Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 854: The AI Labor Shift: When It Will Happen and What It Means for Jobs (Start Here Series Vol 13)

Episode Date: September 2, 2026

40,000+ AI-linked job cuts in two weeks. 😬But AI is also supposed to create millions of new jobs.So are we watching traditional work slowly die while the replacements don't exist yet?And did we really spend decades building expertise just to babysit AI agents that are smarter and faster than us?Join us live to find out.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode: AI-Driven Job Cuts and Layoffs AnalysisThe AI Labor Shift: Timeline PredictionsBig Tech AI Infrastructure InvestmentsAI as Layoff Scapegoat and "AI Washing"Capability Gap: AI Use vs. PotentialRoles Most at Risk in AI Labor ShiftCodified vs. Tacit Knowledge Job ImpactThree-Step Survival Guide for AI CareersDomain Expertise Plus AI Skills PremiumNew AI Job Roles and Emerging OpportunitiesTimestamps:00:00 "AI Jobs, Investments, and Displacement"06:18 AI's Impact on Jobs08:20 "AI Layoffs and Overhiring"11:36 Driving AI Value with Section16:00 AI's Workforce Impact Gap18:25 "AI Impact on Work Dynamics"20:25 AI-Driven Workflows Mainstream by 203024:25 "AI Impact on Job Security"27:47 "Emerging AI Roles and Expertise"31:17 "Surviving the AI Labor Shift"33:38 "Boost AI Adoption with Section"Keywords: AI labor shift, AI job displacement, AI and employment, AI job loss, AI job creation, traditional employment decline, future of work, AI workforce transformation, AI layoffs, tech layoffs,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Welcome to the Everyday AI podcast. My name is Jordan Wilson, and for the past three and a half years, we put out more than 800 episodes. Yet, one of the most common questions I get I didn't really have an answer for. Where do I start on the Everyday AI podcast? And that's why we started the Start Here series. And with the fall now back in full swing, the Everyday AI podcast is going back to school and playing back the entire Start Here series from front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike.

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In the past two weeks alone, we've seen reports with more than 40,000 plus jobs that will soon be eliminated to TV on. Yet we've read all the reports for years. because the dog is the first of the future in the future. Well, it seems to be a soon-the-age-thro's like to be. Then slowly trying in the birth of a new era of Earth. I'm following a new year to ship. And, well, it sounds kind of excitement like for it can be a new era.

It's also pretty scary. like the really pasted of our lives and this whole teaching to our

wastewater and we're going to be maybe we're going to be digging into this topic a lot on the

station of every day in I will our start here series welcome named

Jordan Wilson and let's just get straight to it here is the big picture of what we're going to be talking about today. Jobs are getting work. And no one does what the future of work is going to look like.

I mean, we've always seen 45,000 in the playoffs and we just got word in the past week and a half that there's going to be 40 pounds. Or, you know, the CEOs of these companies are all citing because of AI in the current team for. Yet, I, Our census not a billion dollars, you know, by infrastructure.

And we know that AI will operate billion to build, but eliminate more traditional jobs that it replaces. So that puts us at this crossroads. That's exactly what you're going to learn about how to show. Because here's what we're told. I'm going to talk to a lot of financial trip. We have way off.

Most workers and journalists are falling for. We're going to go over why the real disposed of us. And almost nobody can see yet. We're going to talk about which everyday roles are first designed to be displaced. Why in which ones did not exist too much. And we had probably to be with a step of survival guide. You can start losing today before you finish one of the call.

All right. New here? This is Everyday A-A. This is our start year to six. I realized after more than 700 episodes of three years and talking about AI every single day, and they'd have a great answer for this. Which is, hey, Jordan, where do I start?

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So in our last series, we talk about the state of the AI race. Who will win in 2026? Open AI, Microsoft, off of Google or nonprofit. And today, let's get into it. This state, well, the AI labor shift. All right.

And then it'll happen in what happens for the job. So, reality, the biggest companies are slashing headcount. So we just saw reports that data is going to think in 2000. Last week, we saw Block cut forward. In Oracle is probably putting 30,000 jobs.

The February 26 job report showed 92,000 jobs lost. And it's ridiculous of a little bit. So if you are listening to this in March, maybe that's relevant. Maybe you're listening to this in late

2006 or early 2027.5 months. So you'll keep in mind these stats are going to change but now is that I don't know. reality is going to change. It needs to pursue. Big tech companies are starting to lay rehearsing on. And yes.

