by admin | Oct 28, 2026 | Future Jobs
If you have a quiet feeling that your job is changing shape even though your job title hasn’t, a new report just confirmed you’re not imagining it. Workday, the HR and finance software company, released its 2026 Global Workforce Report on October 5, and its headline finding is refreshingly specific: most business leaders don’t expect AI to shrink their workforce. They expect it to rewrite the jobs people already have.
That’s a different story than the “robots are coming for your job” headlines we’ve covered before, like our look at McKinsey’s occupation-transition forecast. This report is less about people losing jobs and more about people needing to grow into the job they already have. Here’s what it actually found, and what to do with it.
AI Isn’t Replacing Jobs So Much As Reshaping Them
Workday surveyed over 6,000 employees and business leaders, plus a separate pulse survey of nearly 6,000 workers, and pulled real hiring data from roughly 550 enterprise employers. The pattern was consistent: 40% of leaders expect AI to help them get more out of current employees, while only 28% expect it to cut headcount outright.
But “your job isn’t disappearing” doesn’t mean “your job is staying the same.” Internal career moves actually slowed down at 57% of employers year over year, and nearly 4 in 10 employees said their company went through a reorganization in the past year. As Workday’s VP of People Systems, Phil Willburn, put it: “Employees may not be changing jobs, but their jobs are changing around them.”
The Skills Employers Want Have Flipped, Fast
This is the part worth paying attention to. Job postings asking for basic AI skills, things like simple prompting, rose through late 2025, peaked in January 2026, and then fell 25% in the months after. Meanwhile, demand for hands-on AI skills, like building AI tools, automating workflows, and AI engineering, rose 51% between September 2025 and July 2026.
- Knowing how to “talk to ChatGPT” is no longer a standout skill on its own.
- Employers increasingly want people who can build with AI, automate a process, or troubleshoot when it breaks.
- Postings mentioning management and leadership skills fell 7%, and training-related skills fell 13%, over the same stretch.
This tracks with what we found when we looked at which skills matter most for future jobs: the bar keeps moving up from “can you use the tool” to “can you make the tool do something useful for your specific job.”
Quick tip: if your current AI skill set stops at writing good prompts, that’s a good place to start, not finish. Spend a weekend learning how to connect an AI tool to a real workflow you use at work, even a small one, like auto-sorting your inbox or drafting first-pass reports from raw data.
There’s a Real Gap Between What Workers Need and What They’re Getting
Here’s the uncomfortable number: in a September 2026 survey, 79% of employees said they know which skills they need to learn, but only 66% said their employer actually helps them develop those skills. That’s a 13-point gap between knowing and being supported.
Interestingly, confidence isn’t the problem. 65% of employees said they’re confident they could learn new skills if asked to. And among heavy AI users, the picture is a little split: 62% think AI will make their current skills less valuable, yet 76% still expect it to open new career opportunities. People aren’t in denial about this. They’re waiting for someone to hand them a ladder.
From years of working around websites, online tools, and cybersecurity projects, I’ve seen this exact gap play out one-on-one: people are genuinely willing to learn new tools, but they rarely get a clear, structured path to do it at work, so they end up learning in scattered bits on weekends, exactly when it’s most likely to be dropped.
Hiring Is Getting More Competitive, Not Less
If you’re job hunting right now, this part explains why it feels harder: the median number of applicants per open role climbed to 69, up from 58 the year before. In financial services and tech specifically, applications per role rose 27% and 40%, and more than half of applicants in both sectors said AI helped them apply to more roles than they otherwise would have.
In other words, AI is making it easier to apply everywhere, which means everyone is applying everywhere, which means a generic application gets lost faster than it used to. Standing out now leans more on showing you can actually do something with AI tools, not just that you own a ChatGPT account.
Common Questions
Does this mean AI is mostly safe for jobs?
It means mass layoffs aren’t the main story at most companies right now. But roles are genuinely changing shape, and people who don’t adapt their skills risk becoming a worse fit for the job they already hold.
