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In the past two weeks alone, we've seen reports of more than forty thousand plus jobs that will soon be eliminated.
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We've read all those reports for years and dogs.
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And well, it's like a new era.
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We're gonna be digging into this topic.
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Today's mission of everyday IR.
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Welcome.
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Let's just get straight to it.
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Here is the big picture of what we're gonna be talking about today.
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And no one knows what the future of work is.
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I mean, we've got housing off.
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You know, these CEOs of these companies are all citing because of AI in the treatment.
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Yes, AI companies don't get infrastructure.
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And we know that AI altered it.
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So that puts us at this prospect.
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That's exactly what you're gonna learn about on today's show.
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Here's what we're told.
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Most workers and journalists are all the work.
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We're gonna go over why the real discounts are threatening conduct, and almost nobody can see that.
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We're gonna talk about which everyday roles are first designed to be displayed by which ones did not even exist.
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And we have a nominally step survival, you can start missing it to 10 or new here?
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This is everyday AI.
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This is our start here to this.
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I realized after more than 700 episodes in three years of talking about AI every single day.
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I didn't have a great answer for the question, which is hey Jordan, where do I start?
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I'm new to the podcast.
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And I'm like, I don't know.
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Well, now you start with the Start Here Series.
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This is the essential podcast series to learn AI basics and to double down on work knowledge.
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So make sure if you haven't already, go to starthearseries.com and it's going to give you free access to our inner social candidates.
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It's private.
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You can't even probably starthearseries.com.
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And that will also put you straight into our start series, start here series space where you can go back and read and listen to every single episode in the series.
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So in our last series, we talked about the state of the AI race.
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Who will win in 2026?
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OpenAI, Microsoft, Google, or in Tropic.
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And then today, let's get into it.
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This state, well, the AI labor shift.
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All right, when it'll happen and what happens in our jobs.
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So in reality, the biggest companies on earth are slashing headcamps, right?
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So we just saw reports that Nevada is going to be getting 2,000 jobs.
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In Oracle is currently uh cutting 30,000 jobs.
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So if you are listening to this in our relevant, if you're listening to this in the late 2026 or early 2020s, I don't know.
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So you know, keep in mind these stats are going to change, but I don't think the reality is going to change.
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Big tech companies are starting to play rehearsal.
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Right?
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Um because not all of these jobs, I think, are getting cut because of that.
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And we're going to talk about that later.
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But AI does kind of become a scapegoat scapegoat or a get out of free uh get out of chip free card for a lot of these companies.
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There was actually a uh Harvard Business Review study that showed 60% of hiring managers cited AI's uh AI for layoffs, yet only 2% actually replaced those roles or used AI to actually replace those roles.
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So it seems like maybe in some uh instances it's just kind of corrections from this overhiring that happened post-pandemic.
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Uh rising interest rates and just margins.
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Thinner, I think one of those reasons is because of artificial intelligence, right?
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Uh it's allowed medium-sized companies to compete with big companies, it's allowed SMBs to compete with small enterprises.
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So uh margins are getting thinner, uh, you know, budgets are getting tighter not because of the interest rate, well, because also companies are trying to do more, with less than cause they got, right?
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They're making uh investments into artificial intelligence, right?
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No one's training their people, but you know, bigger companies are spending you know, maybe at least uh seven, eight figures uh you know on AI investments annually.
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So they're like, okay, well, we gotta say, hey, these jobs are being cut by AI.
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And if they're a public company or a couple with you know shareholders, that's probably gonna make them happy because block we talked about this on our AI news show recently.
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They cut 40% of staff, they cited AI and said, oh, it's essentially because of new technology and their stock rose 22%.
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No, you can go back.
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I've been saying this since 2023, right?
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Um let me just get on the record right now.
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I've always said AI will ultimately take away more traditional full-time goals than it will create.
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Yes, it will create millions of jobs that no one knows what they are, but I think ultimately it will, and I've said this all along the more companies cut jobs, public companies especially, they're gonna cut jobs, say it's because of AI, stock price goes up, shareholders are happy, Wall Street is happy, the economy is happy, and then the medium-sized businesses that aren't public are going to follow suit because they're like, well, look what happens.
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This is kind of the trend that everyone's doing.
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Uh so Forrester did predict that half of the layoffs, though, will be quietly uh reversed.
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So it's not always the actual answer.
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Sometimes it's eight, you know, stop having to either, like I said earlier, correct over hiring, or companies honestly need a ball.
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And maybe they have too many layers of bureaucracy, right?
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Like uh we heard the Amazon report, um, you know, when they cut their multiple out of uh, you know, more than 10,000 said it was too too much bureaucracy, right?
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There's too much red tape, and companies couldn't be lean and agile and actually built AI allows small teams to do what used to take many, many large teams, right?
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When you have too many pieces of metal management and you know, too many meetings, you know.
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Sorry, demos for memos because it's getting way more costly to just have meetings about to just actually do that, right?
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Whereas traditionally that's what you do.
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You smart people in, things go slowly because, well, usually the investment builds at time, I guess really change that.
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So what is actually driving these?
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Well, is this shift from operating expenditure to capital expenditure, right?
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You don't speak business language essentially, companies are looking to, especially the big right, Fortune 100 companies, the max seven companies mainly, right?
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But in general, companies are looking to move away from operating expenses, which was salaries for humans, benefits, moving it into AI infrastructure, right?
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Capital uh expenditures.
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So just five companies Amazon, Alphabet, Meta, Microsoft, and Oracle, uh, have already announced access savings of$700 billion to month.
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So that's the data centers, the cost of silicon, cooling plants, power generation, all that good stuff, right?
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But they're not buying today's AI, right?
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That's not$700 billion that they're spending on training their current employees, right?
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These are a lot of the same companies that are cutting the jobs and taking that money, right?
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Literally just borrowing against you know human spending and putting it into the AI industry, right?
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Buildings, towers, GPUs, right?
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And because they're butting on tomorrow's AI, not today's AI.
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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.
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We'll just say, all right, game's over.
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Let's trade our people and don't dominate.
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No, I think everyone knows that the uh future compute is going to be a scarce resource.
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Um, and the access to it and the ability to scale it up uh is going to be a one of the company's biggest assets, right?
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Even more so than in years past, right?
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The biggest asset was people, right?
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You had world-class experts, that was your company's calling hub.
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Well, in the future, it may just be able to scale, you know, millions or billions of world-class agents where maybe your competitors can only uh deploy five percent of that, or one percent that would be a real advantage, and that's what we're seeing uh in this shift from obex to capex.
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But that's really solved the jobs, right?
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Obviously, you know, there's jobs uh you know at these plants, you know, people boots literal boots on the ground constructing, maintaining books.
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But what about the jobs that come to that's why uh this we did talk about this recently, so I'm not gonna go too much into it, but a little over this labor study in episode 730.
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But just as an example in traffic great study, uh when we look at millions of anonymized uh inside of their chat, then they essentially map everything across 20,000 different tasks um in these uh government jobs uh data set, essentially.
