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The AI Labor Shift: Concrete Data on Job Elimination, CapEx Priorities, and New Skills That Command Premium Wages
As AI investments accelerate and business headlines warn of tens of thousands of job cuts, new data reveals the real business implications behind the shifting employment landscape. Leaders are no longer navigating potential scenarios—they are contending with a labor shift defined by calculable workforce reductions, strategic investments, and clear skill demand signals. The following analysis translates research and hard numbers from recent business reports to provide practical, data-driven insights for decision makers.
AI Job Reductions: Quantifying Current Reality
In the first months of 2026 alone, U.S. companies reported over 40,000 planned job eliminations attributed to AI. This adds to an existing 45,000-plus AI-related job cuts already recorded in 2026, according to Section research. Major tech firms including Meta, Block, and Oracle are publicly associating tens of thousands of headcount reductions with “AI efficiencies.” Meta, for instance, cited more than 15,000 roles targeted for elimination, while Block cut 4,000 jobs and Oracle is moving towards 30,000 reductions.
However, recent Harvard Business Review data shows that 60% of hiring managers referencing AI as the cause for layoffs actually do not replace these roles or augment them with AI—a strong indicator that AI explanations are often leveraged as a narrative device for broader cost reductions. Public companies frequently experience positive market responses for these AI-cited cuts: when Block announced its 40% workforce reduction and attributed it to AI, the company’s stock climbed 22%.
Capital Expenditures: Following the Money to Understand Corporate Strategy
Unlike in previous technology cycles, today’s largest tech firms are making unprecedented capital investments directly into AI infrastructure. Amazon, Alphabet, Meta, Microsoft, and Oracle are collectively set to spend $700 billion in 2026 on data centers, custom silicon, cooling technology, and power generation—rather than on hiring or team expansion.
This reallocation of capital—moving from operating expenses (OPEX, primarily wages and benefits) to capital expenditures (CAPEX)—holds crucial meaning. It reveals deliberate substitution of salary-based spend with long-term infrastructure placement, betting on automation and agentic systems to ultimately handle work previously done by large employee populations. For sectors that build and support these data centers, the current environment is lucrative; for traditional white-collar roles, it signals narrowing opportunities and the need to pivot towards expertise that cannot be easily outsourced to machines or automated away.
The AI Skills Premium: What Data Shows About Who’s Benefiting
Despite mass layoffs, there is substantial wage growth for high-skill, AI-fluent professionals. The Federal Reserve Bank of Dallas found that tech wages have risen faster than cost of living and that workers proficient in AI are earning 56% more than their peers. This outpaced compensation is reserved for employees whose productivity compounds thanks to AI tools—enabling individuals to achieve outcomes that previously required teams of ten or more.
Crucially, these premium roles are most accessible to employees who combine deep domain expertise with a high level of fluency in a single, powerful AI platform. For them, the capacity to train, orchestrate, and evaluate AI agents becomes a competitive edge. Studies also show that the “capability gap” between what AI can theoretically handle and how it is used daily at most organizations is massive—across computer and math roles, for example, AI could theoretically handle 94% of workloads, yet only 33% of this potential is realized due to underutilization, lack of training, and hesitation to adjust job design for the AI era.
Entry-Level Hiring Slowdown and the Crumbling Corporate Pyramid
A silent adjustment is occurring beneath the radar: entry-level positions in knowledge work are quietly disappearing rather than being replaced. Growth in junior hiring is down by 14%, and the classic corporate ladder is being dismantled in favor of flatter, smaller teams. Surveyed companies report they are simply not filling roles as staff depart, preferring “quiet hiring” through internal upskilling or process automation over publicized layoffs.
This trend is most acute in roles reliant on codified knowledge—tasks that AI can now complete better and faster than most new graduates, such as research, writing, synthesis, and basic analytics. Instead, organizations are seeking existing, proven experts and amplifying their capabilities with AI, compensating these top performers at higher wage bands.
Actionable Steps: How to Capture Value and Avoid Redundancy
For organizations and professionals aiming to thrive during the labor shift, three immediate strategies have emerged:
Audit Role Tasks for AI Susceptibility: Explicitly separate codified (routine, information-based) tasks from tacit (expertise, nuanced judgment) activities. This clarity is the foundation for future relevance.
Document Expert Reasoning: Develop a practice of capturing decision frameworks and domain-specific insights that are not easily codified. This protects and reveals the core human value in each role.
