There is a belief among some economists that AI would be as pivotal to the multi-faceted application of technology as the steam engine was to manufacturing and automotive industries and as the semiconductor has been to computing technology. This is due to its quality of being a predictive and prescriptive tool relevant in fields ranging from the financial sector to the agricultural industry.
Macro View (the big picture)
- The use of AI would inevitably reduce the number of human workers with repetitive tasks as processes get more streamlined and redundancies/dispensable factors are eliminated. An example cited by Nesta is the adoption of an automatic tool for recommending books resulting in human book reviewers losing their jobs.
- AI improves efficiency in terms of the output of human tasks. Costs that might have accrued due to calculations and simulations needed for prototype creation and scenario reproduction can be reduced or altogether avoided. The only workers that would benefit from this are those involved in work that can be enhanced rather than eliminated by AI.
- Higher quality products and services will be achievable using AI, resulting in greater customer satisfaction and increased revenue.
- As much as it is a problem solver, AI poses new ones, especially machine learning, creating new jobs.
The efficiency of AI will result in more significant innovation, which opens up new industries and diversifies existing ones. The above instances go a long way in refuting the prevalent opinion that the advent of AI heralds a shift towards wide-scale unemployment. Still, the fears are not unfounded, especially if firms take a half-hearted route towards AI incorporation, causing job losses but not opening the avenues for creating new ones.
This view considers AI’s effect on specific jobs/fields and policies that might check the more negative effects. An example to illustrate this is the use of AI in the agricultural sector; by taking weather and soil constitution into account, the ideal crops to be grown and livestock to be reared can be easily projected. The most efficient machinery can also be known beforehand.
This knowledge will inevitably lead to many planters, harvesters, and other similarly non-essential personnel being laid off. At the same time, skilled technicians would be engaged to manage machinery and machine learning systems that are to be utilized.
In this scenario, increased crop yield and optimized management of livestock and processing measures bring the firm profits, but many people have lost their means of livelihood. This indicates that a wage gap could become evident between the qualified beneficiaries of AI (management personnel and skilled technicians) and the unskilled (and very likely uneducated) losers, which would be detrimental to the socioeconomic balance if left unchecked.
AI has been engaged in some health facilities to reduce scheduling errors, reduce the time taken in fulfilling preregistration requirements, etc. This affords medical personnel time to focus on issues more closely related to health that would have been expended on menial pursuits.
In manufacturing-intensive economies like those of Asian countries, AI has contributed to significant growth with projections of a sharper increase as aspects of its application become more fine-tuned. AI in manufacturing will lead to AI taking up more dangerous jobs potentially harmful to humans either by accidents or substance toxicity.
This focuses on how organizations and people will be affected by AI. It includes taking AI limitations into account as well as its progress. From an organizational standpoint, there is a reservation that AI would lead to a monopoly in which pioneering firms would wield a disproportionate financial strength, leaving individuals and possibly nations at their mercy.
Because the prominent drivers of AI (who unsurprisingly are also the significant researchers in the field) are profit-driven institutions, socioeconomic balance is unlikely to rank high on their list of considerations regarding the effects AI would have on a more significant economic scale. This possibility of an economic catastrophe can only be avoided by political elements, particularly governmental bodies and their regulatory arms becoming early players in the AI game with the primary role of acting as referees.
This is an exploration of the means through which AI methodologies can be used to examine AI itself. Machine learning tools can be harnessed to investigate the process used by AI to arrive at specific results.
While it is likely that AI would dramatically transform the workspace, there is no concrete evidence that it would be a negative transformation. Job specifications and roles will experience evolution as AI permeates industries. Companies will make successful and failed forays into AI adoption, and an AI economy will have a lot in common with the internet economy we are currently experiencing.
Impact in Economics as a Discipline
Unlike conventional business models with a capital consisting primarily of machines and buildings (physical structures), AI deals with intangibles like data and information whose creation is more complicated and whose security in terms of inability to be compromised is never wholly assured. This estimates potential worth more guesswork than statistic-based at present.
AI will also be instrumental in understanding economics itself and aiding in formulating and analyzing economic theories. Natural sciences involve examination of existential data, then modeling and projecting possible outcomes. Economics, on the other hand, has the added factor of unpredictability/irrationality of human behavior.
One of AI’s core strengths is sifting through large datasets and identifying even the most subtle patterns that would typically escape previous observation methods. This would be invaluable in recognizing human behavior based on choices made and predicting future decisions to a more accurate degree. The application of AI in economics is still in its most rudimentary stages.
Developed countries have a lower GDP to growth ratio increase due to the aging population and higher wage rate than developing countries. Introducing AI-capable machines would lower this wage rate and negate the aged population barrier. This could result in these developed countries having the economic edge over the developing ones, which will take longer to adopt AI.