How to understand AI's potential impact on knowledge jobs

For example, the main inputs in the legal services industry are the time and expertise of lawyers and paralegals, while the main outputs are essentially solutions to legal problems, in the form of legal advice and representation.

The growing importance of knowledge work in the modern economy implies that the capacity to adapt and solve problems — knowledge — is increasingly valued.

But time and knowledge are scarce and, as a result, organizations often adopt pyramidal hierarchies to economize on these resources. Returning to the legal example, law firms usually employ several associate lawyers per partner. Associates typically focus on relatively routine legal problems, allowing partners to use their expertise to solve the most important cases.

There’s another mechanism at work, which economists call positive assortative matching. Better associate lawyers tend to work with better partners, because it’s mutually beneficial. That means more knowledgeable individuals are more productive and earn more, not only because they can solve more problems but also because they work in better organizations.

Also read: 170 million new jobs to be created by 2030, but 92 million to be displaced: World Economic Forum

Non-autonomous AI: Human intervention and productivity gains

If we think of AI as an algorithm that uses computing power to automate knowledge work, its potential to impact the knowledge economy becomes readily apparent.

AI can roughly be divided along autonomy lines. Non-autonomous AI lacks the ability to operate independently and, hence, always requires human assistance to be able to produce knowledge work. Autonomous AI solves problems with only minimal human intervention.

Non-autonomous AI can only collaborate with humans, and labor productivity is greatly enhanced as a result. This synergy ensures that human labor remains essential, particularly in assisting AI in those contingencies where human-like intelligence is required. This results in humans moving into roles complementary to AI rather than being replaced by it.

The introduction of non-autonomous AI may still generate disparities in wages. Individuals with higher knowledge levels benefit more, as they can better assist AI and handle more complex problems. Over time, as AI technology improves, wages across the entire knowledge distribution can increase, leading to widely shared prosperity.

Autonomous AI: Basic or advanced knowledge

The labor outlook becomes more complex when considering autonomous AI, or AI that is indistinguishable from human intelligence and can solve problems with minimal human assistance. The realism of this scenario is hotly debated and may vary by industry.

The impact of autonomous AI on knowledge work depends on whether its knowledge is relatively basic (i.e., it can mimic humans that perform routine knowledge work) or advanced (i.e., it can mimic humans that perform specialized problem-solving).

Consider first an AI that can mimic the tasks of an average legal associate (i.e., a relatively basic AI). Introducing such AI effectively increases the supply of legal associates, reducing their wages and increasing the demand for more specialized problem-solving (i.e., legal partners). As a result, the most knowledgeable associates become partners, so AI’s introduction displaces humans from routine roles to more knowledge-intensive positions. In other words, AI increases the knowledge content of human work.

This occupational displacement implies that, in this case, the introduction of AI leads to smaller, less productive and more centralized organizations. Why? Because the knowledge of the best associates is lower post-AI than pre-AI, leading to a decline in the quality of the legal associates at the best legal firms, and increasing centralization. In addition, the knowledge of the least knowledgeable partners is also lower post-AI than pre-AI, and hence, the productivity of the least productive firms is also lower.

The effects of AI are substantially different when AI is sufficiently advanced that it can mimic those higher in the knowledge hierarchy. In this case, AI decreases the knowledge content of human work, leading to larger, more productive and more decentralized firms. For instance, an AI assistant automating specialized customer support effectively reduces the scarcity of these agents, forcing the least knowledgeable among them to shift to routine customer support.

Irrespective of whether AI is basic or advanced, its introduction increases total labor income, but it necessarily creates winners and losers in the labor market. In particular, the least knowledgeable humans benefit when AI’s knowledge is high because its introduction gives them access to better and/or less expensive solvers. The most knowledgeable humans, in contrast, always benefit from AI — regardless of AI’s knowledge level — because it serves as a relatively inexpensive technology to leverage their knowledge.