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And they now theoretically, AI can do 94% of computer enhancements.
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That's just what it's it, but it was only used 33% of the time.
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Um that's actually the highest usage of observance was 33%.
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So in most cases, you have this capability gap of saying, like, hey, no, hey, I can actually go do all these jobs that we're hiring all these people to do, uh, right?
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All these codifiable tasks.
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Yeah, most companies are doing it, uh right.
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Even if you look at gap, uh, you know, sectors like management, legal, and business, below 20% observed uh usage despite 80% capability, right?
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So it's not 80% of controls and tasks associated with those sectors, management, legal, and business, yet the US at large, um at least rates flawed real anonymous usage, it's only increased by 20% of the time.
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So most companies are overwhelmingly still treated AI like J and it's actually a workforce shifter, right?
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You couldn't have made that claim uh, you know, probably nine months ago, but I can't when is the real shift gonna hit?
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When's shift gonna hit the and who is gonna get hit first?
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Well, we'll dive into that in in a second after a word from our partners.
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00:14:51.200 --> 00:14:57.360
All right, so here's when that shift might happen and the types of roles that might feel it.
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And here's the reality: it's already started.
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You maybe haven't seen it, but uh we've I've talked about it multiple times on the show.
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I think it started mainly toward the end of 2025, but we started to see a little bit of it in 2024.
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That's essentially where AI is augmenting senior workers while entry-level hiring has quietly slowed, right?
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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%.
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Which again, if that was the actual unemployment rate across the board, you know, the the US would be up in arms.
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Uh, 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 middle uh tier workers with AI and then just not hiring those people.
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And the same thing with the baby boomers and the silver tsunami happening.
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Well, they're just not hiring to replace these people.
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But I think what we're gonna start to see this year and next year is just the flattening.
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It's gonna be the great flattening.
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I don't think it's gonna be the great white-collar recession, but I think teams are gonna restructure and that capability gap that I talked about is going to close fast because I think now the average non-AI techie, right?
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The people who haven't been listening to the show since 2023, they're finally starting to wake up and realize, right?
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I actually had a a friend, you know, text text our group chat uh the other day, and she said, um, you know, just kind of a group of our friends, and she said, Wow, like Claude just did an entire presentation for me, right?
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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.
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Yes, it can.
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So I think in 2026, 2027, that's where we see this uh the capability gap start to shrink.
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And also the corporate ladder start to, well, it's not gonna be a ladder anymore.
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I don't think we're gonna have you know that normal tier, right?
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Where you have your entry level, you have your middle management, and then you have your senior leadership.
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I just think the bottom rungs of the corporate ladder are gonna get chopped off.
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Um, and yeah, so bye-bye middle management, I think, as well, um, in 2026 and 2027.
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And then in 2028 to 2030, I think that's where we're gonna start to see the return of the$700 billion in growing, obviously, of infrastructure.
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Uh, because I think that's when the autonomous workflows start to arrive in mask.
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So, one of the things is if you're listening to this, well, you're ahead of the game.
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You know what's coming, right?
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And I'm gonna uh end the show by leaving you with some uh some pieces of advice.
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Um, but I do think it's one of those things, probably by 2030, there's most, I think most roles are gonna be orchestrating agents, right?
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Which might sound weird, just like it probably would have sounded weird in you know 1999 if someone was like, hey, all roles are gonna be using the internet.
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It's like, no, that makes no sense, right?
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But that's that's the reality, right?
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So many knowledge workers uh are just using the internet all day.
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Um, and I think that's where we're gonna be.
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I think probably by 2030, early 2030s, uh, most knowledge working jobs are gonna be agent orchestration.
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So let's talk a little bit about those types of roles.
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So there's a recent study that came out from the Federal Reserve uh Federal Reserve Bank of Dallas, and it talked about kind of uh codified knowledge versus tacit knowledge, uh, which is a great way to kind of classify these different types of well, knowledge and tasks, right?
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But essentially, codified knowledge is what new graduates really rely on, right?
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It's those easy kind of quote unquote grunt work, and that's how companies have historically trained juniors, right?
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Junior researchers, junior analysts, whatever.
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It's this kind of this learning track that companies have always gone through.
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You give the easy, the quote unquote easy work that can easily be verified, but it's just manual, right?
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It's the manual grunt work, and it's those those tasks, uh, the summarization, the personalization, the synthesis of of knowledge, synthesizing knowledge um and personalizing through uh through the company's tone and creating something of value, right?
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So a lot of times that's your general research task, you know, competitors, uh, market trends, you you know, put in spreadsheets, run some formulas, make a little presentation, right?
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Like that's junior work.
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That is the codified knowledge.
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That's gonna be gone, right?
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AI does that already much better.
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Uh, and companies are starting to realize that.
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And they're saying we don't need to hire humans for that anymore.
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Uh, right.
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But the tacit knowledge, that's obviously not where AI can touch yet, right?
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That's the nuanced uh required to make decisions that come along with being a subject matter expert.
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I think that's you know how you have to, you know, sometimes be able to understand a larger organization or a competitive field, uh, right, is that kind of uh the the savvy uh that you might need, not office politics per se, but sometimes office politics, right?
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That's what AI cannot uh you know do yet.
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But I think regardless, another shift that's happening and already happening is that younger generation uh, you know, that we've seen that uh I already talked about 14 uh you know that the exposed um field, so hiring for younger workers 22 to 25 in exposed fields dropped roughly 14%.
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All right, so what can you do about it today, right?
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Before you finish that morning coffee that maybe you're sitting on right now, know a couple things.
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Again, if you if you are not new to AI, you're in a great place.
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Even if you do feel uncomfortable about this labor shift that is going 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?
00:21:36.960 --> 00:21:38.400
And it's actually pretty telling.
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Uh, the Dallas Fed study showed that actually a lot of wages have risen uh right uh nationally versus where they were at previously.
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Uh, because if you are keeping your job, even though there's fewer jobs, those jobs are getting paid much more.
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And not just the standard, you know, cost of living, cost of living in inflation.
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It's companies have Now um realize that they will have to pay a premium dollar, right?
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Because when you do have fewer employees, the stakes are higher.
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And those employees that you are going to be orchestrating agents, you really want to show that you have the right people, right?
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Because that's a compounding factor that we haven't seen before.
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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, right?
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But with AI and agentic AI, it is a compounding factor.
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So if you have the right smart people, they can literally 5x, 10x, 20x over time.
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So that's why you know these roles are actually getting paid more.
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And workers with AI skills are earning on average 56% more than those without skills across similar industries.
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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.
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That's the highest combination in any field.
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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.
00:23:17.440 --> 00:23:23.680
So the new AI jobs, though, they're just not going to be for engineers.
00:23:23.839 --> 00:23:27.599
I think two years ago, uh, that was kind of the uh the thought, right?
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Oh, anything uh, you know, for the future growth of AI, if you want to work in there, you have to be CS.
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You gotta be computer science, you gotta be a coder, you gotta be an engineer.
00:23:36.720 --> 00:23:37.519
Not anymore.
