AI-based prediction is a boon to human decision-making that will enhance productivity, income and the quality of life.
In a nutshell
- There is a division of labor between humans and technology
- Artificial Intelligence frees people to focus on what they as individuals do best
- AI-based prediction improves human decision-making and enhances wealth
The main concern of classical economics revolved around how specialization and exchange make human life better. One of the main advantages of artificial intelligence (AI) lies in enabling further specialization and division of labor. AI processes data to predict and humans use the predictions to make decisions.
Let us unpack this phenomenon now.
Humans and technology
Early-generation, or classical, economists were particularly concerned with how the division of labor along the lines of freely chosen specialization allows people to benefit from a society-wide chain of value creation. The father of modern economics, Adam Smith (1723-1790) established that specialization drives surpluses and innovation. David Ricardo (1772-1823) identified how even the least productive members of society could benefit from specialization. And even the angry Karl Marx (1818-1883) saw the advantage of the division of labor. His erroneous qualm was with the capitalist retaining the benefits of the division of labor instead of allowing them to flow to the specialized worker.
Specialization, however, is not restricted to humans. The more technology permeates economic exchange, the more there is a labor division between humans and technology. Instead of weaving by hand, most textile producers use machinery for that task. The machine makes the fabric that has been designed and engineered by people. Instead of doing arduous calculations ourselves, we let the calculator perform the arithmetic of models conceived by humans. The Bloomberg Terminal freed the human analyst from writing quotes on a chalkboard to be able to focus on deciding how to invest. In short, utilizing technology is about finding its specialized employment along the value creation chain.
The technology is becoming cheaper to use, making it increasingly significant economically.
Technology enhances the division of labor between humans and machines. It allows each to specialize even further in what they respectively do best. And this comes to the greatest benefit to humans. This division of labor enables them to create novelties, produce more goods and increase quality. By making people able to focus on what they individually do best, specialization and the division of labor enhance their productivity and, along with it, their income and the quality of life.
Making prediction cheaper
How does AI fit into this picture? As the prices of weaving machines, calculators, and computers decreased, they were incorporated into the division of labor. The same is happening with AI. The technology is becoming cheaper to use, making it increasingly significant economically. And while AI is making progress in different applications, it is especially salient in one area – the making of predictions.
Presently, predicting is expensive because it involves gathering an enormous amount of data, analyzing it, identifying patterns, and calculating possibilities. But, as AI is increasingly employed in performing exactly these tasks, prediction is becoming less arduous and less expensive. In a virtuous circle, AI is becoming cheaper itself. If predicting becomes cheaper through AI, the scope of its use will widen, and its use will intensify.
Prediction is the process of filling in the missing information. It uses available data to find patterns to generate new information and calculate probabilities for these patterns to repeat themselves or change. Prediction is used for traditional tasks, like inventory management and demand forecasting. More significantly, because it is becoming cheaper, it is being used for undertakings that were not until recently prediction problems, such as driving, translating, or medical care.
The drop in the cost of AI prediction will influence the value of other things. A cheaper AI increases the value of such complements as data, judgment, and action and diminishes the value of its substitutes (human prediction).
Machines and humans have distinct strengths and weaknesses in the context of prediction. Humans, including professional experts, make poor predictions under certain conditions: they often overweight salient information and do not account for statistical properties. As prediction machines improve and become cheaper, businesses will likely adjust the labor division between humans and machines in response.
Prediction machines are better than humans at factoring in complex interactions among different indicators, especially in settings with rich data. As the number of dimensions for such interactions grows, the ability of humans to form accurate predictions diminishes, especially in comparison to machines. However, humans are often better than machines when their understanding of the data generation process confers a prediction advantage, especially in settings with limited data. Humans are better at handling uncertainty and significantly better at making decisions based on prediction.
As prediction machines make predictions increasingly better, faster, and cheaper, the value of human judgment will increase.
