For the last year, the debate around AI at work has split into two unhelpful extremes. On one side, we have breathless talk of “AI coworkers,” complete with onboarding rituals, performance reviews, and soft-focus imagery of humans and machines collaborating happily at their desks. On the other, we have an anxious counter-reaction that insists this language is dangerous, misleading, and fundamentally wrong, because AI systems are not people and should never be spoken of as if they were. Both camps miss the point. The real question is not whether machines deserve human metaphors, but whether leaders understand what kind of economic force they are unleashing, and what kind of organization that force demands.
Calling something “labor” has never meant it is human. It means it performs work, incurs cost, produces output, and sits inside a system of incentives, controls, and trade-offs. We already accept this logic in countless places without controversy. We speak of mechanical labor, industrial labor, and even “work” performed by capital assets, logistics networks, or energy infrastructure. The word labor is not a sentimental label. It is an accounting term, a political term, and a strategic one. It tells us where effort is applied, how value is created, and who captures the surplus.
The discomfort with the phrase “digital labor” often comes from confusing metaphor with mechanism. The fear is that if we talk about AI as labor, executives will start treating software like employees, importing human management practices into systems that do not need motivation, morale, or meaning. That fear is not unfounded. We have already seen organizations fall into the trap of grafting new technology onto old organizational charts, preserving familiar roles and routines while claiming transformation. But that failure is not caused by the term. It is caused by shallow thinking. Bad metaphors do not invalidate good economics.
What digital labor actually names is a shift in how work gets done, measured, and priced. AI systems do not simply assist humans. They execute tasks end to end, at variable cost, with increasing autonomy, and with performance characteristics that are fundamentally different from human workers. They scale instantly, improve unevenly, fail in strange ways, and demand oversight that looks nothing like traditional management. Pretending this is just “capability” without acknowledging its labor-like effects does not make organizations wiser. It makes them blind.
This blindness shows up most clearly in how firms talk about productivity. When AI is framed purely as a tool, leaders focus on local efficiency gains. Faster reports. Cheaper analysis. Fewer errors in routine tasks. These improvements matter, but they are not the transformation. The transformation comes when the cost of performing cognitive work collapses and organizations are forced to rethink which activities are scarce and which are abundant.
At first, this shows up as redistribution. Tasks move. Responsibilities shift. What once required teams now requires supervision. What once consumed days collapses into minutes. Work does not disappear so much as it migrates, flowing toward the edges where judgment, context, and accountability still matter. But redistribution is only the visible surface of change. If leaders stop there, they mistake motion for progress.
The deeper shift occurs when organizations recognize that collapsing cognitive costs undermine the logic of existing processes. When work becomes cheap and fast, many structures no longer make sense. Approval layers exist because information was scarce. Handoffs exist because humans were slow. Entire organizational designs evolved to manage limitation, not to maximize value creation. Digital labor exposes this reality relentlessly, forcing a question most firms avoid: if this process were designed today, knowing what machines can now do, would it exist at all?
This is why digital labor cannot be reduced to a workforce debate. Labor is not just something you manage. It is something you allocate. It competes with capital. It reshapes bargaining power. It determines how value flows through the firm. When AI performs meaningful portions of knowledge work, the organization is not merely adopting a technology. It is redefining its production function. Ignoring this reality because the word “labor” feels anthropomorphic does not make organizations more precise. It makes them strategically incoherent.
The opposite reaction, fear of mass job displacement, suffers from a similar lack of depth. It assumes a zero-sum replacement model, where machines simply take human jobs and the story ends. History suggests something more complex. Technological shifts rarely eliminate work in aggregate. They reprice it. They change where value is created, which skills command a premium, and which roles lose their economic justification. The political consequences are real, but they are not caused by machines acting independently. They are shaped by decisions leaders make about deployment, governance, and distribution.
Digital labor does not automatically destroy jobs. It destroys certain task bundles. It exposes inefficiencies that were previously hidden inside roles. It forces organizations to confront how much of their structure exists to coordinate human limitation rather than to create value. In doing so, it often increases demand for judgment, system design, oversight, and creative problem solving, even as it reduces demand for routine execution. The danger is not that machines work. The danger is that institutions fail to adapt.
One reason this debate remains stuck is that we lack a language for hybrid systems. Tools are subordinate. Workers are autonomous. AI agents are neither. They act independently within boundaries, they learn from feedback, and they require governance rather than supervision. Calling them tools understates their agency. Calling them coworkers overstates their humanity. Digital labor sits in between. It is productive capacity that must be designed into workflows, not bolted on.
The deeper challenge is not reallocating tasks, but reimagining the system itself. When work can be executed at near-zero marginal cognitive cost, many processes cease to make sense in their current form. Entire functions exist today to compensate for latency, error, and coordination overhead that no longer need to exist. Digital labor does not simply improve the organization. It questions whether the organization, as currently designed, is still fit for purpose.
This is where transformation either accelerates or stalls. Redistribution optimizes the existing machine. Reinvention questions whether the machine should exist at all. Firms that stop at redistribution preserve their hierarchies and routines while quietly automating away the substance inside them. Firms that pursue reinvention redesign decision rights, collapse layers, and rebuild workflows around intelligence rather than headcount.
Some argue these systems should be treated purely as capital investments, governed through portfolio logic rather than workforce thinking. There is truth here, but it is incomplete. Unlike traditional capital assets, digital labor is not static. Its performance can drift. Its costs can spike. Its failures can be rare but catastrophic. It requires continuous monitoring, retraining, and oversight. These are operating realities, not one-time investments. Treating AI purely as capital underestimates the work required to keep it reliable and aligned.
More importantly, capital language alone cannot capture the social and political dimensions of this shift. Labor has always been about power as much as productivity. Who controls work. Who benefits from efficiency gains. Who absorbs risk when systems fail. When AI performs work at scale, these questions do not disappear. They intensify. Avoiding the language of labor may feel safer, but it often obscures the very consequences leaders must confront.
The real issue, then, is not whether digital labor is the right phrase, but whether leaders are willing to engage with its implications. Digital labor does not mean machines are people. It means work has become programmable. It means cognition has a marginal cost. It means organizations must design systems in which humans and machines jointly create value, each doing what they do best, without pretending they are interchangeable.
This reframing also explains why superficial adoption fails. Simply inserting AI into existing roles preserves the old logic of the firm. It optimizes locally while leaving global structure untouched. True transformation requires rethinking workflows from first principles, asking which decisions should be automated, which should remain human, and which should be shared. It demands new metrics, new governance models, and new leadership capabilities.
Language matters because it directs attention. When we talk about digital labor, we force a confrontation with cost, substitution, and value capture. We ask how cheap cognition reshapes strategy. We ask who benefits when intelligence becomes abundant. Avoiding the term does not eliminate these questions. It merely delays them.
In the end, the future of work will not be decided by metaphors, but by design. Organizations that succeed will be those that treat AI neither as an employee nor as a gadget, but as a new productive force that reshapes everything it touches. They will measure it rigorously, govern it deliberately, and integrate it thoughtfully. Digital labor is not about making machines more human. It is about making organizations more intelligent.
