The company might deliver the same amount of software using fewer engineering hours—the same with less. It might use its existing engineering capacity to build more products—more with the same. Or it might redirect that capacity from feature implementation towards product management, architecture and testing.

All three outcomes begin with the same productivity improvement. What differs is how the company responds to it.

Making an input more productive does not tell us how much of that input the company will ultimately use. That depends on what the technology can do, how production is reorganised and how much additional activity becomes worthwhile as costs fall.

What the technology changes: technological bias

Technological bias describes how a technology changes the relative productivity of different inputs. It is a property of the production technology—not a statement about how much labour or capital a company will ultimately use.

Economists assess this by comparing the inputs’ marginal products: the additional output produced by adding a little more of one input while holding the quantities of the others constant.

If a technological change raises the marginal product of labour relative to capital, it is labour-biased. If it raises the marginal product of capital relative to labour, it is capital-biased. Here, “biased” simply means tilted towards one input.

Capital includes productive assets such as compute and GPU capacity, software systems and AI infrastructure, not simply money available for investment.

Increasing what a worker can produce in an hour is an absolute productivity improvement. Technological bias asks a different, relative question: whether the technology increases the marginal product of labour more or less than it increases the marginal product of capital.

A technology may clearly make workers more productive without that fact alone telling us whether the underlying technological change is labour-biased or capital-biased. Answering that question requires comparing its effects on the productivity of both inputs.

The important point is that technological bias describes a change in productive possibilities. It does not, by itself, determine the quantities of labour and capital that a company will choose to employ.

What the company chooses: factor use

Factor use describes how much labour, capital or another input a company employs after introducing the technology.

Unlike technological bias, factor use is an outcome of the company’s response. It depends not only on the technology, but also on input prices, the ability to substitute between inputs, demand for the company’s output and the scale at which the company chooses to operate.

It helps to separate two margins.

Factor use per unit of output

If a technology reduces the labour required to produce each unit, it is labour-saving for a fixed production target. The company can produce the same amount using fewer hours of labour.

Total factor use

A company that can produce something more cheaply may decide to produce more of it. Total labour use therefore depends on both the labour required for each unit and the number of units produced:

Total labour hours = labour hours per unit × units produced

A technology may reduce the first term while increasing the second.

Suppose a technology cuts the labour required for each unit by 30 per cent. If the company continues to produce the same number of units, total labour use will fall by 30 per cent. But if the lower cost leads it to double production, total labour use will rise by 40 per cent.

In the second case, the technology is labour-saving per unit but labour-using in total. It reduces the labour needed for each unit while increasing the total amount of labour the company employs.

The substitution effect, the scale effect and Jevons paradox

Economists separate the forces behind these outcomes into the substitution effect and the scale effect.

The substitution effect captures how a company changes the combination of inputs used to produce a given amount of output. A new technology may allow capital to perform work that previously required human time, reducing the labour needed for each unit.

The scale effect captures the additional demand for inputs created when lower production costs make a larger volume of output worthwhile. The company may produce more, serve more customers or undertake activities that were previously uneconomic.

If the scale effect is smaller than the saving per unit, total use of the input falls. If the scale effect is larger, total use rises and the technology is factor-using in total.

Jevons paradox describes the case in which an efficiency improvement lowers the effective cost of an activity and causes demand to expand by enough that total use of the input whose efficiency improved increases rather than decreases.

The terms factor-saving and factor-using can therefore be ambiguous unless the relevant margin is stated. “Labour-saving per unit of output” and “labour-using in total” can both describe the same technology.

How technological bias and factor use fit together

Technological bias and factor use answer different questions:

• Productivity effect: Which inputs become more productive?

• Technological bias: Which input’s marginal product increases relative to the others?

• Factor use per unit: How do input requirements or the input mix change for a given amount of output?

• Total factor use: How much of each input does the company employ after output and production have adjusted?

These are related questions, but they are not competing descriptions of the same effect.

A technology may increase what a worker can produce in an hour. It may be labour-saving per unit because each unit requires fewer working hours. And it may be labour-using in total if the resulting fall in costs leads the company to expand production sufficiently.

Similarly, a technology may increase the productivity of capital while also making some forms of human expertise more valuable. More capital and automation could increase the amount of activity that one experienced worker can direct, creating additional demand for people able to define objectives, evaluate results and handle exceptions.

There is no contradiction. Each statement concerns a different property of the technology or a different part of the company’s adjustment.

Technological bias describes how the production technology changes. Factor use describes how the company reorganises production in response.

Applying the framework to generative AI

Consider a generative AI coding assistant that helps software engineers write, test and review code more efficiently.

If the assistant increases what an engineer can produce in an hour, it increases the productive capacity of the labour working with it. For a fixed amount of output—for example, a given number of software features—the assistant reduces the engineering time required per unit. A project that previously required ten thousand engineering hours might now require seven thousand. In this sense, the technology is labour-saving for that production target.

This does not mean that the company will necessarily use less engineering labour overall. Lower development costs may lead it to build more software. If that expansion outweighs the engineering effort saved on each unit, the company will use more engineering labour overall: the technology will be labour-saving per feature but labour-using in total.

The company might use its released capacity to shorten development cycles, improve quality, address technical debt or start projects that were previously too expensive. It might build more features, customise products for more customers or enter new markets.