So, AI’s specialization in prediction is valuable because it can often produce better, faster, and cheaper predictions than humans. Prediction is a crucial ingredient in decision-making under uncertainty, and decision-making is ubiquitous throughout the economy and social lives. However, a prediction is not a decision, only a decision component, and another crucial component is judgment. And here lies the advantage of the human – and therefore, their area of specialization, human judgment.
It helps to break down a decision into its components to understand better the impact of prediction machines on the value of humans and other assets. The value of substitutes to prediction machines – namely, human prediction – will decline. However, the value of prediction-making complements, such as human judgment skills, will increase.
Judgment involves determining the relative payoff associated with each possible decision outcome, including those associated with a correct decision and those associated with mistakes. Judgment requires specifying the objective that one is pursuing and is a necessary step in decision-making. As prediction machines make predictions increasingly better, faster, and cheaper, the value of human judgment will increase because society will need more of it and will value it better – or price higher. Humans may be more willing to exert effort and apply judgment to the cases where previously they abstained from deciding (e.g., by accepting the default situation or setting).
The quality of AI prediction would most likely increase faster than its price goes down.
Why is it that humans are better at judging than AI? While the machine can account for more data, and therefore, more patterns, to make a prediction, the human has a gut feeling. Human agents can deal with gaps in a dataset and fill those gaps based on intuition, and no AI can do that. Also, humans are good at dealing with and responding to “unknown unknowns” or “black swans.” Arguably, this skill set comes from two different sources. First, humans see uncertainty as a resource or a chance leading to opportunities. Second, humans need to take responsibility for their judgments, which hone their decision-making capability.
AI is best thought of in terms of how it is used and what it does instead of what it is. Presently, prediction is one of its most vital applications. The cheaper AI predictions become, and the more and better AI predictions are made, the more the technology can be used as an asset by economic agents. It could be employed in tasks specialized in prediction making, which free humans to specialize in judgment and decision-making activities. This incorporation of AI in a chain of value creation, utilizing its specialization and division of labor with humans, is broadly beneficial.
By allowing the human to focus on what it does best, prediction-specialized AI increases human efficacy and efficiency and, as a result, income and quality of life.
Four broad scenarios emerge from this reasoning.
The first, base scenario
AI continues to increase its prediction capabilities, making them better and cheaper. This outcome could be expected if current AI-related investment and research trends continue. The quality of AI prediction would most likely increase faster than its price goes down. As a result, a lower rate of adoption of AI prediction among economic agents could be expected than the rate of the technology’s ability increase.
The second scenario
This scenario builds on the first. Under this script, the economy will incorporate AI into various activities, increasing the degree of specialization. Machines will specialize in making predictions and humans will specialize in making judgments and deciding. Human decision-making will become better and more valuable with the increased division of labor within this alliance. Also, the demand for it will grow. Likely, the increases in decision-making quality will be faster than its valuation growth. Under what conditions could this scenario materialize? It hinges on the degree of freedom that economies and individual entrepreneurs will have in developing and implementing the technology.
The third scenario
This one also builds on the first. But it concedes that, at least in the short to medium run, specialization and division of labor might not occur. There could be different reasons for this. If economic agents perceive AI as a threat instead of an asset, they may try to compete against it. Instead of allowing for specialization along the lines of the different advantages, humans would direct their abilities to less productive tasks, such as data gathering and predicting. Another possible reason for this scenario would be regulation preventing the use of AI or curbing its functions. The third trigger for this scenario would also be regulation barring or diminishing the economic payback from using AI. For example, by not allowing investors to finance it or retain the revenues from its use.
The fourth scenario
This scenario does not relate to any of the previous three. Here the focus of AI changes completely, and it shifts away from predicting to some different application, e.g., sensors or robotics. In this scenario, AI can still be used to the advantage or disadvantage of economic agents; AI could even lead to specialization and division of labor. But in this variant, specialization would not occur along the lines of machine prediction and human decision but result from other applications. Similarly, in this scenario, there is an upside and downside potential. The fourth scenario could materialize if the returns from prediction fall considerably or if other uses of AI become cheaper faster, making these uses easier to fit with the chain of added value.