Young Workers Land High-Paying AI Jobs as Workplace Debate Intensifies Over Junior Employee Usage

Workers can't agree if junior employees should use AI at work: CNBC survey

Should junior-level workers use artificial intelligence on the job? Their coworkers are split on the matter, according to the CNBC and SurveyMonkey Quarterly AI and Jobs Survey published Thursday. Roughly 42% of all workers said junior-level staff should be allowed to use AI at work with clear guidelines and limitations, while 35% said the technology should be prohibited for junior-level workers, according to the survey, which polled 1,686 students and workers across the U.S. from July 22 to 27. Another 14% said junior-level workers should be able to use AI only with supervision and approval, and 9% said they should be able to use it freely without any restrictions. The issue is thorny: Many employers are pushing their workers to build AI skills in the hopes they'll be more productive, but there are unique considerations for those on the lower rungs of the career ladder. Entry-level jobs are typically considered more likely to be disrupted or replaced by AI. Nearly one-third of employers say AI has raised the experience requirements for their entry-level roles, according to a July report from ZipRecruiter. Entry-level job seekers also face steeper competition for fewer roles. Many members of Gen Z, who are between 14 and 29 years old, "are lukewarm on what [AI] means for the workplace," says Shawn VanDerziel, president and CEO of the National Association of Colleges and Employers. In CNBC's survey, 42% of Gen Z respondents said entry-level workers should be prohibited from using AI at work, a higher proportion than any other generation polled. Early career workers may also feel whiplash from being prohibited from using AI in college to suddenly being strongly encouraged to use it at work, VanDerziel says: Some of them "are afraid to get caught 'cheating' because of what they've been told in school." AI can be useful for some entry-level work, he says, including delegating routine tasks, proofreading, analyzing large data sets, and answering common workplace questions to fill skill or resource gaps as young workers start out. But using AI on some other types of tasks could set younger workers back, VanDerziel cautions, like automating tasks without learning how to do them yourself first or outsourcing decision-making to AI. If early career staff aren't doing the work themselves, he says, "they're not honing those workplace skills that can be beneficial, and that serve as building blocks, for other work." Though AI may offer "a shortcut" that's convenient now, repeatedly relying on it could keep these workers from developing expertise on a subject, he adds. This concern that AI could stunt young workers' professional development may be one reason they and their colleagues support guardrails or restrictions around who can use the technology at work. Employees might also worry about younger workers submitting "AI workslop" or outputs containing false or misleading information, resulting in more work for colleagues left to fix these mistakes. Another potential fear: With 37% of workers citing privacy as a reason they've avoided using AI, according to a CNBC survey published in May, they might be concerned their most inexperienced coworkers could accidentally expose proprietary or sensitive information. AI literacy may appeal to many employers, but it's not everything. Critical thinking, communication, and collaboration still regularly top NACE's lists of skills employers look for in entry-level workers, VanDerziel says, so young employees should focus on developing those skills as well. "Companies are looking at these new grads to be their future leaders, and they want a pipeline for the future," he says. "Those skills still endure even in the age of AI," he says, and they "are even more important today." Want to get ahead at work? Then you need to learn how to make effective small talk. In CNBC's new online course, How To Talk To People At Work, expert instructors share practical strategies to help you use everyday conversations to gain visibility, build meaningful relationships and accelerate your career growth. Sign up today!

Millennials and Gen Z are landing fast-growing, high-paying AI jobs, according to LinkedIn study