What AI skill should I build first?
Start past basic prompting. Learn to connect an AI tool to one real task you already do, whether that’s data cleanup, drafting, or scheduling, so you have a concrete example to point to.
My employer isn’t offering AI training. What now?
You’re in the 34% majority, according to this report. Free structured options exist, including our own guide to learning AI for free, which doesn’t require waiting on a company training budget.
Is this only a tech-industry trend?
No. The report’s hiring data covers enterprise employers broadly, and the skills shift shows up in management, training, and general operations job postings too, not just engineering roles.
Final Takeaway
The honest summary of this report is that AI isn’t quietly erasing jobs for most companies right now, it’s quietly rewriting them, and the rewrite rewards people who move past basic AI use into actually building and troubleshooting with these tools. If you want a head start, revisit our breakdown of how AI is changing future jobs and treat your current role less like a fixed job description and more like a skill set you keep updating, on your own terms, before someone else decides it needs updating for you.
by admin | Oct 21, 2026 | Future Jobs
If you have applied for a job in the last year or two, there is a decent chance software read your resume before a person did. That shift already happened quietly. The next one is louder: AI agents that do not just screen candidates, they also apply for jobs on a candidate’s behalf, sometimes without a human reading each application first. A survey published this week shows a lot of employers are not comfortable with that at all.
What the New Survey on AI Agents Applying for Jobs Found
Recruitics, a talent acquisition platform, published its “State of AI in Hiring” report on October 20, 2026, based on a survey of 159 talent acquisition decision makers at US companies with 5,000 or more employees. The question was simple: what should happen when an AI agent submits a job application for someone?
- 27 percent said they would block AI agents from applying on a candidate’s behalf entirely.
- 35 percent said they would allow it, but flag those applications for extra review.
- 18 percent said they would build a separate application path just for AI agents.
- 18 percent said they would treat an agent’s application the same as a human’s.
The reasons behind this are practical, not just cautious. Recruiters in the survey said they lose close to 9 hours a week on average to applicants who turn out to be unqualified, duplicates, or not who they claim to be. Seventy percent said their teams lose six or more hours a week this way, and 74 percent believe more than a quarter of the applications they receive now contain AI generated or AI assisted content. Application volume per role has also grown, with 71 percent of leaders reporting more applications over the past year.
None of this means AI is banned from hiring. It means employers are trying to tell the difference between a candidate who used AI to present themselves well and a flood of applications that were never really tailored to the role at all.
Why Hiring Is Turning Into AI on Both Sides
This is not only a job seeker trend. Employers are automating too. LinkedIn’s Hiring Assistant, the company’s first AI agent for recruiters, already turns job descriptions into candidate pipelines and can handle early messaging and interview scheduling. SHRM has covered this shift in its AI and HI series, where recruiting expert Marcus Sawyerr predicts “agent to agent” hiring, where an AI agent representing the employer and one representing the candidate handle part of the early conversation before two people ever speak. His point that stuck with me is that resumes and job descriptions are losing some of their old value, since both can now be produced in minutes. What replaces them, in his view, is evidence: a project someone can walk you through, not just a polished document anyone can generate.
From my own experience building websites, online tools, and automated workflows, I get the appeal of handing something repetitive like job applications to an AI agent. But hiring is still a trust exercise between two people, and that is exactly where full automation can quietly work against you instead of for you.
What This Means for Your Own Job Search
You do not need to avoid AI tools to search for work. You do need to use them differently than a lot of people currently are.
- Use AI to draft and tailor one strong application, not to blast the same content to fifty roles. Volume is exactly what recruiters say they are now filtering against.
- Keep your resume and your LinkedIn profile accurate. More sourcing now happens directly from profiles, so an outdated or exaggerated one can cost you before a recruiter ever messages you.
- If a tool offers to “auto apply” to dozens of jobs for you with one click, treat that as a warning sign rather than a shortcut. It is the exact pattern employers are now building filters for.
