I like to think about and discuss business. It is what economics has helped me do. It was why I was attracted to a career in economics in the first place. I was posed an issue by a tenant of a landlord that planned to compete directly with their retail offering. I applied my mind, essentially following the money trail, for clues about the rationale for the intended action of the landlord and its likely consequences. And finding arguments for the tenant to advance as to why the landlords might be persuaded not to proceed- in their own interest essentially – and to the advantage of the tenant
Economic action is not a game played once off for amusement or small stakes. It is played over a lifetime of endeavour for cumulatively very large stakes. Rationally – in a present value maximising over the long-run – using pattern recognition –repeated trial and error- as the path forward. If it seems to work the action is repeated until it doesn’t and the business, to survive or better still prosper for its owners and managers, adapts and innovates according to the continuous flow of evidence of sales and costs.
The assumption of Homo economicus following the money – if you can understand and describe it well enough – will be essential to the purpose of the analysis of any business enterprise. An essential tool with which to identify the business model, the theory and strategic insights that drives the actions observed. The well-informed investment analysts who are rewarded for valuing any business will identify the moats that protect the business from competition and the runway of future opportunities to grow profitably. And recognises the KPI’s that drive the remuneration of the key executives and therefore the actions of a business enterprise.
I thought I would test my reasoning and logic by asking the same landlord-tenant question of AI. And the answer received immediately, and almost exactly, repeated my own reasoning and conclusions. Not surprisingly, because the question had been asked before and answered similarly. As the bot was able to recognise from the complete record of thought and action. AI did as well as I did much more rapidly and conveniently in answering an interesting but clearly not an original question. In other words, if you are having to cope with a complex issue with contractual implications that will affect future income, refer conveniently to the bot and take seriously what they come up with. An expensive authority is not necessary to the purpose.
What matters most in economics is the importance of the question asked. If the question is interesting and relevant, the answers then follow almost automatically. The best economists identify interesting questions to ask – and provide their own compelling answers for them. Which by now will be picked up systematically by the computers that power AI. The test of any student utilising AI, as not in the quality of answers provided by AI, but rather have they asked an interesting question- and are they capable of interpreting the answers provided?
But what then of the role of true originality? New questions asked and answered that move forward so helpfully, the frontier of knowledge to be applied creatively. That then become part of the wisdom to be recorded digitally. Academic research has been valued for its originality. For the citations earned by scholars in the same field who recognise the contribution made by the inventor of an idea and its formulation. Published research is for the record. It is public knowledge freely available- and now collected systematically and comprehensively by the bots.
The reward and incentive for the original researcher comes in the form of salaries and research grants and promotion up the academic ladders. The laws that protect copyright and confer time limited monopolies of intellectual property in the form of patents can usefully encourage originality. Patents limit property rights, but the limits are sensibly not such as to inhibit invention. Pay for copyright can perhaps be extended to the actions of bots that are being monetised for their owners.
More important than solving the ownership of intellectual property issues raises another question. Can the bots however do more than keep the record? Can they advance knowledge originally in ways that can further advance the human condition? Can they observe the world around them to observe patterns and come up with helpful explanations and actionable theories that add to output and utility. As scientist and analysts of all kinds, including economists do.
Much scientific enquiry is a mixture of observation and generalised theoretical explanations that hopefully can be tested with evidence from repeatable experiments. An interdependent mixture of induction – seeing the patterns – and deduction – making sense of them. The bots can record and measure and summarise the established questions and answers. But can they be creative in the way the best scientists and analysts are creative? Can they see the world from their data centres and exercise the imagination of the intended experiments that is the stuff of true creativity? I would suggest not. Creativity, originality, rather than the literature review mastered by the bots, is the path to true excellence in science and the arts. Which raises the further question. How can creativity be stimulated by individuals or teams of them? And further what can educators do to foster creativity?