Can 24/7 AI Availability Increase the Pressure on Human Workers Rather Than Reduce It?

A system can be always available without making people freer

Artificial intelligence can respond at any hour. It can draft a message during the night, summarize a document across time zones, classify requests while a team sleeps, and continue processing tasks without a biological pause.

This creates a technical possibility: work no longer has to stop when one person leaves the workplace.

That possibility can support human freedom. Workers may complete routine tasks more quickly, work asynchronously, and spend less time waiting for information.

It can also create a new expectation. If the system is always ready, the organization may begin to expect faster responses, longer availability, larger workloads, and shorter deadlines from the people who use or supervise it.

The question is not whether AI can work continuously. The question is whether human beings are then expected to remain continuously available as well.

Thompson: from clock discipline to always-on time

E. P. Thompson’s account of industrial time helps us understand this transformation.

Industrial production organized work around clocks, shifts, schedules, and measurable intervals. The worker’s time became something that could be purchased, supervised, and divided into productive and non-productive periods.

AI can extend this discipline beyond the traditional workday. The organization may no longer need to wait until morning for a draft, a translation, or a preliminary analysis. The task can begin immediately, which may make the worker’s delay appear unnecessary.

The working day does not have to be officially lengthened for its boundaries to become weaker. A message received at night, a request expected before breakfast, and a task completed across several time zones can gradually turn availability into an unspoken obligation.

Marx: labor power still needs to be reproduced

Karl Marx’s analysis of labor power makes the biological difference impossible to ignore.

Human workers need sleep, food, healthcare, recovery, family time, and social life in order to continue working. These activities reproduce the capacity to work, even when they are not recorded as part of the production process.

AI can reduce the time required for a task, but it cannot remove the human conditions required by the people who design, use, supervise, correct, and maintain the system.

If a company treats AI availability as a reason to reduce human recovery time, it is consuming the conditions of future labor. The immediate output may rise while fatigue, turnover, mistakes, and long-term incapacity accumulate outside the productivity calculation.

When availability becomes a performance measure

Artificial intelligence can make availability easier to measure. Systems record response times, open tasks, unresolved queues, missed notifications, and periods without visible activity.

Harry Braverman’s analysis of management helps explain why this matters. Once the workflow is embedded in a system, the organization can separate the design of the process from the worker’s control over it. The software defines the sequence, the target, and sometimes the acceptable delay.

The worker may appear free to organize the day while being continuously evaluated by a system that treats every pause as unused capacity.

AI can therefore turn availability into a measurable form of performance. The worker is not only judged by what was produced, but by how quickly the worker became responsive to the next demand.

The pressure can return as workload density

AI assistance does not always lengthen the workday directly. It can make the existing workday denser.

A worker who once completed ten cases may now be expected to complete twenty. A team that once answered requests during business hours may be expected to provide coverage across multiple time zones. A draft that once required a day may now be expected within an hour.

The organization can claim that AI reduced effort while increasing the amount of work packed into each available period.

Gray, Suri, and Casilli: the workers behind continuous service

Mary L. Gray and Siddharth Suri’s work on ghost work helps reveal what constant digital availability requires behind the interface.

Someone must review uncertain outputs, handle unusual requests, moderate harmful material, correct data, and respond when the automated system cannot complete the task. These workers may be distributed across locations and time zones, making the service appear uninterrupted.

Antonio Casilli’s research on digital labor shows how automation can reorganize this support work rather than eliminate it. A service can operate continuously because human labor has been fragmented and placed in less visible positions.

The user experiences a 24/7 system. The worker experiences a chain of shifts, alerts, queues, exceptions, and performance targets.

Crawford: continuous availability has a physical cost

Kate Crawford’s analysis of AI reminds us that a 24/7 system is not immaterial. Continuous operation requires data centers, energy, cooling, hardware, security, engineering, and maintenance.

These systems also require people who monitor infrastructure, repair failures, update models, and manage access. The service appears available at all times because the supporting labor and material systems remain active in the background.

When organizations demand uninterrupted service without funding adequate staffing and maintenance, the pressure is transferred to the people who keep the infrastructure functioning.

Pasquinelli: collective intelligence does not mean collective exhaustion

Matteo Pasquinelli’s work on collective intelligence helps clarify another confusion. AI systems draw on language, knowledge, culture, professional practice, and problem-solving accumulated through social activity.

That collective intelligence can remain available in a model at any hour, but the people who produced the underlying knowledge still need time to rest, learn, and participate in life outside production.

Making collective knowledge technically available does not justify demanding continuous human extraction from the communities that created it.

When can AI genuinely reduce pressure?

AI reduces pressure when the organization treats saved time as a human benefit rather than as unused capacity.

That may mean shorter working hours, fewer interruptions, realistic response windows, more staffing for exceptions, protected off-hours, and enough time to check difficult outputs.

AI can also support asynchronous work when the system is used to reduce waiting rather than to erase boundaries. A worker can return to a prepared draft later without being expected to answer immediately.

The difference is institutional. The same technical capability can either increase autonomy or extend managerial reach.

The boundary that AI cannot remove

AI may reduce the time required for some tasks, but it cannot eliminate the human need for recovery, attention, judgment, and social life.

A system that never sleeps can still be organized around people who do. If the organization refuses to recognize that boundary, technical availability becomes human pressure.

The promise of AI is not that people should imitate machines. Its promise should be that machines absorb more of the repetitive burden while people regain control over their time.

TravelIAQ Smart Tip: When AI makes a service available 24/7, check whether human working hours became shorter or merely more densely packed. Faster tools reduce pressure only when the saved time is protected from becoming new availability, new volume, or new deadlines.