Ep 730: Is AI creating a great recession for white collar workers? Inside Anthropic’s labor report

Summary

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Everyday AI Podcast – Ep 730:

Is AI Creating a Great Recession for White Collar Workers? Inside Anthropic’s Labor Report

Date: March 10, 2026
Host: Jordan Wilson (with brief co-host segments)

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Episode Overview

In this episode, Jordan Wilson dives into the findings of Anthropic's recent AI labor report, which investigates AI’s present and future impact on white-collar employment. While headline news focuses on the absence of mass AI-induced unemployment, Jordan reveals the deeper, more nuanced threat: a significant reduction in new job opportunities for young, educated workers, and a growing “capability gap” between what AI can do and what organizations actually leverage. This conversation challenges listeners to understand the subtleties of AI’s labor disruption and lays out actionable insight for staying competitive amid rapid change.

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Key Discussion Points and Insights

1. Headline vs. Reality: The Absence of Mass AI Unemployment

[01:00-04:15]

• Anthropic’s study—one of the most comprehensive to date—tracks U.S. employment data from 2016 through the “post-ChatGPT” era.
• The main finding: “There’s no mass AI unemployment… AI isn’t taking millions or tens of millions of jobs yet.”
• Even in fields where AI could theoretically automate most work, actual AI-induced layoffs or spikes in unemployment are minimal so far.

2. The Underemployment Threat & The Quiet Drop in Junior Hiring

[04:15-10:00]

• The most significant effect observed isn’t unemployment, but underemployment among young, educated workers.
• “Hiring of workers age 22 to 25 into AI-exposed fields quietly dropped by roughly 14%.”
• Companies are increasingly “quiet hiring” (i.e., not replacing junior roles and not backfilling with entry-level hires).
• Baby Boomers (“the Silver Tsunami”) are retiring, but their jobs aren’t being refilled with younger workers.
• Jordan:

  “That 14% drop, if that were an unemployment rate, would be a global disaster… But for the younger generation, companies just aren’t hiring anymore.” ([07:45])

3. Who is Most at Risk? Shifting the Narrative from Blue- to White-Collar Risk

[10:00-13:45]

• Contrary to earlier expectations, it’s not blue-collar jobs most vulnerable to AI, but educated, white-collar, higher-paid roles—especially those in front of screens.
• Jobs with the highest theoretical AI coverage include management, business/finance, computer/mathematics, legal, arts, and media.
• The most exposed roles right now:
  • 1. Computer Programmers (74.5% exposure)
  • 2. Customer Service
  • 3. Data Entry
  • 4. Medical Records
  • 5. Marketing Analysts
• These positions are automated readily through APIs and software workflows at scale.

4. The AI Capability Gap: What AI Can Do vs. What It’s Used For

[13:45-16:00]

• Anthropic’s study illustrates a stark “capability gap”:
  • E.g., in “computer and math” roles, AI could theoretically perform 94% of work, but is actually only observed doing 33%.
  • Even in top-exposed fields, observed AI use rarely exceeds 40%, often much lower.
• Jordan:

  “Even in those fields, the actual observed AI coverage was low…less than 20% in many instances.” ([12:58])
  “People still are looking at AI like a fun little chatbot, not realizing its agentic nature… It can probably do the majority of your work and you just don’t know it.” ([13:38])

• The capability gap is partly due to lack of awareness and training, not technical limits.

5. Longer-Term Outlook: The White Collar Great Recession Is Still Coming

[16:00-18:30]

• The danger isn’t averted—it’s delayed. Jordan forecasts a rapid closing of the capability gap as businesses catch up, likely by late 2026 into 2027.
• He calls the current phase a “9 to 18 month threat window.”
• Jordan:

  “We are going to see the great white collar work recession. It is a lagging factor…That 33% observed coverage in math and computer right now, that’s going to go up: 40, 50, 60 [percent] in the coming months and quarters.” ([15:05])

6. Younger Workers Are Uniquely Squeezed

[18:30-20:30]

• New grads (late 2024-2026) face historic challenges; most are “getting squeezed out” of their fields.
• Many are taking jobs outside their areas of study—not due to lack of skill or education, but lack of openings in AI-impacted sectors.
• There’s concern that the lack of junior hiring today will lead to future shortages in skilled middle management.
• Jordan:

  “If you graduated between late 2024 and 2026 and have a full-time job in your area of study, consider yourself very lucky because you are in the minority…” ([16:47])

7. Methodology: How Anthropic Generated These Findings

[20:30-22:45]

• Anthropic combined:
  • The U.S. federal O*NET database (800+ occupations, 20,000 tasks)
  • U.S. Census employment surveys
  • Millions of anonymized chats with Anthropic’s Claude chatbot, mapped to specific work tasks
• AI capability was scored as follows:
  • 1: AI alone could double a worker’s speed (no extra tools)
  • 0.5: AI could double speed, but only with extra tools
  • 0: AI could not help meaningfully
• 68% of real-world Claude usage targeted “score 1” tasks, i.e., tasks AI excels at
• Only 3% of usage attempted impossible tasks (score 0)

8. Corporate Greed & Rising Automation in Expensive Roles

[22:45-24:00]

• Most-exposed jobs pay 47% more per hour than those with zero AI exposure.
• Mass layoffs attributed to AI have already hit major firms (Amazon, Meta, Salesforce, Accenture, etc.), and are expected to ramp up.