- Have something you can point to beyond a document, even something small: a short project, a writing sample, a tool you built. It is harder for a filter to ignore and easier for a person to remember.
Important tip: if you use AI to help write a job application, always read the final version yourself before sending it and edit it until it genuinely sounds like you. A generic, clearly AI written application is exactly what recruiters say they are now trained to flag.
If this is new territory for you, our guide on how to use AI to apply for jobs without losing your own voice walks through exactly how to use these tools without sounding like everyone else. It also helps to understand what actually happens after you click apply, since a lot of this filtering is already built into the systems employers use today.
The Bigger Picture: AI Agents Are Reshaping Work Itself
Job applications are just one visible example of a wider pattern. AI agents are increasingly acting on people’s behalf across scheduling, research, customer support, and now recruiting, on both the employer and candidate side. If the term itself is still a little fuzzy, our beginner explainer on what AI agents actually are is a good place to start, and our piece on working with AI agents at work covers how this is already changing day to day jobs, not just hiring.
The honest takeaway from this survey is not that AI agents are bad for your job search. It is that employers are now actively watching for low effort automation, and the job seekers who do best will be the ones who use AI to sharpen a smaller number of genuine applications, not to multiply weak ones.
Common Questions
Is it illegal to use an AI agent to apply for jobs? No. There is no law against it. Some employers simply reserve the right to flag or reject applications they believe were submitted by an agent rather than reviewed by the applicant.
Will using AI hurt my chances of getting hired? Not if you use it to improve one application you understand and stand behind. It becomes a problem when it is used to submit many near identical, untailored applications at once.
How can I stand out if other candidates are using AI too? Bring something a filter cannot easily copy: a specific project, a clear personal answer to “why this role,” or direct outreach to a real person at the company.
Final takeaway: AI agents are now active on both sides of hiring, and employers are starting to push back on the automation they did not ask for. Use AI to help you apply better, not just faster, and your job search will hold up fine in a world where everyone has access to the same tools.
by admin | Oct 14, 2026 | Future Jobs
You finish a report in half the time because an AI tool drafted most of it. That feels like a win, and most days it is. But here is a question worth sitting with: if you stopped using that tool tomorrow, could you still do the whole job yourself, at the same level, without it?
For a growing number of workers, the honest answer is “not as well as before.” That gap has a name now: AI deskilling. It is quietly becoming a bigger talking point among economists than the old fear of mass AI layoffs, and it is worth understanding properly instead of just scrolling past the headline.
What AI Deskilling Actually Means
Deskilling is not about losing your job to a robot. It is about a job slowly losing the harder parts that used to make someone genuinely skilled at it. If an AI tool now does the tricky 20% of your work, the 80% left for you can end up being the easier, more repetitive part, and the muscle you built for the hard part can fade from disuse.
Anthropic, the company behind the Claude AI models, looked at this directly in its newest Economic Index report, published in early 2026. The researchers measured millions of real Claude conversations and ranked tasks by how much education they typically require. The pattern they found was not subtle.
What the Data Actually Found
Claude sped up college-level tasks by roughly 12 times compared to doing them manually, versus about 9 times for tasks that only need a high school level of skill. In plain terms, AI is giving the biggest boost to the hardest, most specialised work, not the simple stuff.
Success rates told a related story. Claude completed about 66% of college-level tasks successfully, against 70% for easier tasks. Slightly lower, but not low enough to cancel out that huge speed advantage. So people keep leaning on it for the hard parts, again and again.
The researchers then ran a thought experiment: if you removed every task Claude currently handles well from a typical job, what would be left? On average, the remaining work skewed toward lower-education tasks. The report is careful to call this a signal, not a prediction, but it is exactly the deskilling pattern described above, shown in real usage data rather than guesswork.
- Data entry keyers and radiologists came out more affected once success rates were factored in.
- Teachers and software developers came out less affected than raw task coverage alone would suggest.
