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AI revolution: How artificial intelligence is reshaping education and jobs in America

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Artificial intelligence has rapidly become a part of American’s lives. What once was a fringe concept a few years ago is now an everyday tool.

Its expansive reach affects what and how students study, as well as the job sector, prompting some to question how students and higher education at large should respond.

The best way an undergrad can prepare for an AI-altered workforce is to develop human qualities that machines cannot replicate, such as critical thinking, creativity, and social intelligence, some experts told The College Fix.

While the value of specific majors may diminish, careers in mental health, healthcare, and fields requiring high-level decision-making and management will remain viable, they said.

But make no mistake, the role of humans will increasingly center on collaboration with AI.

AI will be a job killer. It will also be a job creator.

While some jobs will be eliminated, others will be created.

“The amount of work that’s being created and the opportunities to both create and contribute are going to be expanded exponentially,” said corporate advisor Jack Myers, a University of Arizona lecturer in its School of Information Science.

Forecasts predicting the coming obsolescence of countless careers should be viewed “through the prism of not only what’s going to be eliminated, but what’s going to be created,” he told The College Fix in a telephone interview.

Jobs in coding, basic processing, routine bookkeeping, low-complexity customer service and translation will all soon be eliminated, Myers said.

But the opportunities ushered in by AI are going to be exceptional, said Myers, author of the book “The Tao of Leadership: Harmonizing Technological Innovation and Human Creativity in the AI Era.”

“If you look at almost any area of human creation,” Myers said, “it will be enhanced through the same type of collaborative partnership as if the creator was hiring an expert to assist and support in the process.”

Joey Kim, chair of the Department of Engineering and Computer Science at Master’s University, described AI as “simply a tool.”

“With the advent of new tools, careers do disappear,” Kim said in a telephone interview with The Fix. “There’s also careers that get modified…. It’s not simply binary and careers [either] remain unaffected or [become] obsolete. There is a spectrum.”

But like it or not, AI will be part of many jobs, said Michael Pavlin, an associate professor in the School of Business and Economics at Wilfrid Laurier University, who has been involved in AI research since the early 2000s and serves as the chair of his school’s management analytics program.

“It’s hard to imagine a white collar job where you’re not going to be interacting with AI at some level,” he said in a telephone interview.

However, despite recent AI advances, he said he remains “more on the skeptical side,” later adding he believes “we’re being a little bit oversold.”

Reva Freedman, an associate professor of computer science at Northern Illinois University with expertise in computational linguistics, said AI “is going to have a huge impact on the job market, but not different in kind to the effect that computerization had with the invention of the PC in 1983.”

“In offices, [b]efore the invention of the PC, lots of people had jobs as secretaries and clerks. Secretaries typed memos that other people wrote. Those jobs have been largely replaced by people using word processors themselves,” she said via email. “Clerks did a variety of jobs that have been automated by use of Excel and other software.”

The jobs that will survive require high-level thinking, management skills, or require hands-on work, such as medicine, Freedman said.

Gary Clemenceau, a “deep geek” turned chaplain and author, who claims 30 years of experience in tech, agrees. He told The Fix that “mental health and healthcare jobs, and anything that requires dealing with humans and higher-order thinking, will still be viable.”

AI and the dumbing-down of higher education

But will there be any higher-order thinking left?

“For teachers, it’s absolutely impossible to give a writing [assignment] today that students can’t cheat on,” Freedman said. “Even for an in-class assignment, you can now get glasses that allow you to look up stuff on the web during an exam.”

Kim said the misuse of AI in the classroom devalues a degree’s representation of how well one has been trained in a program and successfully met its requirements.

Freedman also expressed concerns over the misuse of AI in other segments of society, citing allegations it was used to write a recent MAHA report said to contain made-up citations.

Pavlin told The Fix he is more concerned about less obvious errors that require a greater level of expertise to detect. For example, when querying AI about esoteric subjects related to his research, he tends to find deeper ways in which AI makes mistakes than he would if he similarly asked AI a question about general relativity.

In that sense, AI is not bulletproof. Kim echoed similar sentiments: “When big important decisions must be made where it’s either life-or-death or costing millions and millions of dollars, you’re going to need something more than ChatGPT.”

