Few people have spent more time thinking about how AI will reshape work than Ruchir Puri. For years, Puri, Chief Scientist at IBM, has been building the systems that some workers now fear will replace them. He has watched the technology cross thresholds that once seemed decades away. And yet even he has been caught off guard by the speed of change in AI.

By Sascha Brodsky, Staff Writer, IBM

“I think it’s fair to say everybody who was working in AI has been surprised, and in some cases shocked, with how fast this technology has moved,” he said in an interview with IBM Think. “The speed of innovation and speed of progress has been nothing short of awe-inspiring.”

The numbers tell a story of transformation at scale. According to the World Economic Forum’s Future of Jobs Report 2025, which surveyed more than 1,000 employers representing 14 million workers across 55 economies, by 2030, AI will create 170 million new roles while displacing 92 million jobs—a net gain of 78 million positions. But that net positive obscures considerable churn: 22% of all jobs will be disrupted in the next five years. Employers expect 39% of skills required in the job market to change by 2030, and 41% of employers say they plan to reduce their workforce where AI can automate tasks. Meanwhile, Goldman Sachs estimates that if current AI applications were expanded across the economy, 2.5% of American employment would face displacement.

Every major technological shift has sparked predictions of mass unemployment. The car would eliminate the horse industry and everyone connected to it. The computer would render clerks obsolete. The internet would hollow out retail. And each time, the economy created more jobs than it destroyed. Approximately 60 percent of American workers today hold jobs that did not exist in 1940, which means more than 85 percent of employment growth over the past eight decades came from technology driven job creation. The question is whether this time is different.

The jobs that machines find easiest

The vast majority of job titles will persist. That is the view of Robert Seamans, a Professor of Management and Organizations at NYU Stern School of Business and Director of the Center for the Future of Management. “Only very few jobs will disappear,” he said in an interview. But that reassurance came with a warning: “Most jobs will change, however, and workers that don’t lean into those changes will find themselves with an antiquated set of skills that don’t have value on the job market.”

What makes a job vulnerable? According to Seamans, the positions most at risk are those with a limited number of discrete tasks and minimal interaction with other roles. Consider a telemarketer. The job involves a circumscribed set of activities: call, respond to queries and log results. There is no complex group coordination, no ambiguous human situations to navigate. “The telemarketer doesn’t interact with others in a complex group task setting,” he said. “It can be systematized and, therefore, replicated.”

A similar framework emerged from the conversation with Puri. The jobs at risk, he said, are those that involve “mechanical motion” in language and knowledge domains. “Somebody calls, you go look up a manual or a record book, get the answer, say the answer,” he explained. “That kind of job can be eliminated.” He was careful about his word choice. “I’m deliberately not using the word ‘menial,’ actually. Mechanical in nature.”

Software development offers a preview of what is coming elsewhere. In 2011, Marc Andreessen declared that software was eating the world. A decade later, Puri said, the meal was finished. “Software has eaten the world. And it was clear in 2020 that AI is eating software.”

The job of a software developer, Puri said, is evolving at an unprecedented pace. “I’m not in the camp that those jobs will be eliminated. I’m more in the camp of so much more is to be done, so their jobs will evolve.”

Not every job that looks automatable actually is, Puri said. AI now reads millions of medical scans, but a radiologist does far more than interpret images. The work involves connecting patient history, conducting examinations and synthesizing complex information. “It’s just much more complicated than just reading an imaging report,” he said. The same principle applies across professions: the more a job involves weaving together disparate threads of judgment and context, the harder it is to automate.

For workers in vulnerable roles, the takeaway is clear: “If you are in that job, you’d better start figuring out what value add you are bringing, because that kind of job can be automated,” Puri said. “But these tools give you the means to reimagine your job, too, rather than making you so nervous,” he added, noting that workers can experiment with the technology on their phones and propose new roles for themselves.

“Go to your manager and say, ‘This is possible,’” he said. “That gives you a say in your job and what you do in the future, rather than having that future be controlled by someone else.”

The question of which jobs survive may have less to do with the work itself than where it sits in the hierarchy. Gillian Lester, former dean of Columbia Law School, sees a potential “hollowing out of the middle” in the workforce. In her telling, the jobs most likely to persist are at the extremes: highly specialized professionals whose judgment cannot be replicated, and low-wage service workers performing physical tasks that can’t be easily automated. “The professionals, the surgeon and the custodial worker are going to be okay,” she told IBM Think in an interview. But in between lies a vast white-collar middle that may prove vulnerable. “People working in the insurance industry, where they’re sort of checking data sets to figure out whether somebody is eligible for recovery,” Lester said, “that’s just not going to require humans anymore going forward.”

What about middle management and executive functions? Information technology has already reshaped these roles, Puri said, putting “more powerful tools at their disposal” and enabling them to manage larger teams with “better intelligence, better decision-making capabilities at your fingertips.”

But he pushed back against the notion that management itself will be automated away. “I don’t think we are going to get into a scenario where we, en masse, are like, ‘We don’t need middle management,’” he said. “That would be almost equal to saying we don’t need decision making—we’re just going to offload everything to these machines that can make decisions for us as well.”

That scenario is either “nirvana or a dystopian view, depending on whose view you take,” he said. But in either case, it is distant. “Until we get to trust these machines with literally our lives and everything else, there is a long transition period between when the jobs are being reshaped rather than being eliminated.”

The bifurcation of every profession

What happens next may be less like a wave of layoffs than a sorting. Nearly every knowledge-based job will soon split in two, according to Gabe Goodhart, Chief Architect of AI Open Innovation at IBM. “The AI-enabled version of that job will be in extremely high demand,” he said in an interview with IBM Think. Those who master the tools will command a premium; those who do not will find themselves competing in a shrinking market for traditional work, he noted.

The gap between these two camps, Goodhart warned, will be filled by workers who “do not have the skillset to use AI effectively, but try to use it anyway.” These are workers who may produce more but understand less, who trust the output without grasping the process, who cannot tell when the machine has erred.

But the same tools that threaten some workers are empowering others. Consider the emergence of “vibe coding,” in which AI agents translate design concepts directly into working software. Traditionally, designers lacked the programming skills to build their own prototypes. Now they can. “This is a cross-over skill where the key inputs—design—can now translate directly to the target output of working software,” Goodhart explained. The barriers between disciplines are dissolving.

This democratization may be one of the most profound changes underway. “The current generative AI technology has made coding skills and data science skills accessible to non-coders in an almost English language way,” Puri said. He pointed to predictions of single-person billion-dollar valuation companies. “What used to take probably 20 people before, that can be done by one or two people now. And by the way, those people do not have, quote unquote, ‘software skills.’”

The result is counterintuitive: non-technical workers may actually be facing expanded opportunities. “They are the ones who are brought in now,” Puri said. “They were kind of left out before, actually. Now they are brought in.”