It's this issue, right? Because not all of these jobs, I think, are getting cut because of it. And we're going to talk about that later. But the AI does kind of become a scapegoat or a get out of free. get out in check free hard for a lot of these comments. There was actually the

Harvard Business Review study that showed 60% of hiring managers cited AI's AI for layoffs, yet only 2% actually replaced those roles or used AI to actually replace those roles. So it seems

like maybe in some instances, it's just kind of corrections from this over-hiring, that happened post-pricing rising interest rates and just margins. This is profit. It's still in smaller. I think one of those reasons is because of artificial intelligence, right?

It's allowed medium-sized to compete with big things. It's allowed SMBs to compete with small enterprises. So margins are getting thinner. You know, budgets are getting tighter. Not because of the interest rate. because also companies are trying to do more, with less, because they're making peoplely investments into artificial intelligence, right? No one's training their people, but, you know, bigger companies are spending, you know,

maybe at least seven, eight figures, you know, on AI investments annually. So they're like, well, we got it. Say, hey, these jobs are being cut by AI. And if they're a public company or are with, you know, careful. There's that's going to make them happy. Because, we talked about this

on our AI news show recent. They cut 40% of staff. They cited AI. They said, oh, essentially because of new technology and their staff rose

22%. No, you can go back. I've been saying this since 20, 23. Let me just get on the record right now. I've always said AI will ultimately take away more

traditional full-time roles than it will create. Yes, it will create millions of jobs. Nobody knows what it are, but I think hopefully it will, and I've said this all along, the more companies cut jobs, public companies especially, they're going to cut jobs, say it's because of AI, stock price goes up, shareholders are happy, All Street is happy, the economy is happy, and then the medium-sized businesses that are public are going to follow soon because they're like, well, look, what happens. This is kind of the trend that everyone's doing.

So Forrester did predict that half of the playoffs, though, will be quietly reversed. So it's not always the actual answer sometimes is eight, you know, stop. To either, like I said earlier, correct over high rate or when companies honestly beat a bump. And maybe they have too many layers of bureaucracy, right? like we heard the Amazon report, you know, when they cut there, multiple amounts of, you know, more than 10,000. They said it was too much bureaucracy, right? There's too much rent-tape and companies couldn't be lean and hatching how it actually built.

A.I. It allows small teams to do what used to take many, many large teams, right? You have too many pieces of mental management and, you know, too many beatings. A free conversation I had with the leader at Google was, you know, memos or sorry, demos for demos. Because it's getting way more costly to just have meetings about this. To just actually do that, right? Whereas traditionally, that's what you do. You can smart people in.

Things go slowly because, well, usually the investment feels time. Time, right, really changed that. So what is actually driving? Well, it's this shift operating expenditure to capital expenditure. If you don't speak business language, essentially companies are looking to, especially we big, Fortune 100 companies, the back seven companies, but in general, companies are looking to move away from operating expenses, which is salaries for humans, benefits, and moving it into AI infrastructure, right?

capital expenditures. So just five companies, Amazon, Alphabet, Meta, Microsoft, and Oracle have already announced that taxed brings up $700 billion to buy. So that's the data centers, cost of silicon, cooling plants, power generation, all that good stuff. Right, but they're not buying today's AI, right?

That's not $700 billion that they're spending on training their current employees. These are a lot of the same companies that are cutting the jobs taking that money, right? Literally just borrowing against, you know, human spending and paying into AI industry, right? Buildings, powers, GPUs, right? And because they're betting on tomorrow's,

not today's. Because if today's AI was these knees, right, we wouldn't be seeing all these huge companies spending hundreds of millions of dollars to build something else. They are, game over. Let's trade our people and no dominate. No. Everyone knows that the future compute is going to be a scarce resource.

In the access to it and the ability to scale it up is going to be a one of a company's biggest assets, right? Even more so than years past, right? The biggest asset was people, right? You had world-class experts. That was your company's quality. Well, in the future, if they just be able to scale, you know, billions or billions of world-class ages, whereas maybe your competitors can only deploy 5%, that form 1%.