Double Down on One AI Platform: Rather than chasing every emerging tool, select a leading AI platform and embed it deeply into daily workflows, making it an extension of in-house expertise. Mastery, not breadth, is now the differentiator.
Summary: A Data-Driven Labor Shift in Motion
The impacts of AI on employment are no longer matters of speculation—they are specific, accelerating, and tied to measurable business strategies. Wage premiums for AI-savvy professionals, capex surges from tech leaders, entry-level hiring freezes, and the increasing value of domain plus AI fluency provide a roadmap for companies and individuals to avoid obsolescence. Organizations that train for AI daily, document their expert advantage, and align their teams on focused platforms will be best positioned for the business landscape that lies immediately ahead.
For deeper resources and practical frameworks for implementing these shifts, move beyond headlines to actionable guides and strategy communities designed for transformative business leaders navigating the AI era.
Topics Covered in This Episode:
- AI-Driven Job Cuts and Layoffs Analysis
- The AI Labor Shift: Timeline Predictions
- Big Tech AI Infrastructure Investments
- AI as Layoff Scapegoat and "AI Washing"
- Capability Gap: AI Use vs. Potential
- Roles Most at Risk in AI Labor Shift
- Codified vs. Tacit Knowledge Job Impact
- Three-Step Survival Guide for AI Careers
- Domain Expertise Plus AI Skills Premium
- New AI Job Roles and Emerging Opportunities
Episode Transcript
Jordan Wilson [00:00:18]:
Buying AI tools is the easy part. Getting your employees to actually use them for anything meaningful, that's where most companies fall apart. Section's own research shows over 90% of employees only use AI for basic tasks. That's not ROI. Section coaches every employee on real role specific use cases, tracks who's driving AI impact, and it gives you the data to prove it's working. For more, check out Section at sectionai.com. In the past two weeks alone, we've seen reports of more than 40,000 plus jobs that will be eliminated soon due to AI. And that's on top of the already 45,000 plus AI related job cuts in The US we've seen so far in 2026.
Jordan Wilson [00:01:06]:
Yeah. We've read all those studies and projections for years talking about how AI's broad capabilities will create millions of jobs that just don't exist yet. So it seems we may soon then be entering a crossroads of traditional employment slowly dying in the birth of a new era of work. I'm calling it the AI labor shift. And while it does sound kind of exciting, like we're gonna be living in a new era, it's also pretty scary. Like, have we humans really wasted decades of our working lives becoming subject matter experts just to now babysit AI agents who are way smarter and faster than us but just need constant hand holding? Maybe. And that's what we're gonna be tackling on today's show, everyday AI and our start here series. Alright.
Jordan Wilson [00:02:03]:
So here is the big picture. Jobs are getting eliminated, and no one knows what the future of work looks like. So this year, so far, we've already seen 45,000 plus AI related tech layoffs because, well, that's what's happening. Big tech companies, I've always said this, going back to 2023. Let me just start here. Already getting on a on a side rant, and we barely started. I've been saying since 2023 that AI will ultimately take more full time roles than it will create. Yes.
Jordan Wilson [00:02:43]:
There's gonna be tons of new jobs, but I've always said that follow the big tech companies because they're gonna be the first dominoes to fall. Right? And we've already seen five companies that are planning to spend $700,000,000,000 on AI infrastructure. So the jobs are going away. The AI investments are piling up, and, well, we don't know where these new jobs are going to be, which puts us all in this kind of pickle. So we're gonna be, well, coming with some receipts, facts, stats, and trends. And on today's show, we're gonna be addressing that and more. And stick to the end, and you're gonna learn the financial trick behind AI layoffs that most workers and journalists are falling for. You're gonna learn why the real displacement timeline is already underway and almost no one can see it yet.
Jordan Wilson [00:03:32]:
We're gonna go over which everyday roles are first in line to go and which ones did not even exist two years ago. And I'm gonna give you at the end a three step survival guide you can start using before you even finish your coffee today. Also, stick around. I did put together some extra assets for this show, and I'm gonna tell you at the end how you can get a hold of those. So a lot to go over on today's show. If you're new here, welcome to Everyday AI. My name is Jordan Wilson, and this is our start here series. Because after doing this every single day for three years, more than seven hundred and, I don't know, 30 now episodes, 730 episodes, I didn't have a good answer.