00:23:37.680 --> 00:23:43.920
Uh, as an example, one in uh a recent IBM study showed that one in four companies now have a chief AI officer.
00:23:44.160 --> 00:23:49.440
Although part of that is telling, I think, in both regards.
00:23:49.599 --> 00:23:50.960
Okay, that's impressive.
00:23:51.039 --> 00:23:54.720
That, you know, one in four, but why not two and four?
00:23:54.880 --> 00:23:56.160
Why not three and four?
00:23:56.240 --> 00:24:06.400
Uh, but right now I think we're seeing emerging roles include things like context engineer, agentic orchestrator, AI auditor, GEO, strategist, right?
00:24:06.559 --> 00:24:10.079
Showing up um in uh in chat, I guess, right?
00:24:10.240 --> 00:24:19.759
Um, but the barrier has dropped and these roles really, I think, reward domain expertise uh over technical know-how or software or coding.
00:24:19.920 --> 00:24:38.720
So I think a lot of people um have been scared away by job growth because we look at how the uh you know jobs and how careers have progressed over the last couple of decades, because that's all we can look at.
00:24:38.880 --> 00:24:44.319
And I don't think that's how jobs are going to pan out because it's not gonna be a one-to-one match.
00:24:44.400 --> 00:24:50.480
And it's not even gonna be a you know a one to two to three, it's gonna be a one to B.
00:24:50.799 --> 00:24:55.839
You know, it's it's it's gonna be on a on a playing field that isn't level, right?
00:24:55.920 --> 00:25:01.759
So I think a lot of times when people are saying, hey, I do have AI skills that I've been putting to place.
00:25:01.920 --> 00:25:02.880
What do I do now?
00:25:03.039 --> 00:25:10.240
Well, you might want to start looking at roles that you might have thought were previously technical roles, and they're probably not.
00:25:10.480 --> 00:25:10.960
All right.
00:25:11.039 --> 00:25:20.319
You you have some of the greatest, you know, software engineers of all time now admitting that they don't even code anymore.
00:25:20.559 --> 00:25:25.119
These are people that have, you know, helped code the products that have changed the world.
00:25:25.200 --> 00:25:35.200
And they're saying, well, you know, not to get into you know uh self-learning and and all of those things, but the models are really building the models now, right?
00:25:35.599 --> 00:25:38.240
And humans are just orchestrating agents.
00:25:38.319 --> 00:25:46.400
So that does really change, I think, what you are capable of, because that barrier has dropped.
00:25:46.559 --> 00:25:47.920
All right, so we're gonna end on this.
00:25:48.000 --> 00:26:02.559
This is your three-step survival guide for starting today, because we don't know when the next round of jobs are gonna be gone or when the new jobs that don't exist yet are gonna start popping up.
00:26:02.720 --> 00:26:07.599
And I personally don't think it's gonna be a nice, uh, a nice handoff.
00:26:07.680 --> 00:26:15.119
I'm not saying, you know, there's gonna be massive unemployment or anything like that, but I'm a realist when it comes to AI.
00:26:15.759 --> 00:26:21.279
Corporate greed, I think, is gonna cause a lot more harm in the long run than AI.
00:26:21.519 --> 00:26:24.960
Obviously, those two things uh, you know, walk hand in hand.
00:26:25.839 --> 00:26:32.880
But I don't think it's gonna be five million jobs gone this year, five million new jobs created two months later.
00:26:32.960 --> 00:26:33.920
It's not gonna be like that.
00:26:34.079 --> 00:26:35.359
So here's what you have to do now.
00:26:35.519 --> 00:26:37.599
Number one, you have to audit your role.
00:26:37.759 --> 00:26:48.400
You have to start separating those daily tasks into what are those judgment-based tasks uh versus those codifiable tasks.
00:26:49.680 --> 00:26:50.160
Right?
00:26:50.400 --> 00:27:04.480
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.
00:27:04.720 --> 00:27:06.880
All right, so you first need to audit your role.
00:27:06.960 --> 00:27:16.880
You need to have a realistic expect uh or 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.
00:27:17.359 --> 00:27:20.960
Number two, document your reasoning, right?
00:27:21.519 --> 00:27:27.599
Write down why you made hard decisions and what you weighed when going through those decisions.
00:27:27.759 --> 00:27:31.519
I've talked about this, I think companies and departments, this is kind of separate, right?
00:27:31.680 --> 00:27:41.200
I'm kind of leaving you with uh more from a personal career employability uh standpoint, but I've also been very bullish on companies doing this.
00:27:41.359 --> 00:27:54.559
You need to document your literal company reasoning, uh right, because that is some of that uh, you know, knowledge, at least right now, your reasoning, your subject matter expertise, uh, your domain experience.
00:27:54.960 --> 00:28:02.640
Sometimes you don't always practice those things because they are innate and they don't always, you know, show up in that spreadsheet.
00:28:02.720 --> 00:28:06.160
They don't always show up uh in that deliverable, in that artifact.
00:28:06.319 --> 00:28:10.000
A lot of times uh it is the filling in the uh filling in the gaps.
00:28:10.160 --> 00:28:11.920
You really have to document that.
00:28:12.319 --> 00:28:21.920
And then last but not least, uh, I think you need to master one AI platform deeply until it literally just becomes an extension of your own expertise.
00:28:22.079 --> 00:28:24.000
And I think that's difficult.
00:28:24.240 --> 00:28:29.519
I think that's difficult for people because you see all these new advancements in AI, right?
00:28:29.680 --> 00:28:47.599
Oh, the, you know, first we had uh, 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.
00:28:47.680 --> 00:28:57.119
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.
00:28:57.680 --> 00:28:58.880
You will fail.
00:29:00.079 --> 00:29:01.279
Let me say that again.
00:29:01.519 --> 00:29:07.839
If you are just trying to keep your skill set up to par with the skill set of the week, you will fail.
00:29:08.000 --> 00:29:08.720
You can't do it.
00:29:10.160 --> 00:29:11.359
You can't, right?
00:29:11.519 --> 00:29:15.920
This is what I try to do every day, but you can't do it, right?
00:29:16.079 --> 00:29:16.960
I can't do it.
00:29:17.440 --> 00:29:21.359
I can't go out and you know, do all these things I talk about every single day.
00:29:21.599 --> 00:29:23.359
Two years ago I could, today I can't.
00:29:23.519 --> 00:29:25.359
No single human can anymore.
00:29:25.759 --> 00:29:30.319
So you have to double down on one platform, right?
00:29:30.480 --> 00:29:33.039
What it, whatever it may be, uh right?
00:29:33.200 --> 00:29:37.519
I don't know, developing, you know, skills for accounting, uh, right?
00:29:37.680 --> 00:29:38.319
I don't know.
00:29:38.480 --> 00:29:43.839
And when I say skills, I mean, you know, obviously agetic skills and writing them, right?
00:29:44.079 --> 00:29:49.359
But you have to overlap that with your current domain of expertise.
00:29:49.839 --> 00:30:01.680
And I think if you go through those three steps, it's not going to, you know, be a save all safety meta if there is a white-collar job recession, right?
