What’s Wrong with ChatGPT? A view from Economists

Renowned economists—Daron Acemoglu and Simon Johnson—are concerned about ChatGPT. More precisely—the way how AI deployed by corporations in the US. Their analysis points out that it could displace workers, harm consumers, and bring losses to investors. The crux of the issues is focusing on cutting labour costs (in a short run), with little regard for the future of spending power and workers earnings, as well as neglecting the potential benefits of AI.

🤖 AI arms race, funded by billions from companies and venture-capital funds, bringing in a technology that can now be used to replace humans across a wider range of tasks. This could be a disaster not only for workers, but also for consumers and even investors.

👨‍🏭 The workers are facing clear and present danger. The job market is shifting, resulting in a decrease in demand for positions that require strong communication skills, ultimately leading to a decrease in higher-paying jobs. This trend is particularly challenging for younger people, just starting their careers, as there will be fewer entry-level positions available. AI powered tools could help in legal research, but deprive novice lawyers of learning techne through hands-on research.

🛍 Consumers, too, will suffer. Although they may suffice for routine inquiries, they are inadequate for addressing more complex issues—flight delay, household emergency, or dealing with a breakdown in personal relationships. We need understanding and actions of qualified professionals, not eloquent but unhelpful chatbots.

💸 Investors could also be disappointed as companies invest in AI technology and cut back on their workforce. Rather than investing in new technologies and providing training for their employees to improve services, executives are more interested in keeping employment low and wages as low as possible. This strategy is self-defeating and could harm investors in a long run.

🐙 The crux of the issues is that the potential of AI is being overlooked as most US tech leaders are investing in software that can replicate tasks already performed by humans. Contrary, AI-powered digital tools can be used to help nurses, teachers, and customer-service representatives understand what they are dealing with and what would help improve outcomes for patients, students, and consumers. The focus is primarily on reducing labor costs with little regard for the immediate customer experience and the long-term spending power of Americans. However, history has shown that this approach is not necessary. Ford recognized that there was no point in mass-producing cars if people couldn’t afford to buy them. In contrast, modern corporate leaders are utilizing new technologies in a way that could have detrimental effects on our future.

Read full article https://www.project-syndicate.org/commentary/chatgpt-ai-big-tech-corporate-america-investing-in-eliminating-workers-by-daron-acemoglu-and-simon-johnson-2023-02

P.S. I am currently reading “In The Age Of The Smart Machine: The Future Of Work And Power” by Shoshana Zuboff. The book published back in mid-1980s explores impact of the first wave of smart machines on labour relationships and future of work. There are a lot of similarities and lessons learned for current wave of ubiquitous AI-fication.

The Rise of the Data Elite: How AI Research is Re: inforcing Power Imbalances

The rise of AI-powered tools is transforming our everyday lives. We use the magic of ChatGPT and Midjourney and more mundane AI-powered credit profiling and email completion tools. However, the democratization of AI use is accompanied by global power disparities in AI research. A chart from the “Internet Health Report 2022” shows that the landscape of AI research papers is heavily skewed towards a few countries and elite institutions. The map reveals that more than half of the datasets used for AI performance benchmarking were from just 12 institutions and tech companies in the United States, Germany, and Hong Kong (China).

This map shows how often 1,933 datasets were used (43,140 times) for performance benchmarking across 26,535 different research papers from 2015 to 2020.
Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research, Bernard Koch, Emily Denton, Alex Hanna, Jacob G. Foster, 2021.

This major imbalance in the discourse about how AI should be used and who should benefit from it reinforces existing power imbalances. A discussion piece from Data Pop Alliance called “The Return of East India Companies: AI, Africa and the New (Digital) Colonialism” explores various aspects of AI colonialism in Africa. For instance, there is under-development of natural language processing (NLP) technologies for non-Western languages. Computer vision of self-driving cars relies on low-paid human workers to label hundreds of hours of data. Lax ethical standards and “data dumping” in countries with less stringent data protection regulations effectively renders local people and society—AI guinea pigs. Despite the decreasing cost of training machine learning systems and greater availability of data, the power dynamics in AI research and development continue to reflect the dominance of a select few.