- Augmentation (AI helping with part of a task) is now more common than full automation, at 52% versus 45% of conversations.
The Job Losses Didn’t Show Up the Way Everyone Feared
Here is the part that should calm some nerves. A separate McKinsey survey published in September 2026 found that 32% of businesses expected AI to cut their headcount, but only 14% actually reported a reduction a year later. Two thirds of companies said total employment barely moved.
So the mass layoff wave many people braced for mostly has not arrived. That is genuinely good news. But it also means the more interesting story is happening quietly inside jobs that still exist, in the shape of what those jobs now ask of the people doing them. This is a case where two reports from two different organisations line up: fewer people are being pushed out, while the actual content of the work is shifting underneath them.
Important tip: before letting an AI tool handle a task you are still learning, try doing it yourself first at least once a week. You keep the skill, and you also get much better at spotting when the AI’s answer is wrong.
How to Protect Your Own Skills
From my own experience managing websites, online tools and cybersecurity work, the tools that save the most time are also the easiest ones to lean on too hard. I have caught myself letting an AI assistant handle a config or a script I used to write from scratch, and had to deliberately slow down and work through it manually again so the underlying skill did not quietly disappear.
A few habits actually help here:
- Use AI to check your work after you attempt it yourself, not only to produce the work from scratch.
- Keep doing the hardest 10% of a task by hand on purpose, even when a tool could do it faster.
- Ask the AI to explain its reasoning, not just give you the answer, so you are learning alongside it.
- Rotate between tasks you let AI handle and tasks you deliberately do unassisted, so no single skill goes untouched for months.
This matters most for people early in their careers. If you are a new graduate already facing a tougher entry-level job market, skipping the slow, difficult version of a task because AI can do it faster means you never actually build the judgment that senior roles depend on later. The fast way out can quietly become the slow way up.
If you work alongside AI tools day to day, it also helps to understand how AI agents actually operate at work, so you know exactly which parts of a task you are handing off versus which parts you are still responsible for. And if you are updating your CV or thinking about how to present your skills, it is worth reading about how to list AI skills on your CV in a way that shows real judgment, not just tool familiarity.
Common Questions
Is AI deskilling the same as AI taking your job?
No. Deskilling is about a job changing shape while you keep it, not about losing it. The McKinsey data above shows actual job losses have been smaller than feared so far.
Which jobs are most at risk of deskilling?
Anthropic’s data points to roles with a lot of repeatable, well-documented tasks, like data entry and certain parts of radiology, as more exposed once you factor in how reliably AI completes that work.
Can deskilling be reversed once it happens?
Largely yes, with deliberate practice. Skills fade from disuse, not permanently. Doing the harder version of a task occasionally, even when a tool could do it faster, keeps the underlying ability alive.
Does this apply to students too?
Yes, arguably even more. If you lean on AI for every assignment’s hardest part, you reach the job market with a tool-shaped gap in the exact skills employers are testing for in interviews.
Final Takeaway
AI is not quietly emptying out the job market the way a lot of headlines predicted. What it is doing, according to the people who built the models, is reshaping what is left of each job once the hardest parts get handed off. That is a manageable problem if you notice it early. Keep practising the difficult 10%, ask your tools to teach you rather than just finish for you, and you get the speed without losing the skill underneath it. For more on how AI is reshaping the workplace, see our full guide on how AI is changing future jobs.
by admin | Oct 7, 2026 | Future Jobs
If you graduated this year and the job hunt feels harder than it should be, you are not imagining it. New data out of Stanford puts real numbers behind something young job seekers have felt since 2023: the entry level ladder in AI heavy fields is getting shorter, and it just got shorter again.
The AI job gap for new graduates is not a vague headline this time. It comes from a team at Stanford’s Digital Economy Lab that tracks real payroll data every month, and their latest update shows that gap widening in a way that is hard to argue with.
