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Ep 735: The AI Labor Shift: When It Will Happen and What It Means for Jobs (Start Here Series Vol 13)
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This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.
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Buying AI tools is the easy part. Getting your employees to actually use them for anything meaningful. That's where most companies fall apart. Section's own research shows over 90% of employees only use AI for basic tasks. That's not roi. Section coaches every employee on real role specific use cases, tracks who's driving AI impact and gives you the data to prove it's working. For more, check out section@sectionai.com in the past two weeks alone we've seen reports of more than 40,000 plus jobs that will be eliminated soon due to AI. And that's on top of the already 45,000 plus AI related job cuts in the US we've seen so far in 2026. Yet we've read all those studies and projections for years Talking about how AI's broad capabilities will create millions of jobs that just don't exist yet. So it seems we may soon then be entering a crossroads of traditional employment slowly dying in the birth new era of work. I'm calling it the AI labor shift. And while it does sound kind of exciting, like we're going to be living in a new era, it's also pretty scary. Like have we humans really wasted decades of our working lives becoming subject matter experts just to now babysit AI agents who are way smarter and faster than us but just need constant hand holding maybe. And that's what we're going to be tackling on today's show Everyday AI in our Start Here series. All right, so here is the big picture. Jobs are getting eliminated and no one knows what the future of work looks like. So this year so far we've already seen 45,000 plus AI related tech layoffs because, well that's what's happening. Big tech companies. I've always said this going back to 2023. Let me just start here already getting on a on a side rant and we barely started. I've been saying since 2023 that I will ultimately take more full time roles than it will create. Yes, there's going to be tons of new jobs, but I've always said that follow the big tech companies because they going to be the first dominoes to fall, right? And We've already seen five companies that are planning to spend $700 billion on AI infrastructure. So the jobs are going away, the AI investments are piling up and well, we don't know where these New jobs are going to be which puts us all in this kind of pickle. So we're going to be, well, coming with some receipts, facts, stats and trends. And on today's show we're going to be addressing that and more. And stick to the and you're going to learn the financial trick behind AI layoffs that most workers and journalists are falling for. You're going to learn why the real displacement timeline is already underway and almost no one can see it yet. We're going to go over which everyday roles are first in line to go and which ones did not even exist two years ago. And I'm going to give you at the end a three step survival guide you can start using before you even finish your coffee today. Also stick around. I did put together some extra assets for this show and I'm going to tell you at the end how you can get a hold of those. So a lot to go over on today's show. If you're new here, welcome to Everyday AI. My name is Jordan Wilson and this is our Start Here series. Because after doing this every single day for three years, more than 700, I don't know, 30 now episodes. 730 episodes. I didn't have a good answer. When new people would buy the podcast and be like Jordan, there's too much. Where do I start? And I'd always be like, I don't know. Well now you start with the Start Here series. This is the essential podcast series to both learn the AI basics and to double down on your AI knowledge. So go to starthereseries.com that is going to give you free access to our inner circle community. Yeah, you can't find it anywhere else. It's unlisted, it's private. So go to start hereseries.com that's going to give you free access as well. Is your going to go straight into the Start Here series space where you can go listen to every single volume of this series all right there in one place. So if you miss Our last volume, volume 12, we talked about the state of the AI race. Who will win in 2026, OpenAI, Microsoft, Google or Anthropic? And today let's get straight into it. Let's get shift in. Talk about when this AI labor shift is going to happen and what it ultimately means for jobs. So here's what's happening and whether you're listening to this live in real time or maybe you're listening to this, I don't know, in 2027. Well, here in early 2026, the big tech companies are cutting jobs in mass. Right. So, Meta, we just saw a report. More than 15,000 workers on the chopping block because of AI. Speaking of block, block just cut 4,000 jobs. And Oracle is reportedly planning up to 30,000 reductions because of AI efficiencies. And this ties into the larger trend here in the US that we actually saw a pretty down month for jobs. Right. The February 2026 jobs report showed 92,000 jobs lost in unemployment, hit four and a half percent. So not a normal jobs report. So it's kind of the, a lot of the big tech layoffs combined with, you know, kind of agentic AI's surge in late 2025, this job reports, it's got everyone kind of talking and a lot of people understandably, so maybe a little uneasy about what does all of this mean for my job, my career? That's what we're going to be unraveling. But I think AI in general when it comes to jobs, because