Written by: Nitya Wanchoo
I recently recruited for my junior summer internship, an internship that carries the weight of potentially being my first full-time job post-graduation. As most college students know, the job market is notoriously tough right now. According to a Cengage report, about 76% of employers reported hiring the same number or fewer entry-level employees in 2025 than in the year before (Cengage Group, n.d.). This is for two primary reasons: economic pressures, like inflation and tariff policies, and the rise of artificial intelligence (AI). AI has been a buzzword for the past couple of years now. I hear about it in class, read about it in the news, and see it being used all around me. I have had more conversations than I can count, all culminating in everyone’s differing answers to one larger question: What does a future with AI look like?
The Good
Last semester, I took Economics 402, “Intermediate Macroeconomics”. During the labor market unit, my professor taught us about the fixed pie fallacy, also known as the zero-sum fallacy. The fixed-pie fallacy, as defined by Psychology Today, is “the idea that there is a fixed pie and if one person gets more, that means the other person gets less” (Psychology Today, 2018).
Put simply, in the context of AI, many mistakenly believe that using AI to do work will reduce the work left for humans to do. History has proved contrary countless times. “New technologies often create new kinds of work even as they obsolete old tasks. The lamplighters lost their jobs when electric lights came, but we gained electricians, photographers, software engineers, and countless roles no one in the gaslight era could envision” (Chang, n.d.). The fundamental flaw in the fear that AI will steal our jobs is that we are incapable of imagining jobs that do not exist yet (RiskHedge, n.d.).
So why are we seeing megacorporations engage in mass layoffs? The headlines don’t lie: Klarna shrunk their headcount by 40%, Salesforce laid off 4,000 customer support roles in September, and countless other titles that have flooded LinkedIn “Home” pages over the past months. The answer lies in the bottom line – AI offers the perfect way for companies to cut costs. Unlike humans, AI agents won’t require five figure salaries or benefits like paid time off (Chang, n.d.).
On the contrary, in the long run, I imagine that AI will cause labor demand. After learning about the fixed pie fallacy, we were taught about production functions. Let us briefly examine a standard production function Y = z F(K, L) where Y denotes output, z denotes total factor of productivity, and F denotes a production function with K, capital, and L, labor, as inputs. As z increases, because the adoption of AI will increase our ability to be productive, then firms will become more efficient because they can produce more output with the same inputs. This means that the marginal product of labor will increase for any given level of labor. Since the marginal product of labor is the wage at which firms pay employees, a higher marginal product of labor will make it more profitable to hire more labor at the same wage. This causes the labor demand curve to shift right – companies want to hire more as employees get more productive because each worker becomes more useful.
This theory aligns well with the labor market in the real world. “Power tools didn’t kill carpentry, Microsoft Excel didn’t erase accountants” (RiskHedge, n.d.). They enhanced them. David Autor, an economist, even found that 85% of employment growth in the past 80 years has come from entirely new job categories created by innovation (RiskHedge, n.d.). Put shortly, AI is not a zero-sum race with only winners and losers.
The Bad
Many believe that AI will be the biggest game-changer since the creation of the internet. And, if you follow the money, it certainly seems that way. The biggest technology companies, including Amazon, Meta, Google, and Microsoft are racing, each spending billions on AI research and development. More than 1,300 AI startups now have valuations of over $100 million (CNBC, 2025). A report by UBS even shows that total AI spending is expected to reach $500 billion in 2026 (UBS, n.d.).
These numbers are undoubtedly high, perhaps even too high. People have begun to speculate that the money is creating an “economic bubble”. An economic bubble is defined as “a bubble marked by rapidly increasing asset prices that exceed intrinsic value, leading to a sudden market contraction known as a crash” (Investopedia, n.d.). The most notable economic bubbles in history are the dot-com bubble in the year 2000 and the housing bubble which contributed to the 2008 financial crisis. Now, many are drawing parallels between those and recent AI investments. Michael Burry, famous for predicting the 2008 housing bubble and financial crisis that followed, posted on X, “The government will pull out all the stops to save the AI bubble to save the market to save the economy. The problem is too big to save” (Burry, 2025).
Unfortunately, when so much time and money has been invested, it is hard to withdraw efforts. The Bank of America has allocated roughly a third of its total technology spend to AI tools and automation, even though a Bank of America survey shows broader investment sentiment to be cautious, with 60% of investors calling global AI equities overvalued (Bank of America, 2025) (MSN, 2025). It follows then that excessive investment in AI could be attributed to the sunk-cost fallacy. Cambridge Dictionary defines the sunk-cost fallacy as “the idea that a company or organization is more likely to continue with a project if they have already invested a lot of money, time, or effort in it, even when continuing is not the best thing to do” (Cambridge University Press, n.d.). It is possible society is going all in on AI, because past investments feel too large to walk away from, and we’re due for a bubble burst soon.