The AI Job Gap for New Graduates: What the New Data Shows
Economists Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen run an ongoing study called “Canaries in the Coal Mine”, built on ADP payroll records covering millions of US workers. Their August 2026 update, tracked live on the public Canaries Dashboard, found that workers aged 22 to 25 in the most AI exposed occupations are now about 19 percent below where their employment would be if it had kept pace with less exposed peers the same age. A year earlier, in July 2025, that gap was 15 percent. It is not closing. It is opening wider.
In plain numbers, employment for 22 to 25 year olds in the two most AI exposed job categories fell about 11 percent between November 2022 and June 2026. For the same age group in the three least exposed categories, employment grew around 10 percent over that same stretch. Experienced workers in the exposed roles show no comparable drop at all.
Why This Is Hitting New Grads, Not Everyone
The researchers are careful to say this is not mass layoffs. Companies are not firing junior staff en masse. The adjustment shows up almost entirely in hiring: businesses are opening fewer entry level seats in roles where AI tools can now do a chunk of what a junior employee used to do, things like first drafts, basic research, routine code, and simple analysis.
The split that matters more than the headline number is this:
- Jobs leaning on codified knowledge: formal, documented, teachable skills found in manuals and training material. AI tools are strong here, and these roles are losing entry level ground.
- Jobs leaning on tacit knowledge: judgment built from experience, reading a room, handling an unusual situation. These roles are flat or growing, for junior and experienced workers alike.
So the real question is not “will AI take my job.” It is closer to “does my job mostly ask me to follow a known process, or does it ask me to use judgment that is hard to write down.” The second kind of work is holding up far better.
From my own experience running websites and online tools, I have watched this play out in miniature. The tasks I used to hand to a junior freelancer, formatting, basic edits, first pass research, are the ones AI now handles for me directly. The tasks that still need a real person are the ones where someone has to make a judgment call I cannot fully explain in a brief.
Quick tip: when weighing two entry level roles, ask how much of the job is “follow the process” versus “use your judgment on something messy.” The messier, more judgment heavy role is more likely to still need a person in five years.
What This Means for You Right Now
This does not mean entry level jobs are disappearing everywhere. It means the templated end of junior work is shrinking faster than the judgment heavy end. A few moves that track with what the data actually shows:
- Target roles where the job description leans on client handling, troubleshooting, or decisions under uncertainty, not just task execution.
- Learn to work with AI tools directly instead of competing with them on tasks they already do well. Being the person who checks, directs, and fixes AI output is more durable than being the person who does the raw task by hand.
- Build a visible portfolio of judgment calls you made, not just outputs you produced.
- Look at fields the data shows holding up: cybersecurity, hands on healthcare support roles, and skilled trades all lean heavily on tacit, situational knowledge.
We covered the bigger picture in Is AI Taking Entry-Level Jobs? What New Graduates Should Know, and this new Stanford update confirms the trend described there has kept moving, not reversed. For the wider decade long view, our piece on what McKinsey’s new report means for your career pairs well with this one.
If you want to stand out while this shakes out, something you can actually show matters more than another resume line. Our guide on how to build an AI portfolio for free walks through that, and AI skills on your CV: what the research says they are actually worth is a useful check before you list anything that sounds impressive but means little to a hiring manager.
Common Questions
Is this just one study, or is it widely accepted?It is one of the most carefully built studies on this question, using real payroll data rather than surveys, updated monthly through a public dashboard. The researchers call it an early warning system, not a final verdict.
Does this mean experienced workers are safe?The data shows no comparable gap for experienced workers in the same roles, but that reflects hiring patterns today, not a guarantee for the future. Skills still matter more than years on the job.
What industries show the widest gap?The study measures exposure using a methodology from Eloundou, Manning, Mishkin, and Rock’s research on LLMs and labor market exposure . The pattern holds across jobs heavy on codified, documented tasks, think routine coding, basic research, and first draft writing, rather than any one named industry.