Yet, as some of the scholars interviewed by The Fix noted, the increasing overuse of AI by students may lead to the attrition of capacities beyond their proficiency at using ChatGPT.

“I think it’s impacting their learning,” Pavlin said. “Not all students, but [there is] definitely a subset of students where I’m concerned about their critical thinking skills.”

AI and the college student

When asked how students could best prepare for the careers that await them in an AI-altered job market, most of the scholars interviewed recommended they develop their more uniquely human attributes.

“The machines are already smarter than the human brain in many instances,” Myers told The Fix. “[They have] been for a while and that’s just going to continue to become increasingly the norm.”

“So where does the human come in?” Myers asked rhetorically, answering that humans enter through the “collaborative process” and “the unique human qualities of the human brain.” These he said are developed in the social sciences and humanistic majors.

Clemenceau said students must develop their human qualities.

“Students need to put down their phones and THINK,” Clemenceau wrote in an email to The College Fix. “AI is not very good at being creative.”

Whether majoring in computer science and learning to code is still a wise choice was a point of some disagreement.

“Coding will be irrelevant as a tool or resource to bring to the table,” Myers said. “The AI is doing its own coding going forward. It doesn’t need the human coders anymore.”

In contrast, Freedman noted that people “have been saying ever since I was a beginning programmer (in the 70’s) that programs that can write programs were coming.”

“Is it more true now? Probably. Does that mean the [number] of programmers needed will go down? T[h]at’s a much harder question to answer.”

“I think there will always be room for people who care about the quality of their work, understand the business needs, and can communicate with non-programmers,” she said.

As for choosing a major, though, she added: “I don’t think students’ majors have a lot to do with their success in the work world; their personal qualities are a lot more important. So I don’t think we can tell students what majors will be more useful.”

Kim expressed similar sentiments, saying “I personally believe that with any major, if you’re going to be using your tools to your advantage, and if you’re really going to be motivated enough to not just follow the crowd, you will have a job.”

Clemenceau said the future may be bleaker than his optimistic peers.

“I see two roads,” he said via email. “A small percentage of people will reject AI as inhuman and soulless and empty, and take the ‘human road’ as much as possible, living more spiritual lives.”

However, he added, a “larger percentage of people will fully embrace AI and (sadly) sacrifice part of their humanity, becoming less creative, less able to think critically – and more easily manipulated.”

MORE: Using AI to write essays can impair brain function: MIT study

IMAGE CAPTION AND CREDIT: A graphic showing a laptop user employing AI / Supatman, CanvaPro

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AI Insights

Do AI systems socially interact the same way as living beings?

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Key takeaways

  • A new study that compares biological brains with artificial intelligence systems analyzed the neural network patterns that emerged during social and non-social tasks in mice and programmed artificial intelligence agents.
  • UCLA researchers identified high-dimensional “shared” and “unique” neural subspaces when mice interact socially, as well as when AI agents engaged in social behaviors.
  • Findings could help advance understanding of human social disorders and develop AI that can understand and engage in social interactions.

As AI systems are increasingly integrated into from virtual assistants and customer service agents to counseling and AI companions, an understanding of social neural dynamics is essential for both scientific and technological progress. A new study from UCLA researchers shows biological brains and AI systems develop remarkably similar neural patterns during social interaction.

The study, recently published in the journal Nature, reveals that when mice interact socially, specific brain cell types create synchronize in “shared neural spaces,” and artificial intelligence agents develop analogous patterns when engaging in social behaviors.     

The new research represents a striking convergence of neuroscience and artificial intelligence, two of today’s most rapidly advancing fields. By directly comparing how biological brains and AI systems process social information, scientists can now better understand fundamental principles that govern social cognition across different types of intelligent systems. The findings could advance understanding of social disorders like autism while simultaneously informing the development of more sophisticated, socially  aware AI systems.  

This work was supported in part by , the National Science Foundation, the Packard Foundation, Vallee Foundation, Mallinckrodt Foundation and the Brain and Behavior Research Foundation.