00:30:01.759 --> 00:30:04.079
The the AI job apocalypse.
00:30:04.240 --> 00:30:13.759
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.
00:30:14.079 --> 00:30:14.640
All right.
00:30:14.799 --> 00:30:17.519
I hope this was helpful as we talked about.
00:30:17.680 --> 00:30:24.799
Uh start here series, volume 13, the AI labor shift, when it'll happen, and what it means for jobs.
00:30:24.960 --> 00:30:30.559
All right, there's also there's guys, there's way too much I couldn't get to in today's show, if I'm being honest.
00:30:30.799 --> 00:30:36.240
Put together a ton of cool resources, uh, some great things in Nobook LM, all of that.
00:30:36.400 --> 00:30:42.480
So if you do want access to this couple, you know, cinematic videos, some slideshows, uh, all that.
00:30:42.640 --> 00:30:49.440
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.
00:30:49.599 --> 00:30:50.960
There was so much I couldn't get to.
00:30:51.039 --> 00:30:55.680
I didn't want this to turn into one of those, you know, accidental 60 minute shows.
00:30:55.759 --> 00:31:00.079
So if you go repost this on LinkedIn, I will send you all of that over.
00:31:00.240 --> 00:31:02.960
So if you're looking for the LinkedIn show, right?
00:31:03.039 --> 00:31:04.799
If you're listening on the podcast, you probably are.
00:31:04.960 --> 00:31:06.000
Just look in the show notes.
00:31:06.079 --> 00:31:07.359
It's always in the show notes.
00:31:07.440 --> 00:31:10.640
It says, like, you know, go join the conversation on LinkedIn.
00:31:10.880 --> 00:31:12.480
That is the LinkedIn link.
00:31:12.559 --> 00:31:14.160
And then just go repost that.
00:31:14.319 --> 00:31:16.559
And I will send you all of those resources over.
00:31:16.640 --> 00:31:19.039
And then make sure you go to starthireseries.com.
00:31:19.200 --> 00:31:27.920
That's gonna 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.
00:31:28.000 --> 00:31:28.960
So thank you for tuning in.
00:31:29.039 --> 00:31:31.920
Hope to see you back tomorrow and every day for more everyday AI.
00:31:32.000 --> 00:31:32.799
Thanks, y'all.
00:00:01.040 --> 00:00:08.880
In the past two weeks alone, we've seen reports of more than forty thousand plus jobs that will soon be eliminated.
00:00:11.199 --> 00:00:18.320
We've read all those reports for years and dogs.
00:00:34.799 --> 00:00:39.600
And well, it's like a new era.
00:00:58.719 --> 00:01:00.640
We're gonna be digging into this topic.
00:01:02.320 --> 00:01:05.760
Today's mission of everyday IR.
00:01:08.239 --> 00:01:08.719
Welcome.
00:01:11.200 --> 00:01:12.640
Let's just get straight to it.
00:01:12.879 --> 00:01:16.400
Here is the big picture of what we're gonna be talking about today.
00:01:20.000 --> 00:01:22.480
And no one knows what the future of work is.
00:01:23.760 --> 00:01:29.760
I mean, we've got housing off.
00:01:36.079 --> 00:01:40.959
You know, these CEOs of these companies are all citing because of AI in the treatment.
00:01:42.959 --> 00:01:52.159
Yes, AI companies don't get infrastructure.
00:01:53.439 --> 00:01:56.719
And we know that AI altered it.
00:02:04.159 --> 00:02:06.640
So that puts us at this prospect.
00:02:07.040 --> 00:02:09.919
That's exactly what you're gonna learn about on today's show.
00:02:10.560 --> 00:02:11.680
Here's what we're told.
00:02:15.599 --> 00:02:18.319
Most workers and journalists are all the work.
00:02:18.719 --> 00:02:24.960
We're gonna go over why the real discounts are threatening conduct, and almost nobody can see that.
00:02:25.680 --> 00:02:34.479
We're gonna talk about which everyday roles are first designed to be displayed by which ones did not even exist.
00:02:35.599 --> 00:02:46.240
And we have a nominally step survival, you can start missing it to 10 or new here?
00:02:46.560 --> 00:02:47.599
This is everyday AI.
00:02:47.919 --> 00:02:49.280
This is our start here to this.
00:02:49.599 --> 00:02:55.759
I realized after more than 700 episodes in three years of talking about AI every single day.
00:02:55.919 --> 00:03:01.199
I didn't have a great answer for the question, which is hey Jordan, where do I start?
00:03:01.360 --> 00:03:02.479
I'm new to the podcast.
00:03:02.560 --> 00:03:03.919
And I'm like, I don't know.
00:03:04.080 --> 00:03:07.039
Well, now you start with the Start Here Series.
00:03:07.120 --> 00:03:12.719
This is the essential podcast series to learn AI basics and to double down on work knowledge.
00:03:12.960 --> 00:03:20.319
So make sure if you haven't already, go to starthearseries.com and it's going to give you free access to our inner social candidates.
00:03:20.400 --> 00:03:21.039
It's private.
00:03:21.280 --> 00:03:24.879
You can't even probably starthearseries.com.
00:03:24.960 --> 00:03:35.520
And that will also put you straight into our start series, start here series space where you can go back and read and listen to every single episode in the series.
00:03:35.680 --> 00:03:40.879
So in our last series, we talked about the state of the AI race.
00:03:41.039 --> 00:03:42.800
Who will win in 2026?
00:03:43.039 --> 00:03:46.000
OpenAI, Microsoft, Google, or in Tropic.
00:03:46.159 --> 00:03:47.680
And then today, let's get into it.
00:03:47.919 --> 00:03:51.199
This state, well, the AI labor shift.
00:03:51.520 --> 00:03:54.800
All right, when it'll happen and what happens in our jobs.
00:03:55.840 --> 00:04:02.560
So in reality, the biggest companies on earth are slashing headcamps, right?
00:04:03.280 --> 00:04:10.960
So we just saw reports that Nevada is going to be getting 2,000 jobs.
00:04:15.439 --> 00:04:20.079
In Oracle is currently uh cutting 30,000 jobs.
00:04:29.600 --> 00:04:38.879
So if you are listening to this in our relevant, if you're listening to this in the late 2026 or early 2020s, I don't know.
00:04:39.279 --> 00:04:45.279
So you know, keep in mind these stats are going to change, but I don't think the reality is going to change.
00:04:46.480 --> 00:04:49.680
Big tech companies are starting to play rehearsal.
00:04:55.839 --> 00:04:56.399
Right?
00:04:56.720 --> 00:05:04.639
Um because not all of these jobs, I think, are getting cut because of that.
00:05:05.439 --> 00:05:07.199
And we're going to talk about that later.
00:05:07.360 --> 00:05:17.279
But AI does kind of become a scapegoat scapegoat or a get out of free uh get out of chip free card for a lot of these companies.
00:05:17.920 --> 00:05:35.120
There was actually a uh Harvard Business Review study that showed 60% of hiring managers cited AI's uh AI for layoffs, yet only 2% actually replaced those roles or used AI to actually replace those roles.
