Entering 2023, most economists predicted imminent recession. However, the year ended with surprisingly robust economic data. All-item CPI (12-month % change) dropped to 3.4% in December 2023 from its peak of 9.1% in June 2022. Unemployment rate was below 4% for 24 straight months. A survey on consumer finance from 2019-2022, published by Fed, also indicated the growth in both income and wealth is near universal across different types of families. Stock market delivered great performance with S&P 500 ending more than 24% gain and the Dows closed 2023 near a record high.
Yet, there is a huge disconnect between the solid economic data and the strong sense of insecurity among Americans. Consumer sentiment has remained pessimistic as many “feel their long-term financial security is vulnerable to wide-ranging social and political threats.”
Increased inequality and threats towards traditional middle-class jobs due to Artificial Intelligence drive the sense of insecurity in society. Based on a report from the World Inequality Database, the Gini coefficient of pre-tax national income increased to an all time high of 0.626 in 2022 since 1913. The bottom 50% population share of income is 13.3%, but only 1.5% of the total wealth. Since 1980, the top 10 income shares have increased dramatically while the remaining 90% declined. The 2023 stock market reflected that nearly half of the stock market’s overall gain came from the so-called “Magnificent Seven” with Nvidia leading the growth of almost 240% of the year. Many of the “Mag Seven” are the leaders in AI technology.
The continued rise of inequality and pervasive mood of financial insecurity bring the century-old concept Universal Basic Income (UBI) back to the spotlight. The productivity gain and the expected transformation brought by AI ignited a new enthusiasm to pilot the UBI system, hoping to adapt social welfare into this new age.
The Past and Present of UBI
The idea of Universal Basic Income (UBI) dates back to the 16th century to Sir Thomas More. Today, the widely agreed definition is from the Basic Income Earth Network (BIEN) that “a basic income is a periodic cash payment unconditionally delivered to all on an individual basis, without means-test or work requirement.”
There are five distinctive characteristics that set apart UBI from traditional social welfare programs: it is universal, regular, unconditional, cash payment, and meets basic needs. UBI challenges the traditional welfare concept by removing its association with the words poverty, traditional, and contemporary.
For a long time, UBI was not popular in the U.S. primarily due to its being universal and unconditional. The arguments against the program centered around its exorbitant cost and its potential to create laziness and hatred towards work. The proponents for the program emphasize its “social safety net” nature. However, the rise of global inequality from automation and AI has motivated many countries to start piloting small-scale UBI programs in different forms.
The concept caught national attention in the U.S. when Andrew Yang proposed a $1,000 monthly UBI, a key policy during his 2020 Democratic presidential campaign. The Covid-19 pandemic provided the opportunity for many countries to test the UBI concept. In fact, the pandemic relief programs from the federal government in the U.S. are considered experiments of UBI in its own form. They are the direct cash payment from the government to most of American citizens’ bank accounts for recipients to spend at their discretion with no strings attached. This direct cash deposited provided much-needed assistance to most recipients well above the poverty line during the pandemic economic disruption. Arguments existed regarding its impact, but the program undoubtedly helped many individuals during the economic shut down.
At present, more pilot programs across the U.S. are in the progress of exploring the best social program to solve the inequality. In Silicon Valley, many technology entrepreneurs with notable names are founding various UBI programs.
The Present and Future of AI
The IMF published Gen-AI: Artificial Intelligence and the Future of Work in January 2024. The analysis indicates that “60 percent of jobs are exposed to AI, due to prevalence of cognitive-task-oriented jobs in advanced economies, about 40% exposure for less advanced economies.” At the same time, advanced economies are better positioned to capture productivity gains from AI.
Contrasting previous trends of technology advancement, “AI challenges the belief that technology affects mainly middle and, in some cases, low-skill jobs: its advanced algorithms can now augment or replace high-skill roles previously thought immune to automation.”
Decades of data show the disconnect between productivity and wage increase. From 1979 to 2020, net productivity rose 61.8%, while the hourly pay of typical workers grew only 17.5% over four decades (after adjusting for inflation).
From this, a big question emerges: while AI might surpass human intelligence in the future, and complete more sophisticated jobs at much lower marginal cost, where is our world heading? When AI disrupts the traditional connection among cost, price, and value, it is time for us to reconsider how we align social welfare framework with the future society.
When UBI meets AI
The biggest hurdles for UBI are the traditional beliefs that cash payment must be earned and the exorbitant costs of the program.
The technological transformation redefined the value and nature of work in a gradual yet profound way. It invites a fresh perspective in understanding how the digital economy, which propelled AI acceleration, impacts our society. How can we better align the value created from the increased productivity with the well-being of humanity?
Professors Bryjolfsson and Collis argue that the traditional GDP is misleading in its ability to accurately reflect the digital economy because it “measures only how much we pay for things, not how much we benefit.” Most of the digital economy is based on ads revenue on free content. Multiple studies show that the consumer surplus value (the difference between willingness to pay vs. actual price charged) of the content does not correlate with the price, in fact, much higher when compared to the ads revenue generated per user. The article proposed “GDP-B” metrics to capture “the consumer surplus generated by free digital goods and other non-market goods.”