Should I avoid learning AI tools since they are taking entry level jobs?No, the opposite. Jobs where AI complements a worker’s judgment are growing, not shrinking. Learning to direct and check AI tools is becoming a baseline skill rather than an optional extra.
The Bottom Line
The AI job gap for new graduates is real, it is measured in actual payroll data, and it widened again this year. But it is a hiring slowdown in specific kinds of roles, not a collapse of the entry level job market. The graduates doing best right now are aiming at judgment heavy work and treating AI tools as something to direct, not compete against. That is more useful than either panic or denial, and it is something you can act on this week.
by admin | Oct 2, 2026 | Future Jobs
If you have been wondering what an “AI engineer” job will even look like a couple of years from now, Anthropic just gave a surprisingly detailed answer.
On October 2, 2026, Anthropic launched Claude Frontier Academy, a $100 million program built to train 10,000 engineers to deploy AI inside real companies by the end of 2027. It is not a course you can sign up for on a whim, and it is not aimed at beginners. But it tells us something useful about where enterprise AI jobs are actually heading, and it is worth understanding even if you never set foot in one of its classrooms.
What Is Claude Frontier Academy, Exactly?
According to Anthropic’s own announcement, Claude Frontier Academy trains what it calls “Frontier Deployed Engineers,” using a structure the company compares to a medical residency. Instead of a short certificate course, participants learn directly from Anthropic’s own engineers, work through realistic deployment cases, and have to pass a graded assessment before they earn a credential.
The goal is not to teach people how to chat with an AI model well. It is to teach experienced software engineers how to actually deploy Claude inside a large organization, safely and usefully, which turns out to be a much harder and more specific skill than most job listings make it sound.
How the Program Actually Works
The residency runs in five stages:
- A multi-day, in-person program working directly alongside Anthropic engineers
- A simulated enterprise deployment, including picking a use case and taking it through a security review
- A graded practical exam based on that simulated work
- A 12-week residency leading a real Claude project inside the participant’s own company
- A final assessment that leads to a credential
Pass the first stage and you earn a Claude Resident Engineer badge. Finish the full 12-week residency and pass the final review, and you earn the Claude Frontier Deployed Engineer badge. The first cohorts are running in San Francisco, New York, and London, with the first badges expected in early 2027.
Who Can Actually Get In
This is the part most people miss: you cannot apply to Claude Frontier Academy directly. Participation works by nomination, and your employer has to ask Anthropic’s account team or Partner Account Manager whether your organization is eligible. Anthropic says it is looking for hands-on software engineers with strong fundamentals, real experience building with large language models, and a track record of helping others adopt AI on business-critical problems. Prior experience with AI agents specifically is not required.
The first cohorts already include engineers nominated by Accenture, Bain, Capgemini, Commonwealth Bank of Australia, Deloitte, McKinsey, Morgan Stanley, and Novo Nordisk, which gives a pretty clear signal about the kind of large, consulting-heavy or regulated organizations this is built for first.
Tip: you do not need a nomination to start building toward this kind of role. From my own experience working across websites, online tools, and cybersecurity projects, the engineers who get picked for programs like this are almost always the ones who were already quietly doing the work, not the ones waiting for a formal invitation. Deploying a small AI tool for a real task at your own job, documenting what went wrong and how you fixed it, is the closest free substitute available right now.
Why This Matters Even if You Will Never Be Nominated
Claude Frontier Academy is a single company’s program, but it reflects a much bigger problem. The World Economic Forum estimates that AI and information processing will affect 86% of businesses by 2030, and roughly 1.1 billion jobs could be reshaped by technology over the next decade. The Forum’s own conclusion is blunt: AI will create more jobs than it destroys, but only if companies deliberately invest in training people, rather than just installing new software on top of old job descriptions.
That is exactly the gap Anthropic is trying to fill with its own residency model, and it lines up closely with what we covered in our look at McKinsey’s recent report on AI and career transitions: the jobs that disappear and the jobs that get created rarely look the same, and the people who prepare early have a real advantage. Our broader explainer on how AI is changing future jobs covers the same shift from a wider angle.