While young workers are struggling in the broader job market, they're thriving in a subset of AI jobs. In a study published Tuesday, LinkedIn identified the top 12 fast-growing AI jobs, based on an analysis of the platform's data, including the gender, education and age of those who were hired. The study defines AI jobs as "AI engineering and technology-building roles." "There is a big narrative in the labor market right now that it's very difficult to get a job if you're Gen Z," says Kory Kantenga, Head of Economics, Americas, at LinkedIn. That narrative isn't wrong: The unemployment rate was 7.2% for young workers ages 22 to 27 and 5.7% for recent college grads in the same age group in June, compared to 4.1% for all workers, according to data from the U.S. Census Bureau and U.S. Bureau of Labor Statistics. One major finding of the LinkedIn report, however, is that young workers are especially benefiting from the expanding AI jobs market. "To see them taking advantage of the AI labor market in such a robust way was surprising," Kantenga says. Gen Z workers make up more than two thirds of hires for two in-demand individual contributor roles, forward deployed engineer and AI engineer, which have median annual salaries of $199,000 and $166,000, respectively, according to the study. Millennials make up 60% of new hires for head of AI roles, which offer $236,000 in median pay. AI jobs are on the rise, according to LinkedIn: Though the company declined to share exact numbers, the number of AI job postings on the site increased 14% between 2023 and 2024, and then by 156% between 2024 and 2025, according to a LinkedIn representative. Some young workers have been able to "take advantage of this moment" to secure high salaries and to "move into leadership positions," says Kantenga, who encourages recent grads to "take advantage of those opportunities" while "buffering yourself against any changes down the road." The median annual salary for workers aged 25 to 34 was $60,320 in the second quarter of 2026, and the median salary for workers aged 35 to 44 was $74,672, according to data from the Federal Reserve Bank of St. Louis. Pay varies significantly among AI jobs, but the median salary for a typical AI role is $177,000, more than double the $80,000 median salary for non-AI roles, according to LinkedIn's analysis of job posting pay data. "There's a lot of money going into AI today, and in order to attract talent, companies have had to significantly increase the amount of pay that they're offering," Kantenga says. Another reason these jobs pay so well is that they tend to require more education, according to Kantenga: 91% of workers in AI roles have bachelor's degrees or higher, and the average level of educational attainment increases in more senior roles. Almost half of workers hired for head of AI roles have a graduate degree, and 20% have a doctoral degree. Kantenga predicts that the AI job market is "going to drive up wages" for some highly educated professionals whose degrees are relevant to AI roles. But it could also increase pay inequality. If workers who don't have relevant degrees or any higher education are shut out of high-paying AI occupations, Kantenga says, it's going to "widen the pay gap." Women aren't benefiting as much as men from the AI job market's growth, Kantenga says. According to LinkedIn's data, only 26% of new hires in AI roles were women in 2025, compared with 50% in non-AI occupations. Women make up one fifth of hires for head of AI roles, 26% of hires for director of AI roles and just 18% of hires for member of technical staff (MOTS) roles, which are three of the highest-paying AI jobs. The only role in which women are hired as often as men is data annotator, the lowest-paying AI occupation on LinkedIn's list with a median pay of $51,000. Women also hold just 10% of the CEO and top tech roles at AI organizations, according to a February 2025 study from Russell Reynolds, a leadership advisory firm. In short, Kantenga says, "women are less well-represented at every rung of seniority" in AI firms. For students and young workers hoping to break into the field, Kantenga's top recommendation is to pursue a computer science degree. Despite the challenges computer science grads have faced in the job market in recent years, he still believes it's the "ticket" to landing an entry-level AI role. The unemployment rate for recent computer science graduates rose to 7% as of 2024, according to the latest available data published by the Federal Reserve Bank of New York, higher than the overall unemployment rate for recent grads. But computer science majors are also projected to bring in the highest starting salary of all majors among the class of 2026 at $81,535, according to a report from the National Association of Colleges and Employers. Beyond education, "make sure you have experience working with data," Kantenga says. It's not enough to have a "theoretical background" in data engineering -- these AI roles will require you to "know how to prepare and use data effectively." If you have limited real-world experience, Kantenga recommends working on AI side projects to build your portfolio and showcase "what you're passionate about." That's not to say that all young workers are feeling positive about AI, according to Kantenga. Many have mixed feelings about how AI will affect their future employment prospects, and not all young workers are interested in AI jobs. "Some of them feel very positive, and it's very much a career opportunity," he says. "Some of them are worried -- is it going to replace me?" Want to get ahead at work? Then you need to learn how to make effective small talk. In CNBC's new online course, How To Talk To People At Work, expert instructors share practical strategies to help you use everyday conversations to gain visibility, build meaningful relationships and accelerate your career growth. Sign up today!