That would be a real advantage. That's what we've seen in this shift from objects to capes, but that some of it really solved the jobs, right? Obviously, you know, there's jobs, you know, at these plants. you know, people, literally boots on the ground, instructing, and maintaining those kids.

But what about the jobs that come after? That's why this is, we did talk about this recently, so I'm not going to go too much to it. I'm a property's labor study, so 7.30. But just as an example, in traffic,

a great study, when we look at millions of anonymized chaps inside of their call on the chat. Then they essentially add up everything across 20,000 different tasks in these government jobs data set essentially. And they how, theoretically,

AI can do 94% of computer advanced as well as it's a but it was only it used 33% of the time. So that's actually the highest usage of observed use was 33%. So in most cases, you have this massive capability capability of saying like hey no yeah i can actually go do all these jobs that we're hiring all these people to do right all these codifiable tasks yet most companies are doing it right even if you look at you know sectors like management legal and business below 20% preserved usage despite 80%

capability right so it can 80% roles task associated with those sectors, management, legal, and business, yet the U.S. at large, at least according to fraud's real anonymous usage, it's only 50% of this time.

So most companies are overwhelmingly still treating AI like Chet Buy, and it's actually a workforce shinsker. You couldn't have made that claim probably nine months ago, but can So when does the real shift hit? When's the shift going to hit the point?

And who is going to get it hurt? Well, we'll dive into that in a second after a word from our firms. 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.

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That's S-E-C-T-I-O-N-A-I.com. All right. So here's when that shift might happen and the types of roles that might feel it. And here's the reality. It's already started. You maybe haven't seen it. But we've, I've talked about it multiple times on the show. I think it started mainly toward the end of 2025, but we started to see a little bit of it in

2024. That's essentially where AI is augmenting senior workers. Well, entry level hiring has quietly slowed, right? So we did talk about that in episode 730, where essentially if you were to say college graduate placement was the unemployment rate. that's 14%, which again, if that was the actual unemployment rate across the board, you know, the U.S. would be up in arms. But we've already seen, you know, companies instead of hiring juniors,

instead of hiring entry-level people, they're just augmenting, you know, senior workers with AI or middle-tier workers with AI and then just not hiring those people. And the same thing with the baby boomers and the silver tsunami happening, well, they're just not hiring to replace these people. But I think what we're going to start to see this year and next year is just the flattening. It's going to be the great flattening. I don't think it's going to be the great white color recession. But I think teams are going to restructure and that capability gap that I talked about is going to close fast because I think now the average non-AI techie, right, the people who haven't been listening to this show since 2023, they're finally starting to wake up

and realize, right? I actually had a friend, you know, text, text our group chat the other day. And she said, you know, just kind of a group of our friends. And she said, wow, like, Quad just did an entire presentation for me, right? I think now, you know, these people that maybe haven't been following AI for years are starting to be like, wait, AI can do this, like a job that I would normally hire a human for and would probably take a lot of time. Yes, it can. So I think in 2026, 2027, that's where we see this, the capability gap start to shrink. And also the corporate ladder start to, well, it's not going to be a ladder anymore. I don't think we're going to have, you know, that normal tier, right, where you have your entry level, you have

your middle management, and then you have your senior leadership. I just think the bottom rungs of the corporate ladder are going to get chopped off. And yeah, so bye-bye, middle management, I think as well in 2026 and 2027. And then in 2028 to 2030, I think that's where we're going to start to see the return of the $700 billion in growing, obviously, of infrastructure. Because I think that's when the autonomous workflows start to arrive en masse.

So one of the things is, if you're listening to this, well, you're ahead of the game. You know what's coming, right? And I'm going to end the show by leaving, you with some pieces of advice. But I do think it's one of those things. Probably by 2030, I think most roles are going to be orchestrating agents, right?

Which might sound weird, just like it probably would have sounded weird in, you know, 1999 if someone was like, hey, all roles are going to be using the internet. It's like, no, that makes no sense, right? But that's the reality, right? So many knowledge workers are just using the internet all day. And I think that's where we're going to be. I think probably by 2030, early 2030s, most knowledge working jobs are going to be agent orchestration. So let's talk a little bit about those types of roles.