A new, named, credentialed job title like “Frontier Deployed Engineer” is also a useful signal on its own. When a company this size spends $100 million building a formal training pipeline for a role, it usually means that role is about to show up in a lot more job postings, even outside the original partner companies.
If you want to start building toward that kind of role on your own terms, our guide on how to build an AI portfolio for free walks through creating small, finished projects that prove you can actually do the work, and our breakdown of what AI skills are actually worth on your CV shows how specific, demonstrable claims outperform vague ones when recruiters are deciding who to call.
Common Questions
Can I apply to Claude Frontier Academy myself?Not directly. Participation is by nomination only. Your employer has to contact its Anthropic account team or Partner Account Manager to check eligibility.
What exactly is a “Frontier Deployed Engineer”?It is Anthropic’s name for an engineer trained and credentialed to safely deploy Claude inside a real company, covering everything from picking a use case to passing a security review and running a live 12-week project.
Do I need prior experience with AI agents to qualify?No. Anthropic says strong software engineering fundamentals and real large language model experience matter more than prior agent-specific work.
Will this program expand beyond the first eight partner companies?Anthropic has not published a specific list of who comes next, but with a $100 million commitment and a target of 10,000 trained engineers by 2027, further partner organizations are a reasonable expectation.
Final Takeaway
Claude Frontier Academy will stay out of reach for most readers, at least for now. But it is a useful preview of where serious AI jobs are heading: toward named roles, formal credentials, and real accountability for getting deployments right, not just knowing how to write a good prompt. Whether or not a nomination ever lands in your inbox, the underlying skills it is built around, hands-on deployment experience and the ability to show your work, are ones you can start building today.
by admin | Sep 30, 2026 | Future Jobs
If you have ever wondered whether your job will still exist in ten years, you are not alone. A new report from the McKinsey Global Institute, released this week, puts a real number on that worry: as many as 11 million American workers may need to change occupations entirely by 2035 because of AI.
That headline sounds alarming on its own. But the full report, called “Workforce in Motion,” is actually more useful than scary once you read past the number. It tells you which jobs are shrinking, which are growing, which skills are suddenly worth a lot more, and who is most at risk of getting stuck. This post breaks down what McKinsey found and what it means for your own AI job transitions, whether you are years into a career or just starting one.
What McKinsey’s Report Actually Found
McKinsey estimates that automation could reduce demand for roughly 36 million US jobs over the next decade. At the same time, growth in the AI value chain and the wider economy could create demand for more than 40 million jobs. Net result: the US economy could end up with about 5 million more jobs than it has today, not fewer.
The catch is that the jobs disappearing and the jobs appearing are rarely the same jobs, or even in the same city. About 11 million workers, roughly 7 percent of the current workforce, may need to move into a completely different occupation. McKinsey says that works out to around 770,000 people switching occupational groups every year through 2035, which is more than three times the historical average. For comparison, that pace is close to what happened during the pandemic, when job switching briefly spiked in a similar way.
The steepest declines are concentrated in three areas: office and administrative support, retail and sales, and transportation and logistics. On the other side, healthcare support, construction, management, manufacturing, education, and IT roles are all projected to grow.
Why This Isn’t a “Robots Take All the Jobs” Story
McKinsey partner Anna Kortis summed it up well: this shift will happen faster and at a bigger scale than past transitions, but it is not mainly about jobs vanishing. It is about roles getting rebuilt. The report estimates that about 70 percent of workers will see their current role change in some way, even if they never switch job titles. Around a quarter of workers will see major task changes, more than 30 percent of their work hours shifting to new kinds of tasks, while most others will see smaller adjustments.
From my own experience working across websites, online tools, and cybersecurity over the past few years, this tracks with what I have actually seen happen. Very few of the tools I use today replaced a job outright. What they did was quietly change what the job involves, so the people who adapted fastest ended up doing more interesting work, not less work.