Many recent grads say AI is making it harder to get a job. Economists aren't so sure

Irene Chang, 21, studied engineering in college because she enjoys solving problems. She also wanted to major in something she thought would lead to a stable career. "I wanted to work a job right after college," she says. But that has been a challenge. Chang graduated in May from Georgia Tech with a bachelor's degree in industrial and systems engineering. She says she started applying for entry-level data analyst roles about a year ago, while she was still in school. Chang estimates she has submitted around 450 applications since last September and has had about 19 interviews -- so far she hasn't gotten any offers. Jacqueline Kline, 25, is in the same boat. She graduated this past spring from Florida State University with a master's degree in communications. Kline says she did everything she could to land a job after college. She networked, completed multiple internships and started applying for jobs before she even finished her degree. So far, she says, she has applied to more than 500 entry-level jobs since last December. "It's hard knowing that I'm doing everything that I can to be successful and feeling like that's not enough," Kline says. Both Kline and Chang say they think AI may be part of the reason that finding a job in their respective fields has been such a challenge. "I feel like the entry-level skills that you have, they're important, but also something that AI can do very efficiently," Chang says. According to the Federal Reserve Bank of New York, the unemployment rate for recent graduates -- which it defines as 22-to-27-year-olds with a new bachelor's degree or higher -- was 5.7% as of June, more than the rate for all workers, which stands at 4.1%. Many of those recent graduates think AI is to blame for their employment struggles. This year, in a survey from ZipRecruiter, 47% of them said AI had already affected hiring in their field. But is AI really the problem, or is it more complicated? Here's what economists have to say. How AI may be impacting the entry-level job market Stanford University economist Erik Brynjolfsson says AI is impacting the labor market for entry-level roles. "AI is not the whole story, but it's part of the story and the evidence is building," he says. Using payroll data, Brynjolfsson and his co-authors found that since late 2022 -- when large language models like ChatGPT started popping up -- early-career workers ages 22 to 25 in AI-exposed roles, like software developers and marketing managers, have experienced a 16% relative employment decline. In comparison, employment rates for older workers in AI-exposed fields and for all workers in jobs that aren't easily automated -- like home health aides, physical therapists and construction workers -- remained stable or have grown over that same time period. Brynjolfsson says there are two reasons young workers in AI-exposed fields may be feeling the job crunch more than others. First, because young workers typically bear the brunt of economic contractions -- when companies choose to shed employees or pull back on hiring -- junior roles often get cut first. Second, Brynjolfsson says, large language models are trained on codified knowledge -- information that has been written down in a textbook or document. "That has a lot of overlap with entry-level people," he says, because it's the same type of knowledge that inexperienced college graduates rely on when they're getting started in the workforce. But AI models lack the wisdom that comes with on-the-job experience or tacit knowledge that isn't written down. "So for more senior workers, their kind of knowledge is not as affected," Brynjolfsson says, adding that they don't need to compete with AI as much. Or is remote work to blame? Harvard University economist David Deming isn't convinced that AI is to blame for the challenging early-career job market. "If you look very carefully at the timing, it looks like the decline in junior hiring actually started a bit like six months before ChatGPT was released. And so what that tells me is it's something else," Deming says. "I think it's more like remote work." A recent analysis from the New York Fed found that companies are less likely to hire recent college grads into roles that can be done remotely. As remote jobs increased following the COVID-19 pandemic, so did unemployment among younger college grads, the New York Fed found. The analysis also found that AI didn't explain the rise in unemployment among younger workers and that remote work was more of a driving force. Deming says hiring an entry-level worker requires companies to invest time and resources to get them up to speed. Employers hiring for remote jobs may be less likely to choose an entry-level candidate because it's harder to train them from afar. "And so the value proposition of hiring a junior person when you know they're going to be at home is just not as great," Deming says. "And conversely, the value proposition of hiring a more senior person is greater, and because of remote work, you have access to a broader pool of talent from around the country." And here's another thing to consider: University of Chicago economist Anders Humlum says if AI were replacing entry-level jobs, you'd expect to see companies that rely heavily on AI to hire fewer workers. But that's not what the data shows. Humlum points to a study done by the financial accounting firm Ramp and the workforce research company Revelio Labs. The study examined AI spending and employee head count across more than 21,000 U.S. firms, from early 2021 to early 2026. It found that at companies making the largest AI investments, entry-level head count grew by 12% over the two years following AI adoption. "If we look at the heavy users of these tools, the firms that are paying a lot of money to Anthropic and OpenAI to subscribe to their models, they are hiring more than anyone else," Humlum says. Change is coming While they disagree on how much AI is impacting the job market right now, all three economists interviewed by NPR do agree that there's a major employment shift underway. They're among the economists, tech executives and researchers who signed an open letter last month warning that AI could bring about an economic transformation larger than the Industrial Revolution and potentially cause widespread job displacement. Even so, they're optimistic. "This is a really tough time for a lot of the conventional jobs," Brynjolfsson says. "I also think it's one of the most amazing times to be alive, where these technologies allow people to do things they never could have before." Humlum says most of the university students he talks to "just have the perception that they are being screwed over by the new technology." But he says that's "a very premature conclusion." He thinks AI is poised to help workers more than it will hurt them. Deming agrees. "I think on net, [AI] will be a force for good," he says. And he does expect AI to affect employment in the future, even if he thinks it isn't happening yet. "But it'll take some time and it's going to be very bumpy and messy, and, you know, buckle up." His advice for recent grads like Kline and Chang: "Hang in there."