Jordan Wilson [00:04:15]:
When new people would buy the podcast and be like, Jordan, there's too much. Where do I start? And I'd always be like, I don't know. Well, now you start with the start here series. This is the essential podcast series to both learn the AI basics and to double down on your AI knowledge. So go to starthereseries.com. That is gonna give you free access to our inner circle community. Yeah. You can't find it anywhere else.
Jordan Wilson [00:04:39]:
It's unlisted. It's private. So go to starthereseries.com. That's gonna give you free access as well is you're going to go straight into the start here series space where you can go listen to every single volume of this series all right there in one place. So if you miss our last volume, volume 12, we talked about the state of the AI race, who will win in 2026, OpenAI, Microsoft, Google, or Anthropic. And today, let's get straight into it. Let's get shifting, talk about when this AI labor shift is going to happen and what it ultimately means for jobs. So here's what's happening.
Jordan Wilson [00:05:21]:
And whether you're listening to this live in real time or maybe you're listening to this, I don't know, in 2027. Well, here in early twenty twenty six, the big tech companies are cutting jobs en masse. Right? So Meta, we just saw a report, more than 15,000 workers on the chopping block because of AI. Speaking of block, block just cut 4,000 jobs, and Oracle is reportedly planning up to 30,000 reductions because of AI efficiencies. And this ties into the larger trend here in The US that we actually saw a pretty down month for jobs. Right? The February 2026 jobs report showed 92,000 jobs lost in unemployment hit four and a half percent. So, not a normal jobs report. So it's kind of, the a lot of the big tech layoffs combined with, you know, kind of agentic AI's surge in late twenty twenty five.
Jordan Wilson [00:06:18]:
This job reports, it's got everyone kind of talking and a lot of people, understandably so, maybe a little uneasy about what does all of this mean for my job, my career. That's what we're gonna be unraveling. But I think AI in general when it comes to jobs, because let's start there, it's become the the scapegoat. Yes. Sometimes, you know, AI is actually being used and jobs do truly become redundant, but not always according to a recent Harvard Business Review study that showed that 60% of hiring managers who cite AI as the reason for layoffs, only 2% are actually replacing those roles or augmenting those roles with AI. So it seems like sometimes AI is just kind of a get out of jail free card or or a scapegoat. And, a lot of these quote, unquote AI, layoffs, especially for big tech, are just over hiring corrections. You know, also rising interest rates and just the overall squeeze.
Jordan Wilson [00:07:20]:
I think it's getting harder for some companies to turn a profit for a lot of reasons. But I think one of those reasons is actually AI. Right? AI is allowing more people, or sorry, it's allowing more companies to do more with less, which in some instances can drive prices down. It increases competition, which are all good things. Right? But what that has meant for maybe large bloated companies is their margins are starting to shrink, whether they're using AI or their competitors are. They're losing out on, you know, new clients, new projects. But it's definitely, a delayering, from probably a lot of post pandemic overhiring and correcting for that. Right? The Amazon, layoffs, in late twenty twenty five, the 16,000, they were attributed to AI, but then essentially, Amazon CEO kind of admitted, yeah, it's also just about reducing layers.
Jordan Wilson [00:08:20]:
So it seems like, you know, sometimes blaming something on AI, and we'll get to why that's actually advantageous for companies to just say, yeah. These are AI job losses. Sometimes they are, but a lot of times it's just, well, you had way too many employees to begin with because so many companies over hire. And as technology naturally progresses, the outputs have to keep pace. Right? So for those companies that have changed their roles to be more AI native and the outcomes and the outputs and the deliverables and the artifacts have scaled correspondingly, I don't think they're running into these same problems. But a lot of the companies that are slower moving and the job description is the exact same as it was ten years ago, I think those are the types of companies that are having to go into these massive layoffs. And here's why they're still saying it's an AI layoff whether it is or not. Because when you do, Wall Street loves you.
Jordan Wilson [00:09:15]:
Y'all, I literally been saying this since 2023, since before AI washing was a thing. That's what this is. This is called AI washing. When companies, you know, make huge cuts, they say, hey. It's AI efficiencies, and their stock goes through the roof. So Block cut 40% of their staff, earlier this month cited AI and their stock rose 22%. So this is the AI washing that has also just created mass worker anxiety. And I don't think it's always necessarily grounded in reality.