let's start there. It's become the scapegoat. Yes, sometimes, you know, AI is actually being used and jobs do truly become redundant, but not always. According to a recent Harvard Business Review study that showed that 60% of hiring managers who cite AI as the reason for layoffs, only 2% are actually replacing those roles or augmenting those roles with AI. So it seems like sometimes AI is just kind of a get out of jail free card or a scapegoat. And a lot of these quote unquote AI layoffs, especially from big tech, are just over hiring corrections, you know, also rising interest rates and just the overall squeeze. I think it's getting harder for some companies to turn a profit for a lot of reasons. I think one of those reasons is actually AI, right? AI is allowing more people, or, sorry, it's allowing more companies to do more with less, which in some instances can drive prices down, it increases competition, which are all good things. Right? But what that has meant for maybe large bloated companies is their margins are starting to shrink, whether they're using AI or their competitors are. They're losing out on new clients, new projects. But it's definitely a delayering from probably a lot of post pandemic over hiring and correcting for that. Right. The Amazon layoffs In late 2025, the 16,000, they were attributed to AI. But then essentially Amazon CEO kind of admitted, yeah, it's also just about reducing layers. So it seems like sometimes blaming something on AI and we'll get to why that's actually advantageous. For companies to just say, yeah, these are AI job losses. Sometimes they are, but a lot of times it's just, well, you had way too many employees to begin with because so many companies over hired. And as technology naturally progresses, the outputs have to keep pace, right? So for those companies that have changed their roles to be more AI native and the output comes and the outputs and the deliverables and the artifacts have scaled correspondingly, I don't think they're running into these same problems. But a lot of the companies that are slower moving in the job description is the exact same as it was 10 years ago. I think those are the types of companies that are having to go into these massive layoffs. And here's why they're still saying it's an AI layoff, whether it is or not. Because when you do, Wall street loves you, y'. All. I literally been saying this since 2023, since before AI washing was a thing. That's what this is. This is called AI, AI washing. When companies, you know, make huge cuts, they say, hey, it's AI efficiencies and their stock goes through the roof. So block cut 40% of their staff earlier this month, cited AI and their stock rose 22%. So this is the AI washing that has also just created mass worker anxiety. And I don't think it's always necessarily grounded in reality. Yes, sometimes these massive job layoffs are because of AI. I think more than anything else, it's a lot of these CEOs and people, boardrooms making these decisions are seeing the future of AI, not necessarily what AI is today. And I think a lot of companies are now having to deal with the egg on their face of not properly training their employees or sitting on the fence in 2023 and maybe even 2024, and they're finally seeing and realizing it for the first time and they're like, oh, oh wait, yeah, this is going to completely change our workforce, right? So maybe let's do this, let's do this big tech Fortune 100 playbook where, you know, we can just chop a bunch of jobs, say it's because of AI. And for public companies at least, it's usually a playbook that works well. So what's actually driving these cuts to get a little technical? Well, not super technical, but maybe for the average everyday listener who's not following this, it's really one big shift. Shift. It's going OPEX to Capex, right? That's operating expenditures to capital expenditures. It's going from companies who have, let's look At Meta. Right, Meta, nearly 16,000 jobs reportedly are going to be gone soon because of AI, yet they're spending tens of billions of dollars in capital expenditure building AI infrastructure. Right. And even just five companies, Amazon, Alphabet, Meta, Microsoft and Oracle combined, this year are going to spend $700 billion in capital expenditures for AI. So instead of money going to salaries, benefits, and all of those nagging costs that come along with working with humans, and instead they're going into data centers, silicon cooling plants, power generation. Yes, there's obviously some new jobs that go around, you know, creating and maintaining, you know, so if, if your, your niche has been, you know, building data centers for decades, you're striking it rich right now. But I think it's income. I think it's incumbent on ourselves to realize that these companies right now, the biggest tech companies, that I think everyone's trying to follow their lead. Notice they're not investing $700 billion in today's AI. They're not saying, oh my gosh, let's take this money and hire more people. Because today's AI is, is where it's at. No, because the learning curve, the education curve, the training curve is so far behind. I think what's happening is we're seeing the agentic, the autonomous capabilities that we have in early 2026 in big tech companies are saying, oh my gosh, you know, I think it's going to take the general population 10 years to rework and to unlearn. Right. Don't reskill, don't upscale. That's failure. You got to unlearn and rebuild. Right. But I think the bigger tech companies are saying, okay, well, I think they'll be able to unlearn their workforce maybe in three, four, five years and