Examining AI agents’ social behavior

A multidisciplinary team from UCLA’s departments of neurobiology, biological chemistry, bioengineering, electrical and computer engineering, and computer science across the David Geffen School of Medicine and UCLA Samueli School of Engineering used advanced brain imaging techniques to record activity from molecularly defined neurons in the dorsomedial prefrontal cortex of mice during social interactions. The researchers developed a novel computational framework to identify high-dimensional “shared” and “unique” neural subspaces across interacting individuals. The team then trained artificial intelligence agents to interact socially and applied the same analytical framework to examine neural network patterns in AI systems that emerged during social versus non-social tasks.

The research revealed striking parallels between biological and artificial systems during social interaction. In both mice and AI systems, neural activity could be partitioned into two distinct components: a “shared neural subspace” containing synchronized patterns between interacting entities, and a “unique neural subspace” containing activity specific to each individual.

Remarkably, GABAergic neurons — inhibitory brain cells that regulate neural activity —showed significantly larger shared neural spaces compared with glutamatergic neurons, which are the brain’s primary excitatory cells. This represents the first investigation of inter-brain neural dynamics in molecularly defined cell types, revealing previously unknown differences in how specific neuron types contribute to social synchronization.

When the same analytical framework was applied to AI agents, shared neural dynamics emerged as the artificial systems developed social interaction capabilities. Most importantly, when researchers selectively disrupted these shared neural components in artificial systems, social behaviors were substantially reduced, providing the direct evidence that synchronized neural patterns causally drive social interactions.

The study also revealed that shared neural dynamics don’t simply reflect coordinated behaviors between individuals, but emerge from representations of each other’s unique behavioral actions during social interaction.

“This discovery fundamentally changes how we think about social behavior across all intelligent systems,” said Weizhe Hong, professor of neurobiology, biological chemistry and bioengineering at UCLA and lead author of the new work. “We’ve shown for the first time that the neural mechanisms driving social interaction are remarkably similar between biological brains and artificial intelligence systems. This suggests we’ve identified a fundamental principle of how any intelligent system — whether biological or artificial — processes social information. The implications are significant for both understanding human social disorders and developing AI that can truly understand and engage in social interactions.”

Continuing research for treating social disorders and training AI

The research team plans to further investigate shared neural dynamics in different and potentially more complex social interactions. They also aim to explore how disruptions in shared neural space might contribute to social disorders and whether therapeutic interventions could restore healthy patterns of inter-brain synchronization. The artificial intelligence framework may serve as a platform for testing hypotheses about social neural mechanisms that are difficult to examine directly in biological systems. They also aim to develop methods to train socially intelligent AI.

The study was led by UCLA’s Hong and Jonathan Kao, associate professor of electrical and computer engineering. Co-first authors Xingjian Zhang and Nguyen Phi, along with collaborators Qin Li, Ryan Gorzek, Niklas Zwingenberger, Shan Huang, John Zhou, Lyle Kingsbury, Tara Raam, Ye Emily Wu and Don Wei contributed to the research.



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I tried recreating memories with Veo 3 and it went better than I thought, with one big exception

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If someone offers to make an AI video recreation of your wedding, just say no. This is the tough lesson I learned when I started trying to recreate memories with Google’s Gemini Veo model. What started off as a fun exercise ended in disgust.

I grew up in the era before digital capture. We took photos and videos, but most were squirreled away in boxes that we only dragged out for special occasions. Things like the birth of my children and their earliest years were caught on film and 8mm videotape.



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That’s Our Show

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July 07, 2025

This is the last episode of the most meaningful project we’ve ever been part of.

The Amys couldn’t imagine signing off without telling you why the podcast is ending, reminiscing with founding producer Amanda Kersey, and fitting in two final Ask the Amys questions. HBR’s Maureen Hoch is here too, to tell the origin story of the show—because it was her idea, and a good one, right?

Saying goodbye to all the women who’ve listened since 2018 is gut-wrenching. If the podcast made a difference in your life, please bring us to tears/make us smile with an email: womenatwork@hbr.org.

If and when you do that, you’ll receive an auto reply that includes a list of episodes organized by topic. Hopefully that will direct you to perspectives and advice that’ll help you make sense of your experiences, aim high, go after what you need, get through tough times, and take care of yourself. That’s the sort of insight and support we’ve spent the past eight years aiming to give this audience, and you all have in turn given so much back—to the Women at Work team and to one another.



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