Similar logic also applies to the cost side of the digital economy, yet cost is even harder to quantify. Data is the critical factor of the digital economy to create value. Never before have all the intricacies of our lives been digitized with such precision. Everything from daily trips, conversations with friends, purchase transactions, to internet searches, has been tracked, stored, and analyzed. Raw data similar to such routine is worthless at an individual level, yet at an aggregate level, provides valuable insight for businesses to better target and achieve higher efficiency, and translate it into more profitable business for shareholders. A digital economy enables every citizen to contribute data like raw materials, but does not have a method to calculate nor pay their fair share.
There is little doubt that AI advancements will result in the overall productivity gain, which brings a shift in values and causes disruption in society. The recent study from Stanford and MIT measured how Generative AI tools improve productivity. It suggests that “access to generative AI can increase productivity, with large heterogeneity in effects across workers.” The AGI tool helped low-skilled workers achieve highest improvement with minimum impact on experienced and highly skilled workers. The evidence suggested that “the AI model disseminates best practices of more able workers and helps new workers move down the experience curve.” Yet, this previously uncovered value from best practice data was not paid to all the workers contributing to the training, it is harnessed by the company’s shareholders via increase in its enterprise valuation.
With AI enabled in productivity gains, less labor is required, and more value will pass to the shareholder in the form of capital gain. Rarely do data creators, unless they are also shareholders, capture the proper share of the value as basic income. Furthermore, firms who are more strategically positioned to benefit from artificial intelligence should fairly transfer their gain to a wider population who have provided data, collectively enabling their technological leadership throughout the years rather than solely distributing them to the shareholders. Today, it is challenging to quantify the worth of raw data at various levels. However, the impact of artificial intelligence on conventional employment and value generation will prompt further consideration of establishing a fresh approach to distributing the value across a wider range of creators. This approach will set the foundation for UBI.UBI is an ambitious plan with no clear timeline to realize its goal in full. AI already delivered great productivity gains, although how fast AI alone can enable an affluent society to support UBI is unknown. It was estimated UBI could add as much as $2 trillion in annual expenses to the U.S. budget. Professor Brynjolfsson estimated back in 2016 that the “idea of a basic income is a good one in a world where robots do most of the work, but we probably won’t be there for 30 to 50 years.” However, limited forms of UBI would provide a financial safety net to a larger population who feel most threatened in the emerging era of artificial intelligence. It takes time to formulate precise measurements of value and cost in a fast-evolving digital economy, but it is never too late to distribute the fair share to the people who contribute to the value creation process.
Bibliography
Brynjolfsson, Erik, and Avinash Collis. “How Should We Measure the Digital Economy?” Harvard Business Review. Entry posted November 2019. https://hbr.org/2019/11/how-should-we-measure-the-digital-economy#:~:text=GDP%2DB%20is%20an%20alternative,well%2Dbeing%20from%20free%20goods.
Brynjolfsson, Erik, Danielle Li, Lindsey R. Raymond. “Working Paper: Generative AI at Work” National Bureau of Economic Research. Last modified November 2023. https://www.nber.org/papers/w31161
Cazzaniga, Mauro, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus J. Panton, Carlo Pizzinelli, Emma J. Rockall, and Marina Mendes Tavares. Gen-AI: Artificial Intelligence and the Future of Work. N.p.: International Monetary Fund, 2024.
Freedman, David H. “Basic Income: A Sellout of the American Dream.” MIT Technology Review. Entry posted June 13, 2016. https://www.technologyreview.com/2016/06/13/159449/basic-income-a-sellout-of-the-american-dream/.
Hamilton, Leah, Meric Yorgun, and Allison Wright. “‘People Nowadays Will Take Everything They Can Get’: American Perceptions of Basic Income Usage.” National Library of Medicine. Last modified July 28, 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317143/.
Jäger, Anton, and Daniel Zamora Vargas. Welfare for Markets: A Global history of Basic Income. N.p.: The University of Chicago Press, 2023.
Lauricella, Tom, and Lauren Solberg. “15 Charts On the Surprise ‘Everything Rally’ for 2023.” Morningstar. Entry posted January 2, 2024. https://www.morningstar.com/markets/15-charts-surprise-everything-rally-2023.
“The Productivity–Pay Gap.” Economic Policy Institute. Entry posted October 2022. https://www.epi.org/productivity-pay-gap/.
Survey of Consumer Finances (SCF). N.p.: Board of Governors of the Federal Reserve Board, 2023. https://doi.org/10.17016/8799.
World Inequality Database. https://wid.world/world/.
Zitner, Aaron, Amara Omeokwe, Rachel Wolfe, and Rachel Louise Ensign. “Why Americans Are So Down on a Strong Economy.” Wall Street Journal, February 7, 2024. https://www.wsj.com/economy/economy-inflation-consumer-spending-unemployment-e6856381?mod=economy_lead_pos4.