really take advantage of that. So I think they're placing bets on tomorrow's technology, not necessarily today's. And that's because we've come to realize now, I think, you know, Claude code maybe helped open a lot of people's eyes. You know, open AI Codex, open claw, right now, these, you know, autonomous, you know, systems that just work. I think that's what's really changed the narrative here. And I tackled this on episode 7 30. So if you want to go listen to this one, we talked about Anthropic's recent labor study and the realization that this is, well, AI can already do the work that humans, smart humans, right? Humans with college degrees, it can already do it. So, you know, one kind of stat to pick on there is we went over the theoretical AI coverage, which is Essentially what AI models are actually capable of and the observed use, which is, well, what humans are actually using AI for. So this is an anthropic study. So they used millions of anonymized chats inside of Claude and then a essentially federal job data that looked at all these different jobs, 20,000 different skills, and they just matched it all up, right? All these anonymized chats, what are they actually being used for? And what can the models theoretically do? And what this led to is a huge capability gap. And what we've realized, and I've been talking about this for a long time, it's number one, education and training. It's like everyone asked me, like Jordan, how do I grow my company? Train your people, educate them, right? Get a bunch of mes running around, that's all you need, right? Literally just have a bunch of people that just understand AI every day, play with it every day, test it every day, scope it every day, every day, every day. That's all you do. Every day AI, right? Anyways, it's a huge capability gap because people don't know. And it's not just, you know. One example in Anthropic's report was computer and math. Row roles showed 94% theoretical AI coverage. So the AI could theoretically do 94% of those 20,000 roles that had to do with computer and math roles, but only 33% observed use. But it's across the board, right? AI coverage in management, legal and businesses all sat down below 20% despite 80% theoretical capabilities. So more. Most organizations are still just looking at AI like a chatbot or like a smarter search. And they're not actually seeing it for the workforce shift. It is. So when does the real shift hit and who's going to get hit first? That is the big question and we're going to answer that after a quick word from our partners. Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value. Most employees are still just using AI to summarize meeting notes. If you're the one responsible for AI adoption at your company, you need section. Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result, you go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems and you can prove the ROI. Stop guessing. If your AI investment is working, check out section@sectionai.com that's S E C T-I-O-N A I dot com. All right, so when will that shift happen and what type of roles might feel it? Well, shift has already hit the fan, y'. All. So yeah, here's what most people are kind of ignoring. It's already happening, right? Because I think in 2024, 2025, AI has already been augmenting senior workers while entry level hiring has quietly slowed. That's another thing that we talked about in episod. Going over that anthropic report saw that 14% slowed down growth for entry level workers. So I think this year and next year, as we look at this timeline of when the shift is going to happen, I think teams are going to restructure and the capability gap is going to start to close because companies have already started to recognize and realize that capability gap and they're going to do something about it. And I think what is going to ultimately happen is the corporate ladder is going to crumble upon itself. In traditional management, middle management, it's going to be gone. We've already seen the, the entry level jobs, you know, kind of slowly starting to disappear. Not even getting into the silver tsunami as baby boomers, you know, retire. I think the workforce is going to flatten because at the same time, personally, I don't see it. Yes, there are going to be new companies that come and do great revenue, right? But a lot of these new companies that are starting are starting very lean because when you start a new company from scratch, you, most people are, well, they're using AI to do it because you can find yourself like, oh my gosh, I can start, right? I'm, I'm a big tech worker. I just got laid off. I know what I'm doing. I'm a consultant. I just got laid off. I know what I'm doing. I can use all these AI tools. I don't need 10 employees to start. I can start in one person, do the work of 10 people. So I think that the overall workforce is going to shrink, but specifically at companies, those teams are going to get probably smaller. It's called, I call it quiet, Quiet hiring, Right? It's the opposite of quiet firing. They're just not really going to hire people anymore. Some companies may not go through the mass firings, but then I think the last leg of this short timeline is in 2028-2030 which is when I think we're going to start to see some of the gains of these, you know, like the $700 billion in infrastructure, essentially. I think that people who follow AI every day, like you and me, we've realized now these autonomous systems can do. Right. These agents now are actually very capable agentic models. Right. With a 20amonth subscription to Claude or Gemini or Chat GPT, you could do. Right. Even what it took 10 