Why AI leaders are hedging their bets

The Fast Company Executive Board is a private, fee-based network of influential leaders, experts, executives, and entrepreneurs who share their insights with our audience. A year ago, the leaders of the biggest artificial intelligence companies were saying AI would take over huge swaths of the work people do, and soon. Some put timelines on it. But recently, many have changed their tune. Nvidia's Jensen Huang took aim at others blaming layoffs on AI. He called the narrative "too lazy" -- AI's barely had time to become productive, let alone be responsible for cuts that started years ago. Sam Altman offered something closer to a mea culpa, admitting he expected far more entry-level white-collar jobs to be gone by now, and that his intuition must've been off. Their walk-backs aren't rooted in AI being less capable than they thought. It hasn't gotten worse at writing code or drafting emails or summarizing reports over the past year -- it's gotten better. What's changed is the leaders' read on what determines whether that capability translates into fewer jobs, or more output from the same people. PERCEIVED VS. DEMONSTRATED FLUENCY These leaders are catching up to something some companies already understood: Handing someone an AI tool doesn't automatically make them faster or better at their job. It simply changes what their mistakes look like and how fast they're noticed. A bad first draft used to take one hour to write and ten minutes to spot. Now a first draft can take 30 seconds using AI. If you're lucky, or smart about it, a manager reviews it. All too often, it goes straight into a client email or a board deck. In a lot of ways, the work has become faster, sure. But has it really improved? Most employees and managers will tell you AI's changed how they work. Look at what they're producing, though, and the story gets murkier: a summary nobody checked against the source, a prompt that took longer to fix than the task would've taken on its own, or an analysis that sounds sharp until someone asks where the numbers came from. This gap between perceived and demonstrated fluency is the part the AI leaders seemed to underestimate when they made their original predictions. It's the part still missing from many corporate AI strategies today. WHAT CLOSES THE GAP Leaders who framed this moment as machines stepping in for people were asking whether AI can do a task in place of a person. Some would still say that's the question. But this isn't the right question to organize a strategy around. Whether your people get more done with AI matters more than whether AI could, in theory, do the job without them. And that comes down to things most companies skip, like teaching people to use it well and checking what the work looks like once they do. They hand out tool access, run a generic onboarding module, and call it training -- then wonder why the skill never shows up in the work. What closes the gap is teaching through live, hands-on, role-specific application. People should learn to use AI on the same problems their job throws at them, not a sandbox exercise that disappears the moment training ends. A salesperson needs to practice AI-assisted prospecting on accounts they know, not a customer relationship management demo. A finance analyst needs to build a model with AI in the loop on next year's forecast instead of a tutorial dataset. The learning must live inside the work or it won't transfer to the work once the course is over. BUILDING CAPABILITY WITH INTENTION Organizations consistently overestimate where their workforce stands, and that overestimation is exactly what makes the gap so persistent. You can't close a problem you don't think you have. The AI leaders walking back their predictions are conceding the point: Machines were never going to make people optional. The companies that build workforce capability on purpose -- rather than assume it'll show up on its own -- are the ones who'll see the gains everyone keeps hearing about. A year ago, the prediction was that AI would replace the need for people. A year later, the people making that prediction are the ones admitting they were wrong. The only thing AI seems to have removed is the excuse not to invest in your workforce. Daniele Grassi is president and CEO of General Assembly.