Jordan Wilson [00:09:50]:
Yes. Sometimes these massive, job layoffs are because of AI. I think more than anything else, it's a lot of these CEOs, and people boardrooms making these decisions are seeing the future of AI, not necessarily what AI is today. And I think a lot of companies are now having to deal with the egg on their face of not properly training their employees or sitting on the fence in 2023 and maybe even 2024, and they're finally seeing and realizing it for the first time. And they're like, oh, wait. Yeah. This is gonna completely change our workforce. Right? So maybe let's do this, let's do this big tech fortune 100 playbook where, you know, we can just chop a bunch of jobs to say it's because of AI.
Jordan Wilson [00:10:37]:
And if for public companies, at least, it's usually a playbook that works well. So what's actually driving these cuts? To To get a little technical, well, not super technical, but maybe, for the average everyday listener who's not following this, it's really one big shift. It's going OPEX to cap. Right? That's operating expenditures to capital expenditures. It's going from companies who have let's look at Meta. Right? Meta, nearly 16,000 jobs reportedly are gonna be gone soon because of AI, yet they're spending tens of billions of dollars in capital expenditure building. Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized.
Jordan Wilson [00:11:36]:
Half of companies have AI tools, but only 12% use them for business value. Most employees are still just using AI to summarize meeting notes. If you're the one responsible for AI adoption at your company, you need Section. Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result? You go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI. Stop guessing if your AI investment is working.
Jordan Wilson [00:12:21]:
Check out section at sectionai.com. That's sectionai.com. AI infrastructure. Right? And even just five companies, Amazon, Alphabet, Meta, Microsoft, and Oracle combined this year are gonna spend $700,000,000,000 in capital expenditures for AI. So instead of money going to salaries, benefits, and all of those nagging costs that come along with working with humans, Instead, they're going into data centers, silicon, cooling plants, power generation. Yes. There's obviously some new jobs that go around, you know, creating and maintaining. You know, so if if your your niche has been, you know, building data centers for decades, you're striking it rich right now.
Jordan Wilson [00:13:15]:
But, I think it's income I think it's incumbent on ourselves to realize that these companies right now, the biggest tech companies that I think everyone's trying to follow their lead. Notice, they're not investing $700,000,000,000 in today's AI. They're not saying, oh my gosh. Let's take this money and hire more people because today's AI is is where it's at. No. Because the learning curve, the education curve, the training curve is so far behind. I think what's happening is we're seeing the agentic, the autonomous capabilities that we have in early twenty twenty six. And big tech companies are saying, oh my gosh.
Jordan Wilson [00:14:00]:
You know? I think it's gonna take the general population ten years, to rework and to unlearn. Right? Don't reskill. Don't upscale. That's failure. You gotta unlearn and rebuild. Right? But I think the bigger tech companies are saying, okay. Well, I think they'll be able to unlearn their workforce maybe in three, four, five years and really take advantage of that. So I think they're, placing bets on tomorrow's technology, not necessarily today's.
Jordan Wilson [00:14:28]:
And that's because we've come to realize now, I think, you know, Claude Code maybe helped open a lot of people's eyes. You know, OpenAI's codex, OpenClaw. Right? Now these, you know, autonomous, you know, systems that just work. I think that's what's really changed the narrative here. And I tackled this on episode seven thirty. So if you wanna go, listen to this one, we talked about Anthropic's recent labor study and the realization that this is well, AI can already do the work that humans smart humans. Right? Humans with college degrees, they can already do it. So, you know, one kind of stat to pick on there is we went over the theoretical AI coverage, which is essentially what AI models are actually capable of and the observed use, which is, well, what humans are actually using AI for.
Jordan Wilson [00:15:23]:
So this is an anthropic study. So they used millions of anonymized chats inside of Claude and then a, essentially, federal job data that looked at all these different jobs, 20,000 different skills, and they just matched it all up. Right? All these anonymized chats, what are they actually being used for, and what can the models theoretically do? And what this led to is a huge capability gap. And what we've realized, and I've been talking about this for a long time, it's number one, education and trading. It's like everyone asked me, like, Jordan, how do I grow my company? Train your people. Educate them. Right? Get a bunch of mes running around. That's all you need.