The Skills That Are Suddenly Worth More
One of the most useful parts of the report is a breakdown of which skills are becoming more valuable. McKinsey groups them into three buckets:
- Essential skills: problem-solving, leadership, communication, and attention to detail, which matter across almost every occupation.
- Enabling skills: decision-making, innovation, and critical thinking, which show up disproportionately in higher-paying roles.
- Empowering skills: AI fluency, adaptability, resilience, and curiosity, the traits that let someone keep learning as tools keep changing.
The report found that demand for AI fluency specifically has grown 11 times since 2022. Demand for adaptability is up fivefold. If you want one practical place to start, our guide to the AI skills that matter most for future jobs walks through how to build these without needing a technical background.
Tip: you don’t need to become a programmer to benefit from this shift. McKinsey’s own data shows the fastest-growing skill demand is for AI fluency and adaptability, not coding. Knowing how to use AI tools well in your own field counts.
Who Faces the Hardest Path
This is the part of the report that deserves more attention than it has gotten. The transition is not landing evenly. Lower-wage workers are 7.6 times more likely to need an occupational change than higher-wage workers. Workers without a bachelor’s degree are 1.8 times more likely, women are 1.6 times more likely, and younger workers face a 1.6 times higher likelihood than workers in their prime career years.
McKinsey sorts the 11 million transitioning workers into three types of paths. About 14 percent have a direct path, meaning they can move into a growing job with little retraining and no pay cut. Another 41 percent face a winding path, needing moderate retraining and possibly a temporary pay cut. The remaining 45 percent are on what the report calls an unpaved path: large skill gaps, and often a credential or certification requirement that takes real time to earn. In fact, roughly 85 percent of growing jobs now ask for some kind of credential, which is one of the biggest practical barriers workers actually run into.
This lines up with what we covered in our earlier look at the World Economic Forum’s four possible futures for AI and jobs by 2030: the outcome depends heavily on how much support workers get, not just on the technology itself.
What You Can Actually Do About It
You cannot control McKinsey’s projections, but you can control how ready you are. A few practical steps worth taking this month:
- Spend an hour looking honestly at which parts of your current job are repetitive and could shift to AI tools, and which parts genuinely need human judgment. The second group is where your value is growing.
- Pick one AI tool relevant to your field and actually use it on real work, not just a demo. Comfort matters more than mastery right now.
- If your field requires a credential to move up, look into free or low-cost options before paying for an expensive course. Our roadmap for learning AI for free is a good starting point.
- If you are actively job hunting, know which AI skills employers actually look for before you put them on paper. We broke that down in what AI skills on your CV are really worth.
None of this guarantees a smooth transition. But McKinsey’s own numbers show the difference between a direct path and an unpaved one usually comes down to preparation that starts months or years before you need it, not after.
Common Questions
Is this report saying AI will cause mass unemployment?No. McKinsey actually projects a net gain of around 5 million jobs in the US by 2035. The concern is about the difficulty of moving between shrinking and growing occupations, not a shortage of jobs overall.
Which jobs are shrinking the fastest?Office and administrative support, retail and sales, and transportation and logistics are seeing the steepest declines in demand, according to the report.
Which jobs are growing?Healthcare support and professional roles, construction, management, manufacturing, education, and information technology are all projected to grow.
What is the single most useful skill to build right now?McKinsey’s data points to AI fluency and adaptability as the fastest-growing in demand, and neither requires a technical degree to develop.
Final Takeaway
McKinsey’s report is one of the clearest pictures yet of what AI job transitions will actually look like this decade: not a wave of unemployment, but a much faster shuffle of who does what, with real winners and real people who get left on a harder path if they wait too long to start adapting. The most useful thing you can take from it is not the 11 million figure. It is the reminder that the workers with a direct path forward are usually the ones who started building AI fluency and adjacent skills before they needed to.