9. What Should Workers—and Companies—Do Now?

[24:00-27:40]

• The current “capability gap” is an opportunity for individuals and organizations to get ahead.
• Jordan’s advice:

  “If you can close that gap first in your organization, in your industry, if you can be first to market that truly… rebuilds being AI-native… you will own the advantage.” ([26:01]) “AI will create millions of jobs…but ultimately, it’s going to change the face of traditional full-time employment…I think it will ultimately take away more full-time roles than it will ultimately create.” ([25:16])

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Notable Quotes & Memorable Moments

• Jordan:

  “Companies have already started…they’ve kind of done this quiet hiring. They’re just no longer hiring for junior people.” ([07:25])

• Jordan:

  “For the younger generation…it’s mass underemployment.” ([08:25])

• Jordan:

  “The great recession for white collar workers…hasn’t happened yet, but it is very much so on the table.” ([09:41])

• Jordan:

  “This capability gap—you need to attack it…there is still an opportunity, right now…a 9 to 18 month threat window where you can do something about it.” ([25:38])

• Jordan:

  “If this show was helpful, make sure you also go check out episodes 712 and 713, our 2026 AI prediction and roadmap series.” ([27:28])

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Timestamps for Key Segments

• [00:16] – Anthropic labor study overview and episode roadmap
• [01:00] – No mass AI unemployment…yet
• [04:15] – Quiet drop-off in hiring of younger workers; mass underemployment
• [07:45] – New grads’ career struggles; companies not hiring juniors
• [10:00] – Shifting from blue- to white-collar AI risk
• [13:45] – The ability gap: observed vs. theoretical AI capability
• [16:00] – Jordan’s predictions and 9-18 month window of opportunity
• [18:30] – New grads forced into jobs outside their fields
• [20:30] – Anthropic’s detailed study methodology
• [22:45] – Highest-paid roles are most automatable; layoffs at big tech firms
• [24:00] – Take action now: how to seize the advantage as AI closes the gap
• [27:28] – Recommendations for further listening

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Summary Takeaways

• Mass layoffs are not dominating headline unemployment rates—yet. The current real danger is underemployment and stalled career starts, particularly for young adults seeking entry-level white-collar jobs.
• The future risk is heavily weighted toward educated, higher-paid desk jobs, not blue-collar occupations.
• A massive, actionable gap exists between the actual capacity of AI and what most companies/utilities are harnessing. Those who can close this gap first—by adopting more AI-native workflows and upskilling—will gain a substantial competitive edge.
• There is a window of opportunity (9–18 months) for individuals and businesses to adapt, before disruption and layoffs catch up with AI’s capabilities.

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For Further Learning

• Episodes 712 & 713: 2026 AI Prediction and Roadmap Series
• Start Here Series (Episode 691 onward): For beginners to AI or those seeking to systematize their company’s AI adoption

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Transcript

[Jordan]

This is the Everyday AI show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business and everyday life.

Anthropic just published one of the most detailed roadmaps on AI's impacts on jobs that we've ever seen and the results are kind of misleading, especially if you take them to topically, I think that most people are getting hung up on the biggest takeaway that as of today, AI hasn't yet caused huge unemployment. But if you read beneath the surface, there's a much larger and more impactful finding from Anthropic's recently released AI labor report, it's that mass AI induced unemployment hasn't happened yet, mainly because the majority of Companies don't understand AI's capabilities. It's not because AI isn't capable yet of automating jobs, because it is. And there's one more finding that is going to hit the younger generation right now and might spell trouble for companies in the future. All right, let's get into it. I'm excited to jump into today's topic and here's what you're going to learn on today's show. Well, we're going to talk about why Anthropic says the AI job apocalypse is isn't happening, but something worse might be happening. Why AI is actually a threat to more highly educated white collar workers and not blue collared ones that we always thought automation and AI would come for. First, we're going to learn which five white collar jobs are already being automated the most right now. And we're going to dig into a little bit more on the massive capability gap in AI that almost no one is talking about and how you can actually use that to your advantage. All right, let's get into it. If you're new here, welcome. My name's Jordan and this is Everyday AI. If you're new here, this is an unedited, unscripted daily live stream, podcast and free daily newsletter helping everyday business leaders like you and me make sense of everything that's happening in the world of AI. Because it is non stop. I tell you what's important, what's not. You take that information to grow your companies and your careers. Sounds like a good trade off, right? Starts here, but make sure to go to the next level. Go to our website@your everydayai.com we break down each day's podcast as well as giving you all the other AI news that you need to know to stay ahead. Right. Like as an example, Microsoft just Released their, their new cowork. Right. Yeah, work is changing. All right, but let's get straight into something that's been dominating the headlines a lot, right? I actually, you know, at first saw this study and I'm like, okay, this is important. We shared about it in our newsletter and my wife was actually like, I'm seeing this all on my Apple news. Right. So she's seeing it everywhