In January, several major tech companies — including industry giants Google, Amazon and Meta — announced a wave of layoffs in which over 20,000 workers lost their jobs, according to CNBC.
Several companies cited investments in artificial intelligence as a reason for the cuts. Meta’s Chief Executive Mark Zuckerberg said that the company had laid off employees in part to “invest in these long-term, ambitious visions around A.I.,” The New York Times reported. Amid company layoffs, German software giant SAP announced that they would be investing over $2 billion toward restructuring their company around AI.
But experts in computer science said that the factors surrounding layoffs extend well beyond AI.
While students have a reason to be concerned about “reduced workforces,” the job cuts can be attributed primarily to “high costs, rapid growth, lack of focus, investor pressure, interest rates” and other business considerations, Professor of Computer Science Shriram Krishnamurthi wrote in an email to The Herald.
“Tech firms did a lot of hiring during the pandemic,” David Autor, a professor of economics at the Massachusetts Institute of Technology, said in an interview with The Herald. “And they may have overshot the mark.” But Autor didn’t rule out AI completely. “I’m just saying that we don’t know.”
Tech industry giants like Meta and Stripe admitted to overhiring during the pandemic, The Herald previously reported.
Suresh Venkatasubramanian, a Brown professor of computer science and data science who served as a technology advisor for the White House, said that the tech industry is likely in “a hype wave,” with companies pivoting to AI in hopes of dominating the market. “I suspect that this is more of a repositioning of hiring more in areas that are focused on AI, and therefore laying off in areas that … seem less core to AI work,” he said.
Venkatasubramanian added that there will likely be a shift in the labor market in the next several decades where “the kinds of jobs that are considered important and the ones that are not” will inevitably change. “New fields are going to get created that we could not have imagined even five years ago.”
According to Krishnamurthi, entry-level jobs are likely the most vulnerable to AI developments. “If what you do best feels rote, then it could probably be automated using code synthesis systems like CoPilot,” he wrote. “This is what often happens with new technology and isn’t specific to AI: it takes things that were previously done by hand by many people and codifies their patterns.”
But new technology itself has a long way to go, Krishnamurthi noted. “A lot of the current AI tools, while very impressive for what they can do, are not really that sophisticated,” he wrote. “You can barely use them to do upper-level undergraduate courses, so how are they going to build serious, large-scale systems?”
Autor noted that AI’s potential to increase efficiency might impact the structure of white-collar work “in the way the computer revolution affected the structure of office work,” adding that AI has the potential to make “elite occupations” more accessible.
“Elite occupations are dominated by highly educated, highly paid people,” Autor said while pointing to the low percentage of people with college degrees in the current national workforce. “If we had more people able to enter the professions without quite as much (of an) expensive investment, that would create more good jobs and reduce costs of some of those activities.”
But despite the potential of AI, Venkatasubramanian acknowledged that students currently looking for a job “might take a while (before they) get a trajectory that feels stable.”
“If you’re going on the job market right now, you’re in the middle of a maelstrom of chaos,” he said. “I think that’s hard … but I think within a couple of years, this will start to settle.”
Venkatasubramanian noted that students should build transferable skills and that computer science students, in particular, should strengthen core tech skills and gain exposure to working with AI. Students from other disciplines who may have had expectations around their future careers may see “those jobs get reimagined and repositioned.”
“There have been periods of time when things were very stable and there was a well-known path or a well-defined path of getting a job,” Venkatasubramanian said. “That’s in flux.”
But education will retain its value, he added. “It just means you have to be a little bit more creative and keep your ear to the ground as to what jobs are coming up and how you can translate your skills into that job.”
Similarly, Autor noted that students should work to build analytical thinking skills while learning to utilize AI for increasing efficiency.
“If these tools are going to be so rampant, how can I use them well?” Students “should figure out how this tool can be useful to them because that will be a real, meaningful skill,” Autor said.
Krishnamurthi added that computer science students should cast a wide net in their job search.
Despite the reset in the tech industry impacting head counts, “I suspect there are still many jobs around the country (and world) that people from most disciplines would consider extremely comfortable,” he wrote. “They may just not be as glamorous.”
He also encouraged Brown students to utilize the support and resources available at the University.
“The more incurious you are, the more conservative with your course selection, etc., the more you’re wasting opportunities to get into the habit of expanding your universe,” Krishnamurthi wrote.
“At a practical level, it sends a poor signal to employers and makes you hard to tell apart from the crowd … if you don’t want to learn new things now with all this support, what habits are you setting in place and how will (the habits) hurt you when you run into innovations down the road?” he added.
Jennifer Shim is a University News editor overseeing the staff and student labor beat. She is a sophomore studying Applied Math-Economics. Outside of The Herald, you can find her playing NYT Connections.