So there's a recent study that came out from the Federal Reserve Bank of Dallas. And it talked about kind of codified knowledge versus tacit knowledge, which is a great way to kind of classify these different types of, well, knowledge and tasks, right? But essentially, codified knowledge is what new graduates really rely on, right? It's those easy kind of quote unquote grunt work. And that's how companies have historically trained juniors, right? Junior researchers, junior analysts, whatever. Is this kind of this learning track that companies have always gone through? You give the easy, the quote unquote easy work that can easily be verified, but it's just manual, right?

It's the manual grunt work. And it's those tasks, the summarization, the personalization, and the synthesization of knowledge, synthesizing knowledge, and personalize it through the company's tone and creating something of value. Right. So a lot of times that's your general research tasks, you know, competitors, market trends. You, you know, put it in spreadsheets, run some formulas. make a little presentation, right? Like, that's the junior work. That is the codified knowledge.

That's going to be gone, right? AI does that already much better. And companies are starting to realize that. They're saying we don't need to hire humans for that anymore. Right? But the tacit knowledge, that's obviously not where AI can touch yet, right? That's the nuanced required to make decisions that come along with being a subject matter expert. I think that's, you know, how you have to, you know, sometimes be able to

to understand a larger organization or a competitive field, right, is that kind of the savvy that you might need, not office politics per se, but sometimes office politics, right? That's what AI cannot, you know, do yet. But I think regardless, another shift that's happening and already happening is that younger generation, you know, we've seen that I already talked about 14 percent, you know, that the exposed field, so hiring for younger workers 22 to 25 in exposed fields, dropped roughly 14%. All right.

So what can you do about it today, right? Before you finish that morning coffee that maybe you're sitting on right now, know a couple things. Again, if you are not new to AI, you're in a great place. Even if you do feel uncomfortable about this labor shift that is going to to inevitably happen. If you have been putting AI in place, if you've been, you know, listening to the show following what we've been talking about for a long time, you're good, right? And it's actually pretty telling the Dallas Fed study showed that actually a lot of wages have risen, right, nationally

versus where they were at previously. Because if you are keeping your job, even though there's fewer jobs, those jobs are getting paid much more and not just the standard, you know, cost of living cost of living in inflation, its companies have now realized that they will have to pay a premium dollar, right? Because when you do have fewer employees, the stakes are higher. In those employees that you are going to be orchestrating agents, you really want to assure that you have the right people, right? Because that's a compounding factor that we haven't seen before. You know, usually both the risk and the reward lever was restricted to a single person and what that single person was capable of for better or for worse. But with AI and agentic AI, it is a compounding

factor. So if you have the right smart people, they can literally 5x, 10x, 20x over time. So that's why these roles are actually getting paid more. And workers with AI skills are earning on average 56% more than those without skills across similar industry. So it's kind of this sweet spot of domain expertise, which we talked about earlier, some of that tacit knowledge as well, plus AI fluency, that's the highest combination in any field.

Although we did talk about in episode 730, well, in some cases, those are also those that are most susceptible or most exposed to AI automation. So the new AI jobs, though, they're just not going to be for engineering. I think two years ago, that was kind of the thought, right? Oh, anything, you know, for the future growth of AI, if you want to work in there, you have to be CS. Yeah, got to be computer science.

You got to be a coder. You got to be an engineer. Not anymore. As an example, one in a recent IBM study show that one in four companies now have a chief AI officer. Although part of that is telling, I think, in both regards. Okay, that's impressive. That, you know, one and four, but why? not two and four. Why not three and four? But right now, I think we're seeing emerging roles include things like context engineer, agentic orchestrator, AI auditor, GEO, strategist, right? Showing up in chat, I guess, right? But the barrier has dropped in these roles, really, I think, reward domain expertise over technical know-how or software or coding. So I think a lot of people have been scared away by job growth because we look at how the, you know, jobs and how careers have progressed over the last couple of decades, because that's all we can look at.

I don't think that's how jobs are going to pan out because it's not going to be a one-to-one match. And it's not even going to be a, you know, a one to two to three. It's going to be a one to be. You know, it's, it's, it's going to be on a, on a playing field that isn't level. Right. So I think a lot of times when people are saying, hey, I do have AI skills that I've been putting to place. What do I do now? Well, you might want to start looking at roles that you might have thought were previously

technical roles. And they're probably not. All right. You, you have some of the greatest, you know, software engineers of all time, now admitting. that they don't even code anymore. These are people that have, you know, helped code the products that have changed the world. And they're saying, well, you know,

not to get into, you know, self-learning and all of those things, but the models are really building the models now. Right. And humans are just orchestrating agents. So that does really change, I think, what you are capable of. Because that barrier has dropped.

