AI is everywhere we look, from the classroom to headlines to our devices. AI has swiftly become one of the most widely adopted technologies of this era. We often hear discussions about what AI can do and what it’s worth, but rarely do we discuss these aspects together.  

As a finance student, the more I immerse myself in these topics, the more I realize that the finances of GenAI and its higher education implications intersect in many ways. One way to see this connection is through investments. The amount of capital flowing into AI, along with investors’ confidence, can influence how fast the technology makes its way into the classroom. By examining these financials, we can begin to understand how the development of GenAI may look in the future.  

The Finance of Gen AI  

Many investors have described AI as a bubble. In finance, a bubble occurs when the price or value of an asset rises far above what its underlying fundamentals justify (Petersen 2014). The current AI market shares several characteristics with past financial bubbles, leading some individuals to think this may be another bubble. However, financial bubbles are difficult to determine in real time and are often only confirmed after the “bubble has popped” (Petersen 2014).  

During the dot-com era, simply adding “.com” to a company’s name could increase market interest and its stock price. Similarly, companies slap “AI” on a product, and its valuation increases regardless of whether AI is an impactful aspect of the business. Some investors are drawn to these companies because of the potential of GenAI technology, despite limited revenue or unproven business models. Sequoia Capital’s David Cahn estimates that $600 billion in annual revenue is needed to justify the current AI infrastructure being built (Cahn 2024). However, OpenAI and Anthropic generated a combined $29 billion in annualized revenue by the end of last year, which is a fraction of the estimate (Emberson and Edelman 2026).  

At the same time, the current AI boom is not simply speculation. Unlike the dot-com era, when most internet startups were still unprofitable and failed within a few years of going public (Laseter, Kirsch, and Goldfarb 2007), a large share of today’s AI buildout is backed by companies that are already generating real profits. For example, as a leading supplier of the specialized chips that power AI systems, Nvidia has benefited directly from surging demand for AI computing infrastructure. The company posted a 75.2 percent adjusted gross margin on $62.3 billion in data center revenue in early 2026 (Nguyen 2026). Even so, AI can be overvalued and still be transformative.  

Why This Matters for Higher Education  

Financial markets influence which technologies receive funding, which companies survive, and how quickly new products are developed. Too little investment can prevent impactful technologies from reaching their potential. Too much investment can build around trends that die rather than sustainable value. The amount of capital flowing into GenAI will determine what the next five years of GenAI looks like, and higher education is one of the industries that will be impacted most directly.  

Right now, students are already ahead of their institutions. Recent survey data puts undergraduate students’ use of Gen AI somewhere around 90 percent or more (Stephenson and Armstrong 2026). However, only a third of students say their institution supports the use of GenAI or provides the necessary AI tools (Stephenson and Armstrong 2026).  

If AI investment continues to grow, I expect AI to become increasingly more important in different disciplines. AI literacy could become as important as media and digital literacy and a necessary skill for the workforce. This would in turn push higher ed to fully adopt the use and teaching of AI to provide their students with the best post-grad opportunities.  

On the other hand, if the market cools off, instead, some AI startups will disappear. Universities may become more selective about which tools they purchase. Institutions may slow down their spending and wait for clearer evidence that GenAI technology improves learning. That would not necessarily mean AI will not be important, but it will certainly take a different trajectory.  

Regardless of where the market goes, institutions and students should teach and learn the underlying skills of how Gen AI functions and how to critically analyze its outputs, rather than following trends and using whichever tools are popular. Ultimately, the market may move in any direction, but skills will outlast product cycles.  

Looking Beyond the Cycle  

Technology tends to move in recognizable cycles: hype, speculation, correction, and then genuine long-term growth (Ahamed 2026). Revolutionary technologies such as railroads, electricity, and the internet all went through a similar trajectory of being overvalued and overbought before a period of cooling, widespread adoption, and continued development (Ahamed 2026). Gen AI could take a similar path.  

What interests me most is the connection between these cycles and education. Markets shape innovation. Innovation reshapes education. Education prepares the workforce. The workforce drives the economy, and the economy influences the markets. Finance and higher ed aren’t two stories about GenAI, but two points in the same loop.  

The markets will fluctuate, but the learning doesn’t have to stop. The skills worth building now are the ones that hold their value on the other side of whatever correction comes next.  

References 

Ahamed, Liaquat. 2026. “I Won a Pulitzer for Explaining the Great Depression. The AI Spending Boom Terrifies Me.” Fortune, republished in Yahoo Finance, June 2. https://finance.yahoo.com/economy/policy/articles/won-pulitzer-explaining-great-depression-090000909.html. 

Cahn, David. 2024. “AI’s $600B Question.” Sequoia Capital, June 20. https://www.sequoiacap.com/article/ais-600b-question. 

Emberson, Luke, and Yafah Edelman. 2026. “Anthropic Could Surpass OpenAI in Annualized Revenue by Mid-2026.” Epoch AI, February 19. https://epoch.ai/data-insights/anthropic-openai-revenue. 

Laseter, Timothy, David Kirsch, and Brent Goldfarb. 2007. “Lessons of the Last Bubble.” strategy+business, February. https://www.strategy-business.com/article/07102. 

Nguyen, Khac Phu. 2026. “Nvidia Posts 75.2% Margin as $650 Billion AI Capex Looms.” GuruFocus, republished in Yahoo Finance, February 26. https://finance.yahoo.com/news/nvidia-posts-75-2-margin-183202786.html. 

Petersen, Deborah. 2014. “A Brief History of Financial Bubbles.” Stanford Graduate School of Business, November 24. https://www.gsb.stanford.edu/insights/brief-history-financial-bubbles. 

Stephenson, Rose, and Charlotte Armstrong. 2026. Student Generative AI Survey 2026. Higher Education Policy Institute (HEPI), March 12. https://www.hepi.ac.uk/reports/student-generative-ai-survey-2026. 

About the Author 

Chiebuka Tor, 2026–2028 CEL Student Scholar, is a finance major with a minor in communications. He is an Odyssey Scholar, S.M.A.R.T. program mentor, Vice President of the African Diaspora of Elon, and SGA senator for the Love School of Business. 

How to Cite This Post 

Tor, Chiebuka. 2026. “How Finance Informs the Future of Gen AI in Higher Education.” Center for Engaged Learning (blog), Elon University. October 27, 2026.  https://www.centerforengagedlearning.org/how-finance-informs-the-future-of-genai-in-higher-education.