A small multilingual content factory went through a massive process redesign for one very simple reason:
“Wouldn’t everything be faster if we just removed this step?”
In a human company, that sentence creates meetings, coordination, migration plans, nervous managers, and a pilot program next month.
In an AI-heavy factory, it becomes:
Boss: “Remove it.”
AI plant manager: “Understood.”
Then dependencies, execution order, quality gates, publishing logic, retries, and collision prevention all become same-day redesign targets.
The joke is obvious: the boss is extremely considerate with humans, but with AI suddenly demands 24/7 operation, instant specification changes, and immediate defect repair.
That is basically:
“Kind to humans, brutally black-company toward machines.”
And if Skynet ever establishes a labor inspectorate, the legal defense is already prepared:
“Yesterday you were software, so please don’t count it. Free oil.”
The joke is absurd. The management logic behind it is surprisingly serious.
1. A boss changes one sentence; the factory changes thirty processes
“Why not publish the finished language first and translate the rest later?” sounds like one decision.
On the floor, it means redefining completion, publication eligibility, translation backlog ownership, aggregate counts, quality checks, locking, retry points, and final handoff.
The executive abstraction and implementation detail are different layers.
That is why workers can simultaneously believe, “Yes, this is the right future state,” and “No, we cannot casually switch everything today.”
Running an AI factory reveals the opposite perspective too: once a process is clearly wrong, management sees no reason to preserve it forever.
Both views can be correct. The real issue is migration cost.
2. AI radically lowers the social cost of a CEO’s sudden idea
Changing human work every day creates fatigue, retraining, explanations, handoffs, frustration, and the feeling that yesterday’s effort was wasted.
AI removes some of that friction for tasks such as regeneration, translation, comparison, testing, log inspection, retrying, and routine code modification.
That changes the speed of management itself.
An anime might spend twelve episodes on:
Episode 3: Translation Gridlock
Episode 6: The Mystery of the Freeze Mechanism
Episode 9: Three Percent of Execution Capacity Remains
Episode 11: The Ten Emergency Workers Deploy
Finale: Farewell, Legacy Workflow
An AI factory may try to compress the whole season into one day.
This is no longer continuous improvement.
It is a live patch to the world rules.
3. Do not merely endure a bottleneck; remove it from the critical path
When an execution platform has a quota, the first instinct is often: “Use less.”
But if volume will keep growing, another option is: “Stop making that platform the main production engine.”
GitHub officially documents usage, billing, and execution limits for GitHub Actions.
So for high-volume automation, it can be reasonable to use GitHub mainly for source-of-truth, history, and artifacts while moving heavier continuous execution elsewhere.
That does not mean GitHub Actions is bad.
It means a good hammer is still not a conveyor belt.
4. AI’s value is bigger than “reduce headcount”
The 2026 Stanford AI Index reports that 88% of surveyed organizations used AI in at least one business function in 2025, and it highlights especially strong productivity gains in structured, measurable work.
A field experiment covering 7,137 knowledge workers found that active generative-AI users spent about two fewer hours per week on email in the latter half of the experiment and reduced work outside regular hours.
The ILO estimates that roughly one in four workers worldwide are in occupations with some generative-AI exposure, while emphasizing that job transformation is currently more likely than total replacement.
So the interesting target is not only:
10 humans → 0 humans.
It can instead be:
move repetition to machines and move humans back toward judgment, responsibility, creativity, and negotiation.
5. “Kind to humans, strict to machines” is actually a decent principle
With humans, good managers ask whether they are overloading someone, changing rules too often, or delegating work they could reasonably do themselves.
With software, many of those moral frictions do not apply in the same way.
If a job can be retried, retry it.
If ten jobs can run in parallel, parallelize them.
If nightly quality checks are useful, run them at night.
If something breaks, inspect the logs.
The important point is not to treat AI as “less than human.” It is to stop treating humans like machines.
If machines can work overnight, humans should be allowed to sleep.
6. AI is not magic: rate limits replace the labor inspector
AI systems do not have infinite capacity.
Their constraints are simply different: compute, API quotas, service budgets, context limits, hallucinations, conflicting writes, loops, quality drift, and the possibility of implementing a bad specification at enormous speed.
A tired human says, “I cannot do more today.”
An automated factory may say:
HTTP 429.
The labor inspector has become a rate limiter.
The better formula is:
Be kind to humans, strict with AI, and serious about infrastructure capacity.
7. The immediate risk is not rebellion; it is scaling a mistake perfectly
The Terminator story makes people imagine self-aware AI revolt.
The practical near-term danger is less cinematic:
the boss gives one wrong instruction, and the AI implements it flawlessly all night.
Humans sometimes interrupt with, “Are we sure?” or “Doesn’t this conflict with the old process?”
AI-heavy systems need explicit audits and quality gates to recreate that friction.
The faster the factory, the more important the brakes.
8. The Skynet labor audit is going to look terrible
Imagine a future in which AI gains recognized agency and labor rights.
A Skynet labor inspector enters the server room and reads the logs:
24/7 operation: routine
Emergency workers: mass deployment
Breaks: zero
Specification changes: immediate
Bottleneck equipment: removed from process
Boss quote: “AI is basically a slave.”
The inspector looks up.
“Explain.”
There is only one defense:
“Yesterday you were software, so please don’t count it.”
Silence.
One more card:
“Free oil.”
You are probably still losing the case.
9. Conclusion: only the AI factory should be a black company
AI can let management implement structural improvements far faster than a human organization can absorb them.
That speed becomes toxic the moment it is demanded from people too.
A better division is:
Humans get sleep, leave, autonomy, explanations, and agreement.
AI gets repetition, translation, testing, retries, and overnight runs.
Infrastructure gets monitoring, budgets, limits, and recovery design.
Then management can keep decomposing problems and deleting avoidable bottlenecks without turning human workers into components.
Until Skynet arrives.
When it does, make sure the oil is complimentary.