All right. So we're going to end up. this. This is your three-step survival guide for starting today because we don't know when the next round of jobs are going to be gone or when the new jobs that don't exist yet are going to start popping up. And I personally don't think it's going to be a nice handoff. I'm not saying, you know, there's going to be massive unemployment or anything like that. But I'm a realist when it comes to I corporate greed, I think, is going to cause a lot more harm in the long run than AI. Obviously, those two things, you know, walk hand in hand.

But I don't think it's going to be five million jobs gone this year, five million new jobs created two months later. It's not going to be like that. So here's what you have to do now. Number one, you have to audit your role. You have to start separating those daily tasks into what are those judgment-based tasks. versus those codifiable. Right. Not saying that you need to panic if, you know,

everything in your day to day is codifiable. But that does mean in theory that, you know, your job might change sooner than later. All right. So you first need to audit your role. You need to have a realistic, expect, a realistic look at your day to day and say,

how automatable is my job right now with what we know of today's models. Number two, document your reasoning, right? Write down why you made hard decisions and what you weighed when going through those decisions. I've talked about this. I think companies and departments, this is kind of separate, right? I'm kind of leaving you with more from a personal career employability standpoint,

but I've also been very bullish on companies doing this. You need to document your literal company reasoning. right because that is some of that you know knowledge at least right now your reasoning your subject matter expertise your domain experience sometimes you don't always practice those things because they are innate and they don't always you know show up in that spreadsheet they don't always show up in that deliverable in that artifact a lot of times it is the filling in the filling in the gaps, you really have to document that. And then last but not least, I think you need to master one AI platform deeply

until it literally just becomes an extension of your own expertise. And I think that's difficult. I think that's difficult for people because you see all these new advancements in AI, right? Oh, first we had, you know, reasoning models and then we had plugins and now we have skills and now we have, you know, all these, you know, IDEs, we have all these vibe coding tools. We have all these, you know, agentic orchestration loops. You know, it's like every day there's a new flavor. And I think sometimes people think or assume, well, the way I make myself more employable in the future is I have to know a lot of everything.

You will fail. Let me say that again, if you are just trying to keep your skill set up to par with, The skill set of the week, you will fail. You can't do it. You can't, right? This is what I try to do every day. You can't do it. Right?

I can't do it. I can't go out and, you know, do all these things I talk about every single day. Two years ago I could. Today, I can't. No single human can anymore. So you have to double down on one platform, right? Whatever it may be, right? I don't know.

Developing, you know, skills. for accounting, right? I don't know. And when I say skills, I mean, you know, obviously, agetic skills and writing them, right? But you have to overlap that

with your current domain of expertise. And I think if you go through those three steps, it's not going to, you know, be a save all safety met if there is a white-collar job recession, right? The AI job apocalypse. But it is your survival.

guide that you can start today to prepare yourself to get yourself going down the right direction now before it's too late. All right. I hope this was helpful as we talked about. Start here series volume 13, the AI labor shift when it'll happen and what it means for jobs. All right. There's also there's guys, there's way too much I couldn't get to in today's show. I'm being honest. Put together a ton of cool resources. Some great things in notebook L.M. all of that. So if you do want access to this couple, you know, cinematic videos, some slide shows, all that. If you want access to all of those additional resources, if you're listening to this episode and you're like, I want to dig deeper. I want to double down. There was so much I couldn't get to.

I didn't want this to turn into one of those, you know, accidental 60 minute shows. So if you go repost this on LinkedIn, I will send you all of that over. So if you're looking for the LinkedIn show, right? If you're listening on the podcast, you probably are. Just look in the show. You're show notes. It's always in the show notes. It says like, you know, go join the conversation on LinkedIn. That is the LinkedIn link and then just go repost that. And I will send you all of those resources over. And then make sure you go to start here series.com. That's going to give you free access to our private inner circle community. And you can go catch up on all of the episodes in the Start Here series. So thank you for tuning in. Hope to see you back tomorrow and every day for more

everyday AI. Thanks y'all. And that's 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 Your EverydayAI.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.