Last month, I was testing the automated monitoring module of our AI platform system. At 3 AM, the system detected an abnormal drop in metal recovery rates on a production line, automatically adjusted the parameters, and sent a report to my inbox.

I didn’t see it until 8 AM. The problem had already been resolved.

This made me pause and think: if the system can make this kind of judgment without human involvement, what is my role? Am I an operator, supervisor, or just someone who receives notifications after the fact?

This question, when scaled up to the entire economic system, is what I want to discuss.

The Always-On Economy Isn’t Just 24-Hour Business

Past economic systems were built on a premise: humans get tired. We need sleep, shifts, handovers, meetings. All institutional design rhythms were benchmarked against human physiological limits. Even globalized enterprises couldn’t truly achieve round-the-clock decision-making—the bottleneck was never information, but human physical and cognitive limitations.

But when AI Agents begin entering decision-making layers, this premise is dismantled.

The always-on economy I’m referring to isn’t about convenience stores staying open all night. Rather, it’s about decision-making, transactions, optimization, and collaboration continuing without human involvement. AI Agents analyze global inventory and demand in real-time, automatically rearrange production and logistics, dynamically adjust pricing, and coordinate supply chains across time zones. The fundamental rhythm of the economy is compressed to frequencies humans can’t react to.

Decisions Transform from Nodes to Continuous Flow

Traditional enterprise decision-making is intermittent. Meetings, discussions, reports, more meetings. Decisions are nodal events.

But when models participate in decision-making, decisions become continuous flow. Instead of adjusting strategy once a quarter, optimization happens every minute automatically. This sounds wonderful, but there’s a problem: when decision frequency far exceeds human comprehension speed, are humans still in control?

My experience at the company is: once systems start making decisions automatically, human roles unconsciously slide from “decision-maker” to “approver,” and finally to “observer.” Not because we’re excluded, but because we can’t keep up. This slippery slope is much faster than imagined.

Organizational Boundaries Are Dissolving

AI Agents don’t belong to any department. They span finance, supply chain, manufacturing, customer service, marketing. In traditional organizations, departments are units of power and responsibility. But in data-flow-driven environments, decisions result from cross-module collaboration.

Future enterprises may not be collections of departments, but networks of algorithmic nodes. When organizational structure gives way to model architecture, power shifts from “position” to “architecture design.” Whoever designs the system’s logic effectively controls the organization. This aligns with the trend I discussed in “The Canary in the Coal Mine of AI Employment”—what’s being replaced isn’t specific positions, but the entire logic of organizational operation.

The Civilizational Problem of Accountability

When AI participates in decision-making, how is responsibility defined? Is the model responsible? The data? The engineers? Or the enterprise?

In always-on systems, responsibility is no longer clearly assignable. This isn’t just a legal problem—civilization’s core lies in traceable accountability. When decisions are made by non-human agents, we’re entering an unprecedented form of responsibility.

Returning to my 3 AM example: the system automatically adjusted parameters and the production line returned to normal, but what if it had adjusted incorrectly? Who bears the loss? I didn’t give the command, but I designed the rules that allowed it to auto-adjust. This gray area of “indirect responsibility” will grow larger as the always-on economy expands.

From Efficiency Problems to Order Problems

The Industrial Revolution amplified mechanical force, the Digital Revolution amplified information processing power, and the AI Revolution amplifies decision-making power. When decision-making is automated, the economy no longer operates on human rhythms.

But efficiency maximization doesn’t equal correct direction. The always-on economy pursues optimization, but civilization must answer: optimization for whom? Without a value framework, AI will only amplify existing inequities. Without order design, automation will only accelerate deviation.

Human roles won’t disappear, but will transform from operators, executors, and sustainers to designers, calibrators, and judges. Future competitive advantage won’t lie in who can work longer, but who can define order.

Technology enables the economy to operate around the clock. But only civilization can decide for whom it operates.