00:05:35.279 --> 00:05:43.680
So it seems like maybe in some uh instances it's just kind of corrections from this overhiring that happened post-pandemic.
00:05:44.079 --> 00:05:47.680
Uh rising interest rates and just margins.
00:05:50.639 --> 00:05:55.920
Thinner, I think one of those reasons is because of artificial intelligence, right?
00:05:56.240 --> 00:06:04.000
Uh it's allowed medium-sized companies to compete with big companies, it's allowed SMBs to compete with small enterprises.
00:06:04.160 --> 00:06:16.079
So uh margins are getting thinner, uh, you know, budgets are getting tighter not because of the interest rate, well, because also companies are trying to do more, with less than cause they got, right?
00:06:16.639 --> 00:06:22.399
They're making uh investments into artificial intelligence, right?
00:06:22.959 --> 00:06:32.399
No one's training their people, but you know, bigger companies are spending you know, maybe at least uh seven, eight figures uh you know on AI investments annually.
00:06:32.480 --> 00:06:38.480
So they're like, okay, well, we gotta say, hey, these jobs are being cut by AI.
00:06:38.560 --> 00:06:49.439
And if they're a public company or a couple with you know shareholders, that's probably gonna make them happy because block we talked about this on our AI news show recently.
00:06:49.600 --> 00:06:58.879
They cut 40% of staff, they cited AI and said, oh, it's essentially because of new technology and their stock rose 22%.
00:06:59.439 --> 00:07:00.879
No, you can go back.
00:07:01.040 --> 00:07:04.079
I've been saying this since 2023, right?
00:07:04.240 --> 00:07:06.319
Um let me just get on the record right now.
00:07:06.480 --> 00:07:13.920
I've always said AI will ultimately take away more traditional full-time goals than it will create.
00:07:14.000 --> 00:07:39.040
Yes, it will create millions of jobs that no one knows what they are, but I think ultimately it will, and I've said this all along the more companies cut jobs, public companies especially, they're gonna cut jobs, say it's because of AI, stock price goes up, shareholders are happy, Wall Street is happy, the economy is happy, and then the medium-sized businesses that aren't public are going to follow suit because they're like, well, look what happens.
00:07:39.120 --> 00:07:41.600
This is kind of the trend that everyone's doing.
00:07:41.759 --> 00:07:48.720
Uh so Forrester did predict that half of the layoffs, though, will be quietly uh reversed.
00:07:48.879 --> 00:07:50.959
So it's not always the actual answer.
00:07:51.040 --> 00:08:01.439
Sometimes it's eight, you know, stop having to either, like I said earlier, correct over hiring, or companies honestly need a ball.
00:08:01.680 --> 00:08:05.439
And maybe they have too many layers of bureaucracy, right?
00:08:05.519 --> 00:08:16.240
Like uh we heard the Amazon report, um, you know, when they cut their multiple out of uh, you know, more than 10,000 said it was too too much bureaucracy, right?
00:08:16.319 --> 00:08:28.240
There's too much red tape, and companies couldn't be lean and agile and actually built AI allows small teams to do what used to take many, many large teams, right?
00:08:28.480 --> 00:08:35.279
When you have too many pieces of metal management and you know, too many meetings, you know.
00:08:39.200 --> 00:08:49.519
Sorry, demos for memos because it's getting way more costly to just have meetings about to just actually do that, right?
00:08:49.679 --> 00:08:51.279
Whereas traditionally that's what you do.
00:08:51.360 --> 00:08:59.679
You smart people in, things go slowly because, well, usually the investment builds at time, I guess really change that.
00:09:01.120 --> 00:09:03.759
So what is actually driving these?
00:09:04.720 --> 00:09:10.879
Well, is this shift from operating expenditure to capital expenditure, right?
00:09:11.279 --> 00:09:21.600
You don't speak business language essentially, companies are looking to, especially the big right, Fortune 100 companies, the max seven companies mainly, right?
00:09:21.679 --> 00:09:32.000
But in general, companies are looking to move away from operating expenses, which was salaries for humans, benefits, moving it into AI infrastructure, right?
00:09:32.159 --> 00:09:34.080
Capital uh expenditures.
00:09:34.320 --> 00:09:44.000
So just five companies Amazon, Alphabet, Meta, Microsoft, and Oracle, uh, have already announced access savings of$700 billion to month.
00:09:44.240 --> 00:09:50.159
So that's the data centers, the cost of silicon, cooling plants, power generation, all that good stuff, right?
00:09:50.320 --> 00:09:52.960
But they're not buying today's AI, right?
00:09:53.120 --> 00:09:58.480
That's not$700 billion that they're spending on training their current employees, right?
00:09:58.720 --> 00:10:03.519
These are a lot of the same companies that are cutting the jobs and taking that money, right?
00:10:03.679 --> 00:10:10.080
Literally just borrowing against you know human spending and putting it into the AI industry, right?
00:10:10.399 --> 00:10:14.159
Buildings, towers, GPUs, right?
00:10:14.639 --> 00:10:19.600
And because they're butting on tomorrow's AI, not today's AI.
00:10:20.159 --> 00:10:29.679
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.
00:10:29.840 --> 00:10:31.519
We'll just say, all right, game's over.
00:10:31.600 --> 00:10:33.519
Let's trade our people and don't dominate.
00:10:33.679 --> 00:10:40.000
No, I think everyone knows that the uh future compute is going to be a scarce resource.
00:10:40.080 --> 00:10:47.840
Um, and the access to it and the ability to scale it up uh is going to be a one of the company's biggest assets, right?
00:10:47.919 --> 00:10:50.240
Even more so than in years past, right?
00:10:50.320 --> 00:10:52.799
The biggest asset was people, right?
00:10:53.039 --> 00:10:58.240
You had world-class experts, that was your company's calling hub.
00:10:58.559 --> 00:11:17.919
Well, in the future, it may just be able to scale, you know, millions or billions of world-class agents where maybe your competitors can only uh deploy five percent of that, or one percent that would be a real advantage, and that's what we're seeing uh in this shift from obex to capex.
00:11:18.240 --> 00:11:21.440
But that's really solved the jobs, right?
00:11:22.000 --> 00:11:30.639
Obviously, you know, there's jobs uh you know at these plants, you know, people boots literal boots on the ground constructing, maintaining books.
00:11:31.360 --> 00:11:48.320
But what about the jobs that come to that's why uh this we did talk about this recently, so I'm not gonna go too much into it, but a little over this labor study in episode 730.
00:11:48.480 --> 00:12:06.159
But just as an example in traffic great study, uh when we look at millions of anonymized uh inside of their chat, then they essentially map everything across 20,000 different tasks um in these uh government jobs uh data set, essentially.
00:12:06.399 --> 00:12:12.879
And they now theoretically, AI can do 94% of computer enhancements.
00:12:13.200 --> 00:12:19.840
That's just what it's it, but it was only used 33% of the time.
