As big tech companies increasingly spend billions on AI initiatives, speculation about an \u201cAI bubble\u201d has dominated the country\u2019s economic discourse.<\/p>\n
The phrase \u201cAI bubble\u201d denotes recent concerns<\/a> about tech companies investing<\/a> hundreds of billions of dollars in AI<\/a>, despite consumers only spending $12 billion on AI technology yearly<\/a>. Pitt professors shared their views on whether massive investments in AI will bring returns for the country and for Pittsburgh.<\/p>\n An economic bubble<\/a> occurs when there is a rise in the market value of a certain product due to hopeful speculation about its future, which results in a crash after investors withdraw their money. The current massive AI investment also draws concern due to what some see as \u201ccircular investment\u201d<\/a> among big tech companies, which occurs when companies in the same field heavily invest in each other.<\/p>\n Morgan Frank, an assistant professor in the School of Computing and Information<\/a>, said he thinks the AI bubble could burst, considering OpenAI<\/a> has invested over a trillion dollars<\/a> in infrastructure to build data centers. Frank noted that the company\u2019s investment is twice as big as the federal government\u2019s Bipartisan Infrastructure Law<\/a>, a $1.2 trillion investment in repairing and modernizing the country\u2019s infrastructure.<\/p>\n \u201cOpenAI is spending more than the federal government,\u201d Frank said. \u201cIt\u2019s an insane amount of money, and I don\u2019t know how they\u2019re doing that, but they see payoffs.\u201d\u00a0<\/p>\n Brad Messner, an assistant professor in the School of Business, said investors are betting on AI because there\u2019s currently a competitive race<\/a> for companies to develop the best and most lucrative AI technology.<\/p>\n \u201cPeople are overinvesting at what is technically an inflated valuation because right now we\u2019re in a race,\u201d Messner said. \u201cWe\u2019re in that bucket right now of asking, \u2018Who are going to be the winners?’\u201d<\/p>\n As for AI\u2019s prevalence in Pittsburgh, Eric Donald, an assistant professor of economics, said he believes the city is a \u201crelatively small player\u201d in the field of AI and will most likely not overinvest heavily like other regions such as Silicon Valley<\/a>.<\/p>\n \u201cPittsburgh strikes me as [being] in a pretty good spot of not being left behind, but not being so far in the lead that you get vast overinvestment either,\u201d Donald said. \u201cWe\u2019re not a top AI hub \u2014 not like Silicon Valley. It just seems like we\u2019re attracting more investment relative to our size than other old Rust Belt cities.\u201d<\/p>\n Frank said Pittsburgh is not a huge AI hub because startups in the city often relocate elsewhere. For example, Zach Lipton<\/a>, a professor in the School of Computing at Carnegie Mellon University, cofounded an AI-health startup<\/a> in Pittsburgh which raised hundreds of millions of dollars in venture capital and shifted much of its corporate functions to California<\/a>.\u00a0<\/p>\n \u201cPittsburgh\u2019s not very good at hanging on to tech startups,\u201d Frank said. \u201cWe have great universities \u2014 CMU and Pitt \u2014 and there are a lot of startups that spin out of that ecosystem, spend a couple of years in Pittsburgh and then as soon as they have some initial success, they get sent somewhere else.\u201d<\/p>\n Messner said he believes Pittsburgh has become a significant market for AI, noting that many large tech firms<\/a> are planning data centers<\/a> around the region. However, he said, data centers using an abundance of natural resources<\/a>, such as water, could add to the concerns about a potential AI bubble.<\/p>\n \u201cOne thing causing some people to pull back is the fear that we\u2019re moving so fast and haven\u2019t stabilized how we\u2019re handling a lot of these resources,\u201d Messner said. \u201cWe were gung-ho for years now with trying to push these systems, and now, we\u2019re realizing this is actually more of an infrastructure that we need to look at.\u201d<\/p>\n Donald said it\u2019s common for a new technology \u2014 like AI \u2014 to emerge without having the full infrastructure for it built yet. He referenced how factories in the 1900s took a long time<\/a> to incorporate electric engines, though the technology was introduced decades before. Donald said he believes society\u2019s adaptation to AI will somewhat reflect this process.\u00a0<\/p>\n \u201cWe have this very large technological innovation that we don\u2019t really know how to use properly,\u201d Donald said. \u201cTo use [AI] most effectively, I think we would have to substantially reorient a lot of our production processes and work relationships, which is going to take a lot of time.\u201d<\/p>\n After this happens, Donald said the \u201cproductivity gains from that technology take time to realize,\u201d and will then match the current massive investments.<\/p>\n \u201cWe\u2019ll then use the infrastructure \u2014 the data centers that we\u2019re building now \u2014 and the faster you expect this diffusion, the more justified would be the financial investment now,\u201d Donald said.<\/p>\n However, Messner said he believes he has not seen substantial returns for companies who invest in AI technology. Companies who have replaced human staff with AI technology have slowly rehired their workers<\/a> because they likely \u201cdid not quite get what they were hoping for,\u201d according to Messner.<\/p>\n \u201cPeople are using these tools, but are their tools really showing improvement in valuation, operations and efficiency?\u201d Messner said. \u201cThere\u2019s promises of it, but we\u2019re not seeing it just yet.\u201d<\/p>\n Messner said AI companies are posing a \u201cbig ask and promise\u201d by claiming<\/a> the infrastructure will stabilize the AI platforms and drastically increase its quality and value. He said investors may soon realize there\u2019s \u201cno plan on how they\u2019re getting [their money] back.\u201d<\/p>\n