A plausible pattern for generative AI

To make the framework concrete, suppose the coding assistant reduces the engineering time required per feature by 30 per cent and raises the marginal product of engineering labour relative to the supporting capital. Suppose also that the company responds by doubling the number of features it develops and that adoption requires additional investment in models, compute and software infrastructure.

Under those assumptions, a plausible description is:

• Labour-biased as a technological change, because the assistant raises the marginal product of engineering labour relative to the supporting capital. That relative-productivity effect is a stated assumption in the example, not a conclusion that follows from the productivity improvement alone.

• Capital-using in total, because the company employs more compute, models and AI infrastructure.

• Engineering-labour-saving per feature, because each feature requires fewer engineering hours.

• Engineering-labour-using in total, because the expansion in software production more than offsets the reduction in labour required for each feature.

These descriptions are compatible because they answer different questions. The first concerns relative marginal productivity. The second concerns the use of capital. The third holds output fixed. The fourth allows output to expand.

This is a potential verdict for a particular use of generative AI, not a universal verdict on the technology. If the company did not expand production sufficiently, the same assistant could be engineering-labour-saving both per feature and in total. If it raised the marginal product of AI infrastructure more than that of engineers, it could be capital-biased rather than labour-biased.

For generative AI, then, the framework produces a clear but conditional assessment: identify the technology’s relative productivity effect, state which factor and margin are being discussed, and then examine whether the scale effect outweighs the saving per unit.

Applying the framework to agentic AI

Generative AI mainly assists people with particular tasks. Agentic AI extends this by carrying out sequences of work: gathering information, selecting actions, using software tools, checking intermediate results and referring uncertain decisions to a person.

This changes the analysis because “labour” is no longer a sufficiently precise category. It becomes useful to distinguish between two broad types of work.

Execution work

Execution work includes producing standard code, processing routine requests, collecting information, preparing documents or transferring work between systems.

As agents become capable of completing longer sequences of these activities, less human time may be required for execution at a given level of output.

Complementary work

Complementary work includes defining objectives, exercising judgement, reviewing uncertain results, handling exceptions and taking responsibility for consequential decisions.

Agentic systems can increase the reach of people doing this work because each person can direct and evaluate a larger volume of machine-assisted activity.

These are categories of activity, not fixed categories of workers. A software engineer, lawyer, analyst or manager may perform both kinds of work. As AI capabilities develop, the balance within each role may change.

A software engineer, for example, may spend less time producing routine implementation and more time on architecture, system constraints and evaluation. A domain expert may examine more cases while concentrating attention on ambiguity and exceptions. A manager may coordinate a larger volume of activity without directly supervising every step.

The economic effect is therefore not only a change in how efficiently existing tasks are performed. It is also a change in how tasks are grouped into roles, how workflows are coordinated and where human attention is most valuable.

A plausible pattern for agentic AI

Once labour is divided into different forms of work, several effects can occur simultaneously.

To make the framework concrete, suppose an agentic system can complete routine production steps that were previously performed and coordinated by people. The company invests in additional compute, models and agent infrastructure. Suppose the system raises the marginal product of that capital relative to human execution labour, increases the marginal productivity of complementary labour relative to execution labour, and produces enough additional activity to increase the need for human direction, evaluation and exception handling.

Under those assumptions, a plausible description is:

• Capital-biased relative to execution labour as a technological change, because the system raises the marginal product of AI capital relative to the marginal product of human execution work. This is a stated assumption in the example, not a conclusion that follows simply from the use of more capital.

• Capital-using in total, because the company employs more compute, models and agent infrastructure.

• Execution-labour-saving per workflow, because less human time is required to perform and coordinate routine production steps for a given amount of output.

• Biased towards complementary labour relative to execution labour, because agents increase the productive reach of people providing judgement, direction and evaluation relative to people performing routine execution work.

• Complementary-labour-using in total, because the assumed growth of AI-enabled production increases demand for direction, evaluation and oversight.

There is no contradiction here. Each description refers either to a different input or to a different margin of adjustment.

Agentic AI might reduce the execution time required to produce a piece of software while increasing the amount of software a company chooses to build. It might allow one experienced engineer to oversee more implementation while also making experienced engineering judgement valuable across a wider set of projects.

Whether the company ultimately uses more or less of a particular kind of labour depends on how these effects combine.

This is a potential verdict for a particular use of agentic AI, not a universal verdict on the technology. If agents did not raise the marginal product of capital relative to execution labour, the technological change would not be capital-biased on that comparison. If AI-enabled activity did not expand sufficiently—or if direction and evaluation could also be automated—total demand for complementary labour might not increase.

The important shift may be in the composition of work

The most immediate question about AI is often whether it will increase or reduce demand for labour. But that may be too broad to capture the more interesting changes.

The effects are likely to differ not only between occupations, but also among tasks within the same occupation. As some activities become easier to delegate, human effort can move towards setting direction, integrating knowledge, resolving ambiguity and evaluating results.

Generative AI raises the productivity of individual tasks. Agentic AI can go further by reorganising how those tasks are combined into workflows. Its economic significance may therefore lie as much in changing the composition and coordination of work as in reducing the cost of any individual activity.

This is why terms such as “capital-biased”, “labour-saving” and “labour-using” should not be treated as competing verdicts on AI. They answer different questions and may refer to different inputs or margins of adjustment.