Young workers thrive in high-paying AI jobs amid broader market struggles

In a study published on August 18, 2026, LinkedIn identified the top 12 fast-growing AI jobs based on an analysis of the platform's data. The report highlights that while young workers face challenges in the broader job market, they are thriving in the expanding AI sector. The unemployment rate for young workers aged 22 to 27 was 7.2% in June 2026, significantly higher than the 4.1% unemployment rate for all workers, according to data from the U.S. Census Bureau and U.S. Bureau of Labor Statistics. LinkedIn's analysis reveals that Gen Z workers make up over two-thirds of hires for two prominent roles: forward deployed engineer and AI engineer. These positions offer median annual salaries of $199,000 and $166,000, respectively. Millennials also play a significant role, constituting 60% of new hires for head of AI roles, which boast a median pay of $236,000. The surge in AI job postings is notable, with a 14% increase between 2023 and 2024 and a staggering 156% rise between 2024 and 2025. The median salary for typical AI roles is $177,000, more than double the $80,000 median salary for non-AI roles. Kory Kantenga, Head of Economics for the Americas at LinkedIn, noted that the influx of money into AI has compelled companies to significantly raise salaries to attract talent. Moreover, 91% of AI workers hold bachelor's degrees or higher, with many in senior roles possessing graduate or doctoral degrees. This educational requirement contributes to the high pay associated with AI jobs. Despite the growth in AI jobs, women are notably underrepresented. In 2025, only 26% of new hires in AI roles were women, in stark contrast to 50% in non-AI occupations. Women occupied just 10% of CEO and top tech roles at AI organizations as of February 2025. Kantenga remarked that women are less represented at every level of seniority in AI companies. Furthermore, 86% of clerical and administrative roles vulnerable to AI takeover are held by women, according to The Brookings Institution. For students and young professionals aspiring to enter the AI field, pursuing a computer science degree is recommended. Although the unemployment rate for recent computer science graduates rose to 7% in 2024, this major is projected to yield the highest starting salary of $81,535 among the class of 2026. Kantenga encourages aspiring AI professionals to gain practical experience with data and work on AI-related projects to build their portfolios and showcase their skills. However, there are mixed feelings among young workers regarding the impact of AI on their future employment.