And The Ugly
Arguably, the ambiguity around a future with AI is the worst. Economists and politicians are at a crossroads regarding understanding, expectations, and opinions about AI. They are tasked with creating policy that will guide the economy through these complex times, without having a technical understanding of AI. Representative Jay Obernolte, a California Republican and the only member of Congress with a master’s degree in artificial intelligence, told The New York Times, “Before regulation, there needs to be agreement on what the dangers are, and that requires a deep understanding of what A.I. is” (New York Times, 2023). That’s tricky too, because, as The Stanford Report puts it “AI is advancing so rapidly that governments may not be able to keep up with its far-reaching effects, including on energy infrastructure, public trust, and national security” (Stanford University, 2025).
While most agree that there need to be guardrails, nobody seems to know what they should look like. Tech moguls, like the CEO of Alphabet Sundar Pichai, are relentlessly pushing for unrestrictive laws that will not hold back the technology (New York Times, 2023). The cost of strangling the technology could slow down innovation and advancements that would change lives for the better (Cato Institute, n.d.).
On the other hand, the less-than-enthusiastic public prefers more restrictive policy out of fear. As per Pew Research, more than half of U.S. adults worry that government regulation of AI will be too lax. Moreso, they have little or no confidence at all in the government’s ability to regulate AI effectively and in U.S. companies’ ability to develop and use AI responsibly (Pew Research Center, 2025).
The biases pushed in mainstream media both for and against AI, perpetuated by the fixed pie and sunk cost fallacies, showcase the vast range of opinions, and more importantly, the wealth of information available regarding this hot topic. Now that we have all this knowledge, the real danger lies in making the wrong decisions when managing the future of AI.
References
Bank of America. (2025). Bank of America joins the AI investment race. World Economic Magazine.
https://worldecomag.com/bank-of-america-ai-investment-race/
Burry, M. (2025). Big Short investor Michael Burry warns about markets. Yahoo Finance.
https://finance.yahoo.com/news/big-short-investor-michael-burry-130840772.html
Cambridge University Press. (n.d.). Sunk cost fallacy. Cambridge Dictionary.
https://dictionary.cambridge.org/dictionary/english/sunk-cost-fallacy
Cato Institute. (n.d.). Why AI overregulation could kill the world’s next tech revolution.
https://www.cato.org/commentary/why-ai-overregulation-could-kill-worlds-next-tech-revolution
Cengage Group. (n.d.). Employability report.
https://www.cengagegroup.com/edtech-research/employability-report/
Chang, S. (n.d.). AI and the zero-sum fallacy.
https://shanechang.com/p/ai-and-the-zero-sum-fallacy/
CNBC. (2025, August 10). AI and artificial intelligence billionaires’ wealth.
https://www.cnbc.com/2025/08/10/ai-artificial-intelligence-billionaires-wealth.html
Investopedia. (n.d.). Bubble.
https://www.investopedia.com/terms/b/bubble.asp
MSN. (2025). BofA survey: Over half of investors see AI as a bubble; 60% say equities are overvalued.
https://www.msn.com/en-us/money/top-stocks/bofa-survey-over-half-of-investors-see-ai-as-a-bubble-60-say-equities-overvalued/ar-AA1Or0pQ
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https://www.nytimes.com/2023/03/03/technology/artificial-intelligence-regulation-congress.html
Pew Research Center. (2025, April 3). How the U.S. public and AI experts view artificial intelligence.
https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/
Psychology Today. (2018, April). The zero-sum fallacy in negotiation and how to overcome it.
https://www.psychologytoday.com/us/blog/statistical-life/201804/the-zero-sum-fallacy-in-negotiation-and-how-to-overcome-it
RiskHedge. (n.d.). Why AI is not a job killer.
https://www.riskhedge.com/outplacement/why-ai-is-not-a-job-killer
Stanford University. (2025, December). AI facts: SIEPR policy forum with Fei-Fei Li and Mark Kelly.
https://news.stanford.edu/stories/2025/12/ai-facts-siepr-policy-forum-fei-fei-ling-mark-kelly
UBS. (n.d.). Market insights on artificial intelligence.
https://www.ubs.com/global/en/wealthmanagement/insights/marketnews/article.2515967.html