people to do with Chat GPT alone in late 2022 when it first came out. Right. The capabilities are compounding. So I think by, you know, 2028, 2030, that's when I think the autonomous workflows are going to start to go mainstream. Right. In the same way that if someone from the mid-1990s said that, hey, one day, you know, knowledge workers are all just going to be people who work on the Internet. You're like, no, the Internet, you sure about that? Yeah, same thing. It's, everyone's going to be orchestrating agents in the2030s. And I think that's kind of the last shift of what's happening with jobs. So, yeah, I think we're going to see normally job changes are very slow because tech innovation comparatively to what we've seen with AI is snail's pace. Right? Which is why jobs sometimes take 5, 10, 15, 20 years to change. Even with the Internet, social media. Right. A lot of those jobs are still relatively unchanged or took two decades to change. Right? That's not what we're going to see with AI. We're going to see jobs completely gone in two years. Yes. There's going to be new roles. We don't know what those are, but we'll see. Like I said, I've always been on the record. I do think AI is going to, quote unquote, take more jobs than it will make, at least when it comes to traditional full time employment. So here's what's actually happening, where that shift is happening. And this is a recent report, a great one from the Federal Reserve bank of Dallas, essentially looking at codified knowledge versus tacit knowledge. So codified knowledge, that's essentially that grunt work, right? This is what a lot of times new graduates rely on to, you know, earn their keep and climb the corporate ladder. But this is now stuff. AI just does way better, right? It's, it's researching, it's writing, it's personalizing, synthesizing information, creating spreadsheets and PowerPoint. Right, right. AI is way better at that. So codified knowledge, tacit knowledge, different. Right. AI is not really, at least yet, you know, started to tread on, you know, nuanced expert judgment. Although I do think it's, it will get there eventually. But when you look at replacement, it's starting with codified knowledge. And companies are just, well, not hiring the young juniors, right? Junior researchers, junior analysts that would normally do some of that codified work. Now it's all very simple for a large language model to do. And that's why we've seen that 14% kind of hiring drop that I referenced earlier. So what can you do about it starting today? Because the reality is there's probably a good chunk of us, even if you do, right, get to exercise that task adjustment a lot, right? Like that, that nuance that, you know, is more flexing your, you know, getting projects through the finish that aren't always related to artifacts and deliverables, right? Maybe the inner office workings and politicking a project to fruition. Well, yes, a lot of people spend a good amount of their time there, but a lot of people are still doing those quantifiable tasks, right? Those things that maybe seem like, oh, maybe an AI could do this better. Which is why I think a lot of people are feeling uneasy about their careers because it's happening this, this collision of the labor shift, AI innovation and companies now being willing to do this in mass, right? It's creating this sense of uneasy. So here's what you can do about it starting today. First of all, if you have been tuning in, right, if you are putting AI to work right now, you're in a better place than most, right? That same Dallas Fed study showed that tech wages actually rose and outpaced the, you know, normal inflation, cost of living, things like that. Because what this study found is even though companies are hiring fewer people and there's maybe at least right now, fewer jobs, the people who do still have that, those jobs are getting paid more. And well, here's why. Because now more than ever, your skills compound, right? What you can accomplish with AI today is much different than what you could accomplish with AI three, four years ago. Here's what I mean by that. Your skills can 3x4x5x10x fairly easily. Whereas even to say that sentence two years ago kind of felt, you know, like, lofty. It's not. That's, that's a realistic outcome right now. So companies, at least that are ahead of the curve have realized that. So they're saying, okay, well, I don't want an average person compounding their average outcome, right? Like, I want smart People. So companies are paying more to get people in there because yes, they are augmenting and, you know, amplifying themselves in domain, it's kind of this sweet spot. And you know, studies have also shown that workers with AI skills are earning 56% more, more than those without. And then last, kind of the sweet spot here is domain expertise plus AI fluency is the highest value combination in any field. Right. So it is harder, and I think it will be harder for younger workers to, quote, unquote, go the traditional corporate ladder route and break into that upper echelon. I think it's going to be increasingly more difficult. I think it'll be easier for them to start their own thing. Right. But that's kind of the sweet spot. If you do have, you know, 10 to 30 years of experience and you still have, have a decade or three to go. Right. And you are practicing AI, that's a great spot to be. So number one, if you find yourself in that category, even though you may feel unease, right. With what's happening with AI, you're in a good spot because the new jobs are not as out of reach as you think. I think a lot of people, when you think about AI job growth and