AI Is Closing Some Career Doors

Young workers do not learn the real lessons of work from the glamorous assignments. The first messy draft and the customer question they are not ready to answer are teachers. They also learn in the meeting where they mostly listen, scribble notes, and start to understand what good judgment sounds like. For decades, that has been the first rung of the white-collar career ladder. AI is starting to crack it. The strongest early signal is showing up in tech. Software developers aged 22 to 25 have seen nearly a 20 percent employment decline from its 2022 peak. That is the labor market telling us that the work that used to help young people get in the door is changing quickly. This is spreading beyond software. AI is compressing junior work across knowledge industries: drafting, research, QA, analysis, customer support, and first-pass code. Companies gave these tasks to younger workers because they are lower-stakes, repeatable, and teachable. That work was where people learned. The apprenticeship problem Companies should use AI to move faster. I run companies, invest in founders, and understand the pressure to operate more efficiently. But there is a cost if nobody is thinking about where judgment comes from. Judgment gets built through reps. You make a recommendation and find out why it was wrong. You write something, get it redlined into oblivion, and learn what "good" means. AI can manage much of that work. It cannot give a 22-year-old the scar tissue that comes from getting something wrong in front of a customer. That is the apprenticeship problem. The optimistic case for AI is productivity, but who benefits from that productivity? If AI makes large companies more efficient while shrinking entry points for young workers, opportunity concentrates. The people already inside the system get more leverage. The people outside have fewer entryways. A different future That is not the only possible future. The same AI weakening some traditional entry-level paths is also giving individuals access to company-building infrastructure that used to require full teams and meaningful capital. A single founder can now use AI tools to write and review code, design interfaces, build landing pages, run customer research, create content, and automate support. What once required a small cross-functional team before product-market fit can increasingly be orchestrated by one highly capable founder. Depending on the tools, a solo founder's AI operating stack can run roughly $3,000 to $8,000 a year, or closer to $12,000 for a small team. The revenue math is changing, too. AI-native startups can generate many times more revenue per employee than traditional SaaS companies. Solo-founded companies now represent more than one-third of new U.S. startups. This does not mean everyone should become a founder. Being a founder is hard, gritty work. Most companies fail. With AI, there is still potential rejection pain, cash-flow pressure, and required discipline to build something people want. But AI does change who can credibly start a company. Some of the brightest young people entering the economy may prove themselves as builders instead of as junior employees. The new graduate mostly likely to become a founder will not always look obvious on paper. Pedigree and headcount matter less than motion and mindset. Investors will want to know: What have you built? How fast do you learn? Can you use tools across domains? 4 founder profiles I see four founder profiles emerging. The first is the permissionless builder: the person already shipping apps, GitHub repositories, or small online businesses before anyone grants them a job title. This person may not have the cleanest résumé, but they have proof of work. They have crossed the line from "I have an idea" to "I launched a thing." The second is the cross-functional orchestrator: the person who can talk to a customer, build the first version, figure out distribution, and know when the system is unraveling. AI rewards people who can connect functions and do more than execute one task. The best founders will translate across product, customer, operations, and go-to-market. The third is the domain-native problem spotter: the person close to a painful industry problem who knows why the obvious solution never worked. They may come from logistics, healthcare, finance, retail, or a family business. They know the problem from the inside. With AI, that proximity becomes more valuable because they can prototype around the problem without a full team. The fourth is the self-taught operator: the person who learns through tools, feedback loops, and failed attempts. They learn because they are already trying to make something work. In an AI-native world, the advantage goes to people who can teach themselves the next workflow before it becomes a job requirement. These are the people I would bet on. They have AI-native workflows, think in systems, experiment quickly, and have a history of building things before anyone asked them to. Build a different career ladder Founders have always done things that look unreasonable. They fly across the country for one customer. They build the ugly first version. They send the awkward email. They get told no, then find another way in. AI does not change that part of company-building, but the builder becomes more leveraged once they decide to move. The conversation about AI and young workers should include creation as well as displacement. If AI produces enormous productivity gains, will those gains sit inside a few large institutions or spread through a rising generation of AI-native builders? The first rung of the old career ladder is crumbling before our eyes. We can spend the next decade trying to preserve every piece of it, or we can build new ways for young people to learn, create, and own the value they produce. The next career ladder may be built by the people who never got a clean shot at the old one.

Here's How Job Applicants Are Trying to Make Their Resumes Stand Out: 'There's No Way to Fight It'

