A group of soap bubbles sitting on top of a table. December 7th, 2016. Photo Credit: rihaij from Pixabay
Kennesaw State University faculty suggest that while some parts of the AI market could carry risk, the broader trajectory of the technology is more complex.
As artificial intelligence continues to expand across institutions and industries, many questions have emerged about whether its rapid growth is sustainable or driven by exaggerated expectations. Critics are warning that this rapid growth surrounding the technology could resemble a financial bubble.
While concerns about an AI bubble continue to increase, some researchers have argued that the technology’s current trajectory reflects deeper structural change. Dr. Nasrin Dehbozorgi, an assistant professor of software engineering and director of the AIET Lab in KSU’s College of computing and Software Engineering, stated that she does not view the current moment in AI as a full scale bubble.
“I don’t view the current moment in AI as a bubble,” Dehbozorgi said. “While some parts of the market show bubble-like dynamics, the underlying technological process and cross industry adoption point more toward transformation than short term hype.”
Dehbozorgi explained that researchers evaluate sustainability through long term impact analysis, rather than short term market enthusiasm and through data driven evidence.
While AI adoption is accelerating across multiple sectors, she noted that the technology continues to remain in a transition phase, causing it to be too early to draw definitive conclusions about its long term systemic effects.
On the other hand, Dr. Amir Karami, a KSU information systems researcher specializing in large scale data analysis and AI-generated content, offered a different assessment.
Karami described the current AI landscape as falling somewhere between transformation and hype, noting that productivity gains are unevenly distributed but real.
According to Karami, experts monitor indicators such as measurable business value, steady productivity growth, adoption beyond pilot projects and stable funding trends to determine whether innovation is sustainable or not.
Karami also added that today’s AI surge differs from past technology booms, like the Dot-com era because it is driven by established, profitable companies.
“The current AI market differs from the internet boom because it is largely driven by profitable, established companies making large, infrastructure-based investments with clever business models,” Karami explained. “Because of this, even if there is a bubble for some firms, it is unlikely that AI as a whole will burst.’
However, Karami also noted that a slowdown in AI investment could have ripple effects across education and industries. A decline in funding could reduce experimental projects and widen gaps between institutions with varying levels of preparedness.
While AI as a whole is pretty unlikely to collapse, Karami said smaller companies could possibly face greater vulnerability if expectations fail to align with outcomes.
Student perspectives reflect similar uncertainty.
Isaac Thoman, a Kennesaw State sophomore and student observer of AI trends, said predicting AI’s Economic future remains very difficult. “My guess is as good as anyone else’s,” Thoman said, noting his concerns about whether AI companies can meet large scale infrastructure costs and continue to convince investors of long term profitability.
Thoman added that while he follows developments closely, he does not consider himself an expert, reflecting broader public uncertainty surrounding AI.
While AI continues to grow across institutions and industries, faculty and students suggest that at this current moment, AI cannot easily be categorized.
Instead, it appears to involve a combination of financial risk, rapid innovation, and unanswered questions that will require evidence and time to fully assess.