jobs that maybe don't exist yet, who's going to be qualified for them if not us? Right. If not the people who have been using AI daily since 2023, 2024. Right. A lot of people think that there is this class of world class engineers that are going to take all these few. No. Right. Large language models. Yes. They've been around for, you know, more than 10 years, but for the most part they've only been popularized now for, for. Right. So if you've been using them, you are in that kind of, you know, 1% of world class experts. So I think these emerging roles, including things like context engineer, agentic orchestration, AI auditor, geo strategist. Right. The barrier has dropped for these roles significantly and I think it rewards and it leans more into that domain expertise over, you know, engineering ability or coding ability. Right. I think it's telling when you've seen literally some of the smartest engineers in the world, the people who built the very systems that we use are now saying that they're essentially vibe coding. Right. And because the models are building themselves, so even the people at Frontier AI Labs, they're saying, I don't write code anymore. Right. You know what, they're doing the same thing that we're doing, they're orchestrating agents. Right. Which, what does that require? It Requires domain expertise and it requires, I think, great communication skills and it requires ongoing education, but it doesn't require, you know, a PhD in machine learning. So here's your three step survival guide as we wrap up. Number one, audit your role. Separate those daily tasks into what is tacit and what is codifiable. That doesn't mean that you know, oh my gosh, my job's, you know, all codifiable things. These are things that you know AI can do. No, you need to know where you stand because that's going to help you decide what path you should ultimately take. Then step two, you need to document your reasoning. You need to write down, you know, your day to day how you make these hard decisions, things that maybe don't show up in spreadsheets and presentations and right, so outside of the codifiable, where is that tacit knowledge, where is your domain expertise that maybe doesn't show up in a quantifi in an easily quantifiable way? Right. This is kind of your domain reasoning, your expert reasoning. This is something I've been very bullish for companies to do. Right, but you need to also do it on the employee side because as more and more skills become agentified, you really need to amplify and invest more in those areas where, well, where's your reasoning? Right? Where's your domain reasoning? And then last but not least, I think you need to double down on one AI platform deeply until it becomes an extension of your own expertise. Here's what I mean by that. I think it's so easy to get caught up in the AI tool or trend of the week. Okay, you can't do it. You will fail. I don't care who you are, I can't do it. Two years ago I could. The innovation now is too fast for any one person, right? Like I said a couple years ago, fairly easy to keep up and sharpen your skills on the AI tool or trend of the week. You can't do it anymore. Find that one area that you can cross over with your domain expertise and that AI platform. Dig in there, in. Regardless of how AI jobs, how this thing shuffles off when the AI labor shift hits the fan, you, if you follow this survival guide today, are going to at least be in a better position than the overwhelming majority of people who are just going to be looking in the air, confused, saying, what do I do now? You know exactly what to do. So I hope this version of the start here series, volume 13 was helpful going over the AI labor shift when it'll happen. And what it means for jobs. So like I said earlier, I put together a ton of resources because I know one thing. I know a lot of people feel uneasy about this. Right. Understandably so. So I had a lot more information I wanted to pack into the show, but I want these Start Here series to be like 25, 30 minutes, not 60 minutes. So we have a ton more. So go repost Today's show on LinkedIn and I will send you all of the additional assets that we put together as compliments and supplements to this episode. So if you're wondering where's the LinkedIn show? Well, if you're listening on the podcast, go check the show notes. There's always something that says join the conversation on LinkedIn, right? Just click that. That's going to take you to this LinkedIn live stream for this very show. Click the repost button and then I will send you all of these additional assets. Then when you're done, make sure you go to start here series.com that is going to give you free access to our private hidden, you can't find it anywhere else, our inner circle community. And in the Start Here series, circle their space. You can go listen every single volume in this series. So I hope this was helpful. Tell me about it. Do you want more of these? How long should we keep these things going? I work for you. Let me know. So thank you for tuning in. I hope to see you back tomorrow and every day for more everyday AI. Thanks, y'. All. If you're leading AI at your company and your employees are barely scratching the surface of what AI can actually do, you need a better plan. Section Coaches employees on real role specific use cases, tracks adoption across your entire organization and helps you prove the ROI to your CEO, your board, or whoever's asking. That's the job Section helps you do it. Check out more on section@sectionai.com and that's
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a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.