Jordan Wilson [00:16:00]:
Right? Literally, file just have a bunch of people that just understand AI every day, play with it every day, test it every day, scope it every day, every day, every that's all you do, every day AI. Right? Anyways, it's a huge capability gap because people don't know. And it's not just, you you know, one example in anthropics report was computer and math role roles showed at 94% theoretical AI coverage. So the AI could theoretically do 94% of those 20,000 roles that had to do with computer math roles, but only 33% observed use. But it's across the board. Right? AI coverage in management, legal, and businesses all sat below 20% despite 80% theoretical capabilities. So more most organizations are still just looking at AI like a chatbot or like a smarter search, and they're not actually seeing it for the workforce shift it is. So when does the real shift hit, and who's going to get hit first? That is the big question, and we're gonna answer that after a quick word from our partners.
Jordan Wilson [00:17:09]:
Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value. Most employees are still just using AI to summarize meeting notes. If you're the one responsible for AI adoption at your company, you need Section. Section is a platform that helps you manage AI transformation across your entire organization. It coaches employees on real use cases, tracks who's using AI for business impact, and shows you exactly where AI is and isn't creating value. The result? You go from rolling out tools to driving measurable AI value.
Jordan Wilson [00:17:53]:
Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI. Stop guessing if your AI investment is working. Check out section@sectionai.com. That's sectionai.com. Alright. So when will that shift happen and what type of roles might feel it? Well, shift has already hit the fan, y'all. So, yeah, here's what most people are kind of ignoring. It's already happening.
Jordan Wilson [00:18:25]:
Right? Because I think in 2024, 2025, AI has already been augmenting senior workers while entry level hiring has quietly slowed. That's another thing that we talked about in episode seven thirty going over that anthropic report. Saw that 14%, slowed down growth for entry level workers. So I think this year and next year, as we look at this timeline of when the shift is gonna happen, I think teams are gonna restructure. And the capability gap is gonna start to close because companies have already started to recognize and realize that capability gap, and they're gonna do something about it. And I think what is gonna ultimately happen is the corporate ladder is going to crumble upon itself in traditional management, middle management. It's gonna be gone. We've already seen the, the entry level jobs, you know, kind of slowly starting to disappear, not even getting into the silver tsunami as baby boomers, you know, retire.
Jordan Wilson [00:19:27]:
I think the workforce is going to flatten because at the same time, personally, I don't see it. Yes, there are going to be new companies that come and do great revenue. Right? But a lot of these new companies that are starting are starting very lean because when you start a new company from scratch, you most people are well, they're using AI to do it because you can find yourself like, oh my gosh. I can start right? I'm I'm a big tech worker. I just got laid off. I know what I'm doing. I'm a consultant. I just got laid off.
Jordan Wilson [00:20:01]:
I know what I'm doing. I can use all these AI tools. I don't need 10 employees to start. I can start in one person do the work of 10 people. So, I think that the overall workforce is going to shrink, but specifically at companies, those teams are gonna get probably smaller. It's call I call it quiet, quiet hiring. Right? It's the opposite of quiet firing. They're just not really gonna hire people anymore.
Jordan Wilson [00:20:25]:
Some companies may not go through the mass firings, but then I think the last leg of this short timeline is in 2028 to 2030, which is when I think we're gonna start to see some of the gains of these, you know, like, the $700,000,000,000 in infrastructure. Essentially, I think that people who follow AI every day, like you and me, we've realized now what these autonomous systems can do. Right? These, agents now are actually very capable. Agentic models, right, with a $20 a month subscription to Cloud or Gemini or ChatGPT, you could do, right, even what it took 10 people to do with ChatGPT alone in late twenty twenty two when it first came out. Right? The capabilities are, compounding. So I think by, you know, 2028, 2030, that's when I think the autonomous workflows are gonna start to go mainstream. Right? In the same way that if someone from the mid nineteen nineties said that, hey. One day, you know, knowledge workers are all just gonna be people who work on the Internet.
Jordan Wilson [00:21:36]:
You're like, no. The Internet? You sure about that? Yeah. Same thing. It's everyone's gonna be orchestrating agents in the twenty thirties, and I think that's kind of the last shift of what's happening with jobs. So, yeah, I think we're gonna see, normally, job changes are very slow because tech innovation, comparatively to what we've seen with AI, is snail's pace. Right? Which is why jobs sometimes take five, ten, fifteen, twenty years to change even with the Internet social media. Right? A lot of those jobs are still relatively unchanged or took two decades to change. Right? That's not what we're gonna see with AI.