00:12:20.639 --> 00:12:25.440
Um that's actually the highest usage of observance was 33%.
00:12:25.919 --> 00:12:36.879
So in most cases, you have this capability gap of saying, like, hey, no, hey, I can actually go do all these jobs that we're hiring all these people to do, uh, right?
00:12:37.039 --> 00:12:39.360
All these codifiable tasks.
00:12:40.000 --> 00:12:44.480
Yeah, most companies are doing it, uh right.
00:12:44.960 --> 00:12:57.200
Even if you look at gap, uh, you know, sectors like management, legal, and business, below 20% observed uh usage despite 80% capability, right?
00:12:57.279 --> 00:13:13.279
So it's not 80% of controls and tasks associated with those sectors, management, legal, and business, yet the US at large, um at least rates flawed real anonymous usage, it's only increased by 20% of the time.
00:13:13.600 --> 00:13:21.200
So most companies are overwhelmingly still treated AI like J and it's actually a workforce shifter, right?
00:13:21.279 --> 00:13:32.240
You couldn't have made that claim uh, you know, probably nine months ago, but I can't when is the real shift gonna hit?
00:13:34.000 --> 00:13:38.559
When's shift gonna hit the and who is gonna get hit first?
00:13:42.240 --> 00:13:47.440
Well, we'll dive into that in in a second after a word from our partners.
00:13:48.159 --> 00:13:49.440
Here's a harsh truth.
00:13:49.679 --> 00:13:56.879
Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized.
00:13:57.039 --> 00:14:02.320
Half of companies have AI tools, but only 12% use them for business value.
00:14:02.559 --> 00:14:05.919
Most employees are still just using AI to summarize meeting notes.
00:14:06.080 --> 00:14:10.399
If you're the one responsible for AI adoption at your company, you need section.
00:14:10.720 --> 00:14:15.919
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00:14:16.080 --> 00:14:26.480
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00:14:26.720 --> 00:14:27.360
The result?
00:14:27.679 --> 00:14:32.000
You go from rolling out tools to driving measurable AI value.
00:14:32.159 --> 00:14:38.720
Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI.
00:14:38.879 --> 00:14:41.279
Stop guessing if your AI investment is working.
00:14:41.440 --> 00:14:44.960
Check out section at sectionai.com.
00:14:45.120 --> 00:14:49.360
That's S-E-C-T-I-O-N-AI.com.
00:14:51.200 --> 00:14:57.360
All right, so here's when that shift might happen and the types of roles that might feel it.
00:14:57.519 --> 00:15:00.559
And here's the reality: it's already started.
00:15:00.879 --> 00:15:06.159
You maybe haven't seen it, but uh we've I've talked about it multiple times on the show.
00:15:06.320 --> 00:15:12.559
I think it started mainly toward the end of 2025, but we started to see a little bit of it in 2024.
00:15:12.799 --> 00:15:20.799
That's essentially where AI is augmenting senior workers while entry-level hiring has quietly slowed, right?
00:15:20.960 --> 00:15:33.120
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%.
00:15:33.840 --> 00:15:40.639
Which again, if that was the actual unemployment rate across the board, you know, the the US would be up in arms.
00:15:40.720 --> 00:15:56.480
Uh, 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 middle uh tier workers with AI and then just not hiring those people.
00:15:56.559 --> 00:16:00.399
And the same thing with the baby boomers and the silver tsunami happening.
00:16:00.480 --> 00:16:04.240
Well, they're just not hiring to replace these people.
00:16:04.399 --> 00:16:08.639
But I think what we're gonna start to see this year and next year is just the flattening.
00:16:08.799 --> 00:16:10.240
It's gonna be the great flattening.
00:16:10.320 --> 00:16:23.519
I don't think it's gonna be the great white-collar recession, but I think teams are gonna restructure and that capability gap that I talked about is going to close fast because I think now the average non-AI techie, right?
00:16:23.679 --> 00:16:30.159
The people who haven't been listening to the show since 2023, they're finally starting to wake up and realize, right?
00:16:30.240 --> 00:16:43.840
I actually had a a friend, you know, text text our group chat uh the other day, and she said, um, you know, just kind of a group of our friends, and she said, Wow, like Claude just did an entire presentation for me, right?
00:16:44.000 --> 00:16:57.200
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.
00:16:57.360 --> 00:16:58.240
Yes, it can.
00:16:58.320 --> 00:17:05.519
So I think in 2026, 2027, that's where we see this uh the capability gap start to shrink.
00:17:05.599 --> 00:17:09.920
And also the corporate ladder start to, well, it's not gonna be a ladder anymore.
00:17:10.000 --> 00:17:13.119
I don't think we're gonna have you know that normal tier, right?
00:17:13.200 --> 00:17:17.759
Where you have your entry level, you have your middle management, and then you have your senior leadership.
00:17:18.000 --> 00:17:22.480
I just think the bottom rungs of the corporate ladder are gonna get chopped off.
00:17:22.640 --> 00:17:30.000
Um, and yeah, so bye-bye middle management, I think, as well, um, in 2026 and 2027.
00:17:30.880 --> 00:17:41.920
And then in 2028 to 2030, I think that's where we're gonna start to see the return of the$700 billion in growing, obviously, of infrastructure.
00:17:42.000 --> 00:17:47.119
Uh, because I think that's when the autonomous workflows start to arrive in mask.
00:17:47.279 --> 00:17:51.680
So, one of the things is if you're listening to this, well, you're ahead of the game.
00:17:51.920 --> 00:17:53.519
You know what's coming, right?
00:17:53.599 --> 00:17:58.720
And I'm gonna uh end the show by leaving you with some uh some pieces of advice.
00:17:58.960 --> 00:18:09.119
Um, but I do think it's one of those things, probably by 2030, there's most, I think most roles are gonna be orchestrating agents, right?
00:18:09.279 --> 00:18:20.160
Which might sound weird, just like it probably would have sounded weird in you know 1999 if someone was like, hey, all roles are gonna be using the internet.
00:18:21.119 --> 00:18:24.480
It's like, no, that makes no sense, right?
00:18:25.279 --> 00:18:27.519
But that's that's the reality, right?
00:18:27.599 --> 00:18:32.480
So many knowledge workers uh are just using the internet all day.
00:18:32.559 --> 00:18:34.240
Um, and I think that's where we're gonna be.
00:18:34.319 --> 00:18:43.839
I think probably by 2030, early 2030s, uh, most knowledge working jobs are gonna be agent orchestration.
00:18:45.839 --> 00:18:49.599
So let's talk a little bit about those types of roles.
00:18:49.680 --> 00:19:06.799
So there's a recent study that came out from the Federal Reserve uh Federal Reserve Bank of Dallas, and it talked about kind of uh codified knowledge versus tacit knowledge, uh, which is a great way to kind of classify these different types of well, knowledge and tasks, right?
00:19:06.880 --> 00:19:11.599
But essentially, codified knowledge is what new graduates really rely on, right?
00:19:11.680 --> 00:19:18.960
It's those easy kind of quote unquote grunt work, and that's how companies have historically trained juniors, right?
00:19:19.119 --> 00:19:21.519
Junior researchers, junior analysts, whatever.
00:19:21.680 --> 00:19:25.440
It's this kind of this learning track that companies have always gone through.
00:19:25.599 --> 00:19:32.400
You give the easy, the quote unquote easy work that can easily be verified, but it's just manual, right?
00:19:32.559 --> 00:19:46.240
It's the manual grunt work, and it's those those tasks, uh, the summarization, the personalization, the synthesis of of knowledge, synthesizing knowledge um and personalizing through uh through the company's tone and creating something of value, right?
00:19:46.319 --> 00:19:57.279
So a lot of times that's your general research task, you know, competitors, uh, market trends, you you know, put in spreadsheets, run some formulas, make a little presentation, right?
00:19:57.440 --> 00:19:59.039
Like that's junior work.
00:19:59.119 --> 00:20:00.480
That is the codified knowledge.
00:20:00.640 --> 00:20:02.079
That's gonna be gone, right?
00:20:02.240 --> 00:20:04.480
AI does that already much better.
00:20:04.559 --> 00:20:06.799
Uh, and companies are starting to realize that.
00:20:06.880 --> 00:20:10.480
And they're saying we don't need to hire humans for that anymore.
00:20:10.559 --> 00:20:11.039
Uh, right.
00:20:11.119 --> 00:20:16.160
But the tacit knowledge, that's obviously not where AI can touch yet, right?
00:20:16.319 --> 00:20:21.920
That's the nuanced uh required to make decisions that come along with being a subject matter expert.
00:20:22.079 --> 00:20:37.920
I think that's you know how you have to, you know, sometimes be able to understand a larger organization or a competitive field, uh, right, is that kind of uh the the savvy uh that you might need, not office politics per se, but sometimes office politics, right?
00:20:38.000 --> 00:20:41.279
That's what AI cannot uh you know do yet.
00:20:41.680 --> 00:21:05.759
But I think regardless, another shift that's happening and already happening is that younger generation uh, you know, that we've seen that uh I already talked about 14 uh you know that the exposed um field, so hiring for younger workers 22 to 25 in exposed fields dropped roughly 14%.
00:21:07.119 --> 00:21:10.400
All right, so what can you do about it today, right?
00:21:10.640 --> 00:21:18.160
Before you finish that morning coffee that maybe you're sitting on right now, know a couple things.
00:21:18.559 --> 00:21:22.720
Again, if you if you are not new to AI, you're in a great place.
00:21:22.799 --> 00:21:36.880
Even if you do feel uncomfortable about this labor shift that is going 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?
00:21:36.960 --> 00:21:38.400
And it's actually pretty telling.
00:21:38.480 --> 00:21:48.480
Uh, the Dallas Fed study showed that actually a lot of wages have risen uh right uh nationally versus where they were at previously.
00:21:48.559 --> 00:21:53.200
Uh, because if you are keeping your job, even though there's fewer jobs, those jobs are getting paid much more.
00:21:53.359 --> 00:21:58.000
And not just the standard, you know, cost of living, cost of living in inflation.
00:21:58.160 --> 00:22:06.000
It's companies have Now um realize that they will have to pay a premium dollar, right?
00:22:06.240 --> 00:22:11.599
Because when you do have fewer employees, the stakes are higher.
00:22:11.759 --> 00:22:19.039
And those employees that you are going to be orchestrating agents, you really want to show that you have the right people, right?
00:22:19.119 --> 00:22:22.480
Because that's a compounding factor that we haven't seen before.
00:22:22.720 --> 00:22:31.440
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, right?
00:22:31.599 --> 00:22:36.400
But with AI and agentic AI, it is a compounding factor.
00:22:36.559 --> 00:22:44.480
So if you have the right smart people, they can literally 5x, 10x, 20x over time.
00:22:44.640 --> 00:22:47.119
So that's why you know these roles are actually getting paid more.
00:22:47.200 --> 00:22:55.599
And workers with AI skills are earning on average 56% more than those without skills across similar industries.
00:22:55.680 --> 00:23:03.440
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.
00:23:03.599 --> 00:23:06.079
That's the highest combination in any field.
00:23:06.240 --> 00:23:17.119
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.
00:23:17.440 --> 00:23:23.680
So the new AI jobs, though, they're just not going to be for engineers.
00:23:23.839 --> 00:23:27.599
I think two years ago, uh, that was kind of the uh the thought, right?
00:23:27.759 --> 00:23:33.759
Oh, anything uh, you know, for the future growth of AI, if you want to work in there, you have to be CS.
00:23:33.920 --> 00:23:36.640
You gotta be computer science, you gotta be a coder, you gotta be an engineer.
00:23:36.720 --> 00:23:37.519
Not anymore.
00:23:37.680 --> 00:23:43.920
Uh, as an example, one in uh a recent IBM study showed that one in four companies now have a chief AI officer.
00:23:44.160 --> 00:23:49.440
Although part of that is telling, I think, in both regards.
00:23:49.599 --> 00:23:50.960
Okay, that's impressive.
00:23:51.039 --> 00:23:54.720
That, you know, one in four, but why not two and four?
00:23:54.880 --> 00:23:56.160
Why not three and four?
00:23:56.240 --> 00:24:06.400
Uh, but right now I think we're seeing emerging roles include things like context engineer, agentic orchestrator, AI auditor, GEO, strategist, right?
00:24:06.559 --> 00:24:10.079
Showing up um in uh in chat, I guess, right?
00:24:10.240 --> 00:24:19.759
Um, but the barrier has dropped and these roles really, I think, reward domain expertise uh over technical know-how or software or coding.
00:24:19.920 --> 00:24:38.720
So I think a lot of people um have been scared away by job growth because we look at how the uh you know jobs and how careers have progressed over the last couple of decades, because that's all we can look at.
00:24:38.880 --> 00:24:44.319
And I don't think that's how jobs are going to pan out because it's not gonna be a one-to-one match.
00:24:44.400 --> 00:24:50.480
And it's not even gonna be a you know a one to two to three, it's gonna be a one to B.
00:24:50.799 --> 00:24:55.839
You know, it's it's it's gonna be on a on a playing field that isn't level, right?
00:24:55.920 --> 00:25:01.759
So I think a lot of times when people are saying, hey, I do have AI skills that I've been putting to place.
00:25:01.920 --> 00:25:02.880
What do I do now?
00:25:03.039 --> 00:25:10.240
Well, you might want to start looking at roles that you might have thought were previously technical roles, and they're probably not.
00:25:10.480 --> 00:25:10.960
All right.
00:25:11.039 --> 00:25:20.319
You you have some of the greatest, you know, software engineers of all time now admitting that they don't even code anymore.
00:25:20.559 --> 00:25:25.119
These are people that have, you know, helped code the products that have changed the world.
00:25:25.200 --> 00:25:35.200
And they're saying, well, you know, not to get into you know uh self-learning and and all of those things, but the models are really building the models now, right?
00:25:35.599 --> 00:25:38.240
And humans are just orchestrating agents.