Workers are trying to adapt amid uncertainty over whether or not AI will replace jobs. AI is showing up everywhere these days, from the classroom to the meeting room. It is also noticeably impacting the job search, as candidates strive to make their resumes AI-proof. As companies pour trillions of dollars into AI and reshape how work gets done, job-seekers are scrambling to show that they can keep up. Some are learning new tools and earning certifications. Others are sprinkling AI language into their resumes and LinkedIn profiles, hoping the right keywords will catch a recruiter's eye. "Everybody's keyword dumping," Denise Bitler, an executive career coach in Tampa, Florida, recently told The Wall Street Journal. LinkedIn says that the share of U.S. users adding AI-related terms to their profiles jumped 70% last year. Employers are contributing to the trend as well, as they increasingly pack job descriptions with AI terminology. AI references are also becoming more common in job postings. In the first quarter of 2026, 8% of U.S. roles listed on Indeed mentioned AI, up from 3% in 2022. And the jobs aren't limited to software engineers or data scientists. Postings now include roles such as "AI Autonomous Truck Test Driver" and "Physical Therapist (AI Documentation)." That reflects the bigger uncertainty hanging over the labor market. No one knows exactly if AI will eliminate large numbers of jobs, make workers more productive or do both at once. Job-seekers face the challenge of figuring out how to position themselves in a labor market that is rapidly changing. Embracing AI Grace Gravestock, a self-employed management consultant, decided earlier this year that she needed to get ahead of the shift to AI. After struggling to find work for about 18 months, she began spending more time experimenting with the technology, updated her LinkedIn profile to call herself an "AI adoption leader" and started pursuing a certification to demonstrate her skills. She had been wary of the technology but ultimately concluded that avoiding it was no longer realistic. "There's a lot of big change happening now, there's no way to fight it," Gravestock told the Journal. She also understands why many workers worry that AI could eventually replace them. The rush to add AI credentials is showing up in people's online work histories, too. Stanford economists Nick Bloom and Gideon Moore compared current LinkedIn profiles with archived versions from before 2023 and found that users have been retroactively adding AI references to past job titles and descriptions. Overall, today's profiles contain 30% more AI mentions than their earlier versions did. Balaji Padmanabhan, director of the Center for Artificial Intelligence in Business at the University of Maryland, told the Journal that "there's a lot of uncertainty in the labor market." What worked yesterday might not have an effect tomorrow, he said. Padmanabhan has witnessed the impact of AI firsthand. He and his colleagues launched a free AI training course last May, expecting 500 sign-ups. Demand was overwhelming. About 62,000 people from a range of industries have signed up for the course to date.

Want to Avoid Being Replaced By AI? A Former Microsoft Exec Says This Is How to AI-Proof Your Career.