While machine learning models and datasets are being developed in other parts of the world, their use in research papers and performance benchmarking is still limited. We have the power to seek greater diversity and inclusivity in AI research, and to advocate for ethical standards that address data inequalities–as consumers and as researchers. For example, the UNDP and UNICEF regional Eurasia platform STEM4ALL to promote women and girls, share knowledge, raise awareness, and break gender stereotypes in STEM. Another way is by promoting collaboration across borders and develop own datasets to contribute to the global conversation.

The Hidden Benefits of Commuting: Finding Serenity in the Space Between

A stray dog named “Boji” has become a local celebrity after using buses, subways and ferries to travel across Turkey’s metropolitan city of Istanbul.

I used to hate mornings. Istanbul is a very anisotropic City. Commuting from one neighborhood to another, just two blocks away, could take everything from 5 to 55 minutes. However, I start valuing this commuting time as a “liminal space” between personal and business realms. It helps me to transition between home and work life, to kick start the day, and to finish it.

There are three elements of this transition ritual. First, is physical activity, which fuels my internal engine. I have to walk 10-15 minutes either to the bus or the subway stop. This walk makes me energetic and improves my mood–oxytocin proved to have beneficial cognitive and behavioural effects. Second, I use  walking and commuting time to learn something new, to push my creativity for the day. Podcasts are great, I have some business related–Anecdotally Speaking, Talking About Organizations, or Re:Thinking.  Some pure fun on the bun–Friday Night Comedy or Something Rhymes with Purple. Other good options are Coursera and LinkedIn learning apps, which allow you to save videos to watch off-line.  Third, I use this time to reflect and think things over. I always have a notebook and pen with me, and Google.Docs on my smartphone. (Full disclosure: I drafted and edited this post while enjoying views of Beşiktaş from DT2 bus). This helps me to kick start day or unload business thoughts at the end of the day.

This ritual works very well while working from home or while on a business trip. You could use  a treadmill for a 15 minutes walk, or simply walk around a block–I often enjoyed cities at 6am, empty and quiet. Podcasts are always with you, as well as a notebook and a pen.

Don’t hate mornings, instead embrace an opportunity. By incorporating a simple morning ritual–such as a short walk, listening to something new and interesting, and reflecting on your goals and intentions–you can prioritize your well-being and kick start the day; or close the day, avoiding business spillover to home life. 

Birthday Opportunity: Natural experiment in Cash Transfers Investing in Infants

Cash transfers following the birth of a first child can have large and long-lasting effects on that child’s outcomes. Andrew C. Barr and two co-authors made use of natural experiment—the January 1 birthdate cut-off for U.S. child-related tax benefits. Children born in December of previous year are eligible for tax deduction, while children born just a couple of weeks later, in January, are not eligible. As a result, families with otherwise similar children receiving substantially different refunds during the first year of life—roughly $1,300, or 10 percent of income for the average low-income single-child.

Using the careful data connection strategy, authors showed that this transfer in infancy increases young adult earnings by at least 1 to 2 percent, with larger effects for males. Baseline estimates indicate that eligibility for additional resources during the first year of life generates a $319 increase in average annual earnings between age 23 and 25 and a $456 increase between ages 26 and 28. These effects persist to older ages, with 2-3 percent increases at ages 29-31 and 32-34. These estimated effects are larger than those generated by the in-kind support programs. According to calculations, additional tax receipts associated with the increased earnings in adulthood, exceed the amount of the initial transfer, implying a negative net cost to the federal government.

The observed earnings effects appear to be explained by earlier human capital effects. the North Carolina education data showed substantial increases in test scores, reductions in behavioural problems, and a greater likelihood of high school graduation during childhood and adolescence. This chart shows effect of cash transfer eligibility on student outcome index, constructed as the mean of normalized test scores in grade 3-8, high school graduation, and any suspension in middle or high school. Birthdates to the left of the dotted line represent those where the child’s family could have received additional resources from child-related tax benefits in the following year (if eligible based on income).

Andrew C. Barr, Jonathan Eggleston, and Alexander A. Smith (2022) “Investing in Infants: The Lasting Effects of Cash Transfers to New Families”. NBER Working Paper No. 30373, August 2022. http://www.nber.org/papers/w30373