00:25:38.319 --> 00:25:46.400
So that does really change, I think, what you are capable of, because that barrier has dropped.
00:25:46.559 --> 00:25:47.920
All right, so we're gonna end on this.
00:25:48.000 --> 00:26:02.559
This is your three-step survival guide for starting today, because we don't know when the next round of jobs are gonna be gone or when the new jobs that don't exist yet are gonna start popping up.
00:26:02.720 --> 00:26:07.599
And I personally don't think it's gonna be a nice, uh, a nice handoff.
00:26:07.680 --> 00:26:15.119
I'm not saying, you know, there's gonna be massive unemployment or anything like that, but I'm a realist when it comes to AI.
00:26:15.759 --> 00:26:21.279
Corporate greed, I think, is gonna cause a lot more harm in the long run than AI.
00:26:21.519 --> 00:26:24.960
Obviously, those two things uh, you know, walk hand in hand.
00:26:25.839 --> 00:26:32.880
But I don't think it's gonna be five million jobs gone this year, five million new jobs created two months later.
00:26:32.960 --> 00:26:33.920
It's not gonna be like that.
00:26:34.079 --> 00:26:35.359
So here's what you have to do now.
00:26:35.519 --> 00:26:37.599
Number one, you have to audit your role.
00:26:37.759 --> 00:26:48.400
You have to start separating those daily tasks into what are those judgment-based tasks uh versus those codifiable tasks.
00:26:49.680 --> 00:26:50.160
Right?
00:26:50.400 --> 00:27:04.480
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.
00:27:04.720 --> 00:27:06.880
All right, so you first need to audit your role.
00:27:06.960 --> 00:27:16.880
You need to have a realistic expect uh or 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.
00:27:17.359 --> 00:27:20.960
Number two, document your reasoning, right?
00:27:21.519 --> 00:27:27.599
Write down why you made hard decisions and what you weighed when going through those decisions.
00:27:27.759 --> 00:27:31.519
I've talked about this, I think companies and departments, this is kind of separate, right?
00:27:31.680 --> 00:27:41.200
I'm kind of leaving you with uh more from a personal career employability uh standpoint, but I've also been very bullish on companies doing this.
00:27:41.359 --> 00:27:54.559
You need to document your literal company reasoning, uh right, because that is some of that uh, you know, knowledge, at least right now, your reasoning, your subject matter expertise, uh, your domain experience.
00:27:54.960 --> 00:28:02.640
Sometimes you don't always practice those things because they are innate and they don't always, you know, show up in that spreadsheet.
00:28:02.720 --> 00:28:06.160
They don't always show up uh in that deliverable, in that artifact.
00:28:06.319 --> 00:28:10.000
A lot of times uh it is the filling in the uh filling in the gaps.
00:28:10.160 --> 00:28:11.920
You really have to document that.
00:28:12.319 --> 00:28:21.920
And then last but not least, uh, I think you need to master one AI platform deeply until it literally just becomes an extension of your own expertise.
00:28:22.079 --> 00:28:24.000
And I think that's difficult.
00:28:24.240 --> 00:28:29.519
I think that's difficult for people because you see all these new advancements in AI, right?
00:28:29.680 --> 00:28:47.599
Oh, the, you know, first we had uh, 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.
00:28:47.680 --> 00:28:57.119
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.
00:28:57.680 --> 00:28:58.880
You will fail.
00:29:00.079 --> 00:29:01.279
Let me say that again.
00:29:01.519 --> 00:29:07.839
If you are just trying to keep your skill set up to par with the skill set of the week, you will fail.
00:29:08.000 --> 00:29:08.720
You can't do it.
00:29:10.160 --> 00:29:11.359
You can't, right?
00:29:11.519 --> 00:29:15.920
This is what I try to do every day, but you can't do it, right?
00:29:16.079 --> 00:29:16.960
I can't do it.
00:29:17.440 --> 00:29:21.359
I can't go out and you know, do all these things I talk about every single day.
00:29:21.599 --> 00:29:23.359
Two years ago I could, today I can't.
00:29:23.519 --> 00:29:25.359
No single human can anymore.
00:29:25.759 --> 00:29:30.319
So you have to double down on one platform, right?
00:29:30.480 --> 00:29:33.039
What it, whatever it may be, uh right?
00:29:33.200 --> 00:29:37.519
I don't know, developing, you know, skills for accounting, uh, right?
00:29:37.680 --> 00:29:38.319
I don't know.
00:29:38.480 --> 00:29:43.839
And when I say skills, I mean, you know, obviously agetic skills and writing them, right?
00:29:44.079 --> 00:29:49.359
But you have to overlap that with your current domain of expertise.
00:29:49.839 --> 00:30:01.680
And I think if you go through those three steps, it's not going to, you know, be a save all safety meta if there is a white-collar job recession, right?
00:30:01.759 --> 00:30:04.079
The the AI job apocalypse.
00:30:04.240 --> 00:30:13.759
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.
00:30:14.079 --> 00:30:14.640
All right.
00:30:14.799 --> 00:30:17.519
I hope this was helpful as we talked about.
00:30:17.680 --> 00:30:24.799
Uh start here series, volume 13, the AI labor shift, when it'll happen, and what it means for jobs.
00:30:24.960 --> 00:30:30.559
All right, there's also there's guys, there's way too much I couldn't get to in today's show, if I'm being honest.
00:30:30.799 --> 00:30:36.240
Put together a ton of cool resources, uh, some great things in Nobook LM, all of that.
00:30:36.400 --> 00:30:42.480
So if you do want access to this couple, you know, cinematic videos, some slideshows, uh, all that.
00:30:42.640 --> 00:30:49.440
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.
00:30:49.599 --> 00:30:50.960
There was so much I couldn't get to.
00:30:51.039 --> 00:30:55.680
I didn't want this to turn into one of those, you know, accidental 60 minute shows.
00:30:55.759 --> 00:31:00.079
So if you go repost this on LinkedIn, I will send you all of that over.
00:31:00.240 --> 00:31:02.960
So if you're looking for the LinkedIn show, right?
00:31:03.039 --> 00:31:04.799
If you're listening on the podcast, you probably are.
00:31:04.960 --> 00:31:06.000
Just look in the show notes.
00:31:06.079 --> 00:31:07.359
It's always in the show notes.
00:31:07.440 --> 00:31:10.640
It says, like, you know, go join the conversation on LinkedIn.
00:31:10.880 --> 00:31:12.480
That is the LinkedIn link.
00:31:12.559 --> 00:31:14.160
And then just go repost that.
00:31:14.319 --> 00:31:16.559
And I will send you all of those resources over.
00:31:16.640 --> 00:31:19.039
And then make sure you go to starthireseries.com.
00:31:19.200 --> 00:31:27.920
That's gonna 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.
00:31:28.000 --> 00:31:28.960
So thank you for tuning in.
00:31:29.039 --> 00:31:31.920
Hope to see you back tomorrow and every day for more everyday AI.
00:31:32.000 --> 00:31:32.799
Thanks, y'all.