His simple test could tell you if your job might be vulnerable to AI. Rob Collie spent 13 years at Microsoft helping lead business intelligence work in Excel. Even with that technical background, he says that AI initially scared him. "I wasn't immune to that," Collie says in a new interview with Entrepreneur about the technology's potential effect on his business and career. He says his "fear turned into excitement" after he began studying how companies can actually put AI to work. Now, as CEO of the 50-person consulting firm P3 Adaptive, Collie is betting that AI will not simply eliminate work. It will change who gets to create software, solve operational problems and become indispensable inside a company. Collie recently published a book about organizational AI strategy called Fair Game. His central advice to workers is straightforward: Do not wait to see what AI does to your job. Help your employer use it. "You want to be on that train," Collie says. "The closer you can get to AI, and the more you can embrace it, the more you can give it a hug, that gives you the opportunity to be nimble." That matters because, in Collie's view, no one can reliably predict which roles will change most or how quickly. However, workers can develop practical AI skills that make them agents of change rather than passive observers. How to have an AI-proof career Collie says that the best way someone can AI-proof their career is to become an active adopter of AI at work, not waiting to see if the technology replaces them. He advises workers to learn to apply AI in a real business context beyond using a general chatbot for one-time tasks. He also tells workers to become part of the company's "adoption team" by identifying practical workflows AI can improve. "You can blow people's minds with this stuff," Collie says. "You can be the AI expert at your company in relatively short order, and that's the approach that I've been taking with my company." Collie says that workers can build or help create specialized AI assistants that use company-specific instructions, documents and data. He notes that no one can reliably predict where AI is headed, or how their job will change, so they should focus on developing agility rather than betting on any one job in particular. Collie urges workers to use AI to strengthen their judgment and productivity, not replace thinking. His preferred employees are "AI-first," meaning that their first question when approaching a task is what kind of AI system could help them do it better. He also encourages workers to leverage business knowledge when using AI. Collie says that people who understand their company's processes are more likely to build helpful tools with AI. The jobs most at-risk from AI Collie does not deny that AI will displace some workers. But he believes employers make a mistake when they view the technology only as a headcount-reduction tool. "You're missing some of the things that it can do for you," he says. AI can provide capabilities a company did not previously have, rather than merely replace existing tasks, he adds. Still, he sees particular risk for entry-level roles where employees can become productive quickly. His test is simple: How much training and organizational knowledge does a worker need before doing the job competently? "The entry-level jobs that are the most replaceable are the ones where a new hire can be truly productive in the first two weeks," Collie says. Call-center work is one clear example. These jobs often involve structured policies, decision trees and predictable response flows, all information that can be documented and supplied to an AI system. In contrast, roles that require years of accumulated judgment, nuanced relationships and substantial company-specific experience may be harder to fully automate. Collie uses the technical concept of an AI's "context window" to explain the difference. An AI can absorb only a limited amount of information at once. If the knowledge required to perform a job fits into that practical limit, the role may be more exposed. If it takes a long time and substantial experience to become effective, AI may have more difficulty replacing the worker outright. For young professionals, however, the threat extends beyond individual jobs. Collie said many companies appear to have become hesitant to hire inexperienced candidates while they try to understand AI's impact. "The zero-years-experience job is now turning into, like, a three-years-experience job," he says. He believes some of that pullback may reverse as employers gain confidence about where human talent still adds the most value. But job-seekers should not count on a return to the old entry-level ladder. AI is a new form of computing Collie's career has long centered on making sophisticated technology accessible to people outside large enterprises. At Microsoft, he led business-intelligence features in Excel 2007 before joining the early Power Pivot effort. That eventually became Power BI, a business analytics platform that turns raw data into interactive charts and dashboards. He later founded P3 Adaptive to help smaller companies use tools that had once been largely out of reach. That experience shapes how he sees the AI moment. He rejects the idea that businesses need to build their own large language models or hire elite AI researchers before getting started. "AI is just a new form of computing that we haven't had before," Collie says. He adds that companies need to learn its strengths, limitations and practical uses, much as they learned to use traditional software. Off-the-shelf chatbots can boost individual productivity, Collie says, but they cannot automatically understand a company's specific context. For example, ask a general-purpose AI tool to write a proposal, and it may produce generic, lowest-common-denominator copy. The difference comes when a business gives the system clear instructions and relevant internal knowledge. Collie compares that process to onboarding a highly educated new hire. An AI may know a vast amount about the world, but it knows nothing about an individual organization. "If you hired someone who had a Ph.D. in everything but had never met your business, they wouldn't be useful on day one," he said. "You would need to give them training." Build an AI handbook Collie calls those written instructions "handbooks." They explain what the AI's role is, which information it should use, what standards it should follow and how it should communicate. A marketing assistant could be supplied with a handbook that captures a company's brand voice, audience, products and approval rules. Instead of opening a chatbot that must be re-briefed every time, an employee could work with a specialized assistant that already has the relevant context. "Every time it wakes up, it needs to be handed that manual before it starts working with you," Collie says. "That is super easy to do." He argues that business knowledge, not advanced technical training, is the starting point. A manager who knows how a team handles customer issues, or a marketer who knows the company's tone, can help create the handbook. Technical employees may still be needed to connect systems and data securely, but they should not be isolated from the people closest to the work. "Don't treat AI as a technology problem," Collie says. "It's a business problem. It's about teaching it about your business." Collie is applying that philosophy inside P3 Adaptive. The firm has frozen hiring while it retrains its current staff and rethinks roles built around dashboard development. Going forward, he said, workers will need to be "AI-first" in how they approach their work. That does not mean allowing AI to think for them. It means using it to handle routine work, accelerate drafting and coding, organize context and extend what skilled professionals can accomplish. "It's not using AI to replace their brain," Collie says. "It's AI to take on lots and lots of stuff for them to make them more effective and more productive."