Jordan Wilson [00:22:22]:
We're gonna see jobs completely gone in two years. Yes. There's gonna be new roles. We don't know what those are, but we'll see. Like I said, I've always been on the record. I do think AI is going to, quote, unquote, take more jobs than it will make at least when it comes to traditional full time employment. So here's what's actually happening, where that shift is happening. And this is, a recent report, a great one from the Federal Reserve Bank of Dallas, essentially looking at codified knowledge versus tacit knowledge.
Jordan Wilson [00:22:55]:
So codified knowledge, that's essentially that groundwork. Right? This is what a lot of times new graduates rely on to, you know, earn their keep and climb the corporate ladder, but this is now stuff AI just does way better. Right? It's it's researching, it's writing. It's personalizing, synthesizing information, creating spreadsheets and PowerPoints. Right? Right? AI is way better at that. So codified knowledge? Yeah. Tacit knowledge? Different. Right? AI is not really, at least yet, you know, started to tread on, you know, nuanced expert judgment.
Jordan Wilson [00:23:33]:
Although, I do think it's it will get there eventually. But when you look at replacement, it's starting with codified knowledge. And companies are just, well, not hiring the young juniors. Right? Junior researchers, junior analysts that would normally do some of that codified work. Now it's all very simple for a large language model to do. And that's why we've seen that 14% kind of hiring drop that I referenced earlier. So what can you do about it starting today? Because the reality is there's probably a good chunk of us even if you do, right, get to exercise that task adjustment a lot. Right? Like, that that nuance that, you know, is more, flexing your, you know, getting projects through the finish that aren't always related to artifacts and deliverables.
Jordan Wilson [00:24:25]:
Right? Maybe the, inner office workings and politicking a project to fruition. Well, yes. A lot of people spend a good amount of their time there, but a lot of people are still doing those fortifiable tasks. Right? Those things that maybe seem like, oh, maybe an AI could do this better, which is why I think a lot of people are feeling uneasy about their careers because it's happening. This, this collision of the labor shift, AI innovation, and, companies now being willing to do this in mass. Right? It's creating this sense of uneasy. So here's what you can do about it starting today. First of all, if you have been tuning in, right, if you are putting AI to work right now, you're in a better place than most.
Jordan Wilson [00:25:20]:
Right, that same Dallas Fed study, showed that tech wages actually rose and outpaced, you know, normal inflation cost of living, things like that. Because what this study found is even though companies are hiring fewer people and there's maybe, at least right now, fewer jobs, the people who do still have that those jobs are getting paid more. And, well, here's why. Because now more than ever, your skills can pound. Right? What you can accomplish with AI today is much different than what you could accomplish with AI three, four years ago. Here's what I mean by that. Your skills can three x, four x, five x, 10 x fairly easily, whereas even to say that sentence two years ago kind of felt, you know, like, lofty. It's not.
Jordan Wilson [00:26:20]:
That's that's a a realistic outcome right now. So companies, at least, that are ahead of the curve have realized that. So they're saying, okay. Well, I don't want an average person compounding their average outcome. Right? Like, I want smart people. So companies are paying more to get people in there because, yes, they are augmenting and, you know, amplifying themselves. In domain, it's kind of this sweet spot. And, you know, studies have also shown that workers with AI skills are earning 56% more than those without.
Jordan Wilson [00:26:51]:
And then last, kind of the sweet spot here is domain expertise plus AI fluency is the highest value combination in any field. Right? So it is harder, and I think it will be harder for younger workers to quote, unquote go the traditional corporate ladder route and break into that upper echelon. I think it's gonna be increasingly more difficult. I think it'll be easier for them to start their own thing. Right? But that's kind of the sweet spot. If you do have, you know, ten to thirty years of experience and you still have a decade or three to go, right, and you are, practicing AI, that's a great spot to be. So number one, if you find yourself in that category, even though you may feel unease, right, with what's happening, with AI, you're in a good spot. Because the new jobs are not as out of reach as you think.
Jordan Wilson [00:27:47]:
I think a lot of people when when you think about AI job growth in jobs that maybe don't exist yet, who's gonna be qualified for them? If not us, right, if not the people who have been using AI daily since 2023, 2024. Right? A lot of people think that there is this class of world class engineers that are gonna take all these few no. Right? Large language models, yes, they've been around for, you know, more than ten years, but for the most part, they've only been popularized now for four. Right? So if you've been using them, you are in that kind of, you know, 1% of world class experts. So I think these emerging roles, including things like context engineer, agentic orchestration, AI auditor, GEO strategist. Right? The barrier has dropped for these roles significantly, and I think it rewards and it leans more into that domain expertise over, you know, engineering ability or coding ability. Right? I think it's telling, when you've seen literally some of the smartest engineers in the world, the people who built the very systems that we use, are now saying that they're essentially vibe coding. Right? And because the models are building themselves.
Jordan Wilson [00:29:06]:
So even the people at Frontier AI Labs, they're saying, I don't write code anymore. Right? You know what they're doing? The same thing that we're doing. They're orchestrating agents. Alright? Which what does that require? It requires domain expertise, and it requires, I think, great communication skills. It requires ongoing education, but it doesn't require, you know, a a PhD in machine learning. So here's your three step survival guide as we wrap up. Number one, audit your role. Separate those daily tasks into what is tacit and what is codifiable.
Jordan Wilson [00:29:45]:
That doesn't mean that, you know, oh my gosh. My job's, you know, all codifiable things. These are things that, you know, AI can do. No. You need to know, where you stand because that's gonna help you decide what path you should ultimately take. Then step two, you need to document your reasoning. You need to write down, you know, your day to day, how you make these hard decisions, things that maybe don't show up in spreadsheets and presentations and, right? So, outside of the codifiable, where is that tacit knowledge? Where is your domain expertise that maybe doesn't show up, in a in a easily quantifiable way? Right? This is kind of your domain reasoning, your expert reasoning. This is something I've been very bullish for companies to do.
Jordan Wilson [00:30:32]:
Right? But you need to also do it on the employee side, because as more and more skills become, agentified, you really need to amplify and invest more in those areas where, well, where's your reasoning? Right? Where's your domain reasoning? And then last but not least, I think you need to double down on one AI platform deeply until it becomes an extension of your own expertise. Here's what I mean by that. I think it's so easy to get caught up in the AI tool or trend of the week. Okay? You can't do it. You will fail. I don't care who you are. I can't do it. Two years ago, I could.
Jordan Wilson [00:31:17]:
The innovation now is too fast for any one person. Right? Like I said, couple years ago, fairly easy to keep up and sharpen your skills on the AI tool or trend of the week. You can't do it anymore. Find that one area that you can cross over with your domain expertise in that AI platform. Dig in there and regardless of how AI jobs, how this thing shuffles off, when the AI labor shift hits the fan, you, if you follow this survival guide today, are gonna at least be in a better position than the overwhelming majority of people who are just gonna be looking in the air confused saying, what do I do? Now you know exactly what to do. So I hope this version of the start here series volume 13 was helpful going over the AI labor shift, when it'll happen, and what it means for jobs. So like I said earlier, I put together a ton of resources because I know one thing. I know a lot of people feel uneasy about this.
Jordan Wilson [00:32:25]:
Right? Understandably so. So I had a lot more information I wanted to pack into the show, but I want these start here series to be, like, twenty five, thirty minutes, not sixty minutes. So we have a ton more. So go repost today's show on LinkedIn, and I will send you all of the additional, assets that we put together as compliments and supplements to this episode. So if you're wondering where's the LinkedIn show, well, if you're listening on the podcast, go check the show notes. There's always something that says join the conversation on LinkedIn. Right? Just click that. That's gonna take you to this LinkedIn live stream for this very show.
Jordan Wilson [00:33:02]:
Click the repost button, and then I will send you all of these additional assets. Then when you're done, make sure you go to starthereseries.com. That is going to give you free access to our private hidden, you can't find it anywhere else, our inner circle community. And in the start here series circle, their space, you can go listen to, every single, volume in this series. So I hope this was helpful. Tell me about it. Do you want more of these? How long should we keep these things going? I work for you. Let me know.
Jordan Wilson [00:33:38]:
So thank you for tuning in. I hope to see you back tomorrow and every day for more everyday AI. Thanks y'all. If you're leading AI at your company and your employees are barely scratching the surface of what AI can actually do, you need a better plan. Section coaches employees on real role specific use cases, tracks adoption across your entire organization, and helps you prove the ROI to your CEO, your board, or whoever's asking. That's the job. Section helps you do it. Check out more on Section at sectionai.com.