Alap Shah is the co-author of a very popular piece of work in the field of macroeconomics that really made global markets pay attention. The essay, ‘The 2028 Global Intelligence Crisis,’ says that AI and the automation it causes could show a false, encouraging picture of how the economy is doing.
Alap Shah’s background and career
Shah got his BA in Economics, with honors, from Harvard University. For fifteen years he worked in professional investing, in both the public and private sectors. At the start of his career, he managed world equity portfolios for some of the best hedge funds and worked in leveraged buyouts and growth equity. In December 2011, Shah co-founded Sentieo – an AI financial research platform – and was in charge as the company got a lot of funding and grew. He was CEO until September 2020 and stayed Chairman until May 2022, when Sentieo was bought. Shah also co-founded a business that sells consumer health foods by subscription, and still has leading roles in a number of investment groups.
The ‘2028 Global Intelligence Crisis’ thought experiment
The essay is set up as a situation from 2028, looking back, to show how AI being adopted could change economies. It was co-authored with a research group that doesn’t have any connection to any one institution and was written to be a story going backwards in time. It looks at a future in which AI goes from helping workers to taking the place of many expensive workers in many different fields. The way it’s done stresses the dangers to the system as a whole and is meant to get investors, the people in charge of companies, and people who make policy to talk.
Main idea and how the piece got around
The main idea is that if automation became widespread, the numbers the economy puts out would become separate from how income is spread among households. The piece made up the phrase ‘Ghost GDP’ to mean the economy putting out more and more, but that not turning into people having more money to spend. Because it was shared so much online, the situation got to trading floors, boardrooms, and policy meetings, and made the debate about how the economy is doing and how stable society is more intense. Shah’s professional good name gave the work more attention, because of his experience building AI tools for financial research.
‘Ghost GDP’ and what it means for the macroeconomy
‘Ghost GDP’ pictures company profits going up while the pay of people in the middle class stays the same or goes down. In that world, how much is produced would go up even as people buy less, hurting the things that have historically made the economy grow. The situation warns that markets may at first praise companies for increasing their profits, before weak demand from households creates big problems for the economy. That difference between the headline numbers and what people really earn creates problems for policy about giving the government’s help, taxes, and changes to the job market.
How white-collar jobs and markets could be hurt
Shah and his co-author show a chain of events starting with automation of expensive white-collar jobs. Less people employed and less money earned could weaken buying, and hurt the money many businesses make in the normal ups and downs of the economy. The value of stocks that expect people to always keep buying things may have to be looked at again if a lot of workers are displaced. The thought experiment says to watch closely how much each worker produces, how wages change, and how companies spend money to find early signs of things being out of balance.
Why the situation matters for investors and policymakers
The piece isn’t put forward as a prediction, but as a helpful idea for planning and managing risk. Shah’s mix of experience in investing and building AI products gives the situation a practical meaning. The debate it started shows how technology can change the macroeconomy faster than institutions can get used to it. Companies, investors, and the people who make rules may have to rethink how they measure things, the safety nets they have, and what rewards they give, to make sure that the gains from automation are shared by everyone in society. The essay has brought attention back to the link between technology and the macroeconomy. Whether it is seen as a warning or useful advice, Shah’s situation forces a clearer talk about how the gains from AI should be spread among people.
