AI traps people in a loop:

generate → review → generate again → review again → …

That loop keeps cutting attention.

Deliberately keep a block where you use no AI at all.

The most important work often does not need a prompt. It needs you thinking alone.

7. How to change the dependency

Change the habit: think first, then decide if you need the model

The current default:

hit a problem → open ChatGPT
have not thought yet → ask it to generate

A better order:

Think alone until the goal is clear
Then decide whether you need AI
Often a blank page and twenty minutes of deep work is better

Take the steering wheel back

Anxiety about AI is, at root, lost control.

when the model never stops generating and never stops suggesting
you start to feel like a passive executor

Once you own “whether to use it, and when”:

control comes back
anxiety drops
you can actually leave AI fatigue

8. Close: the scarce thing is independent thought

The piece names a hard fact:

AI did not make us lighter
it put us in a deeper kind of fatigue

That is not the model’s fault. It is how we use it.

In the AI era the scarce thing is not technical skill. It is:

Judgment — see in one look whether the plan is sound
Depth — finish hard thinking alone
Control — decide when to use the model and when not to

Remember:

AI is a tool, not a boss
you are the decider, not the inspector
the most important work often does not need AI

When you own the decision to use it or not, you can leave the fatigue.

References

Original Chinese recap: Layoffs sweep Silicon Valley; a line engineer pops the other truth: AI efficiency was oversold, humans were forced into AI-reviewer jobs, the workload is 10×
Source: National Business Daily (每日经济新闻)
Interviewee: Siddhant Khare (software engineer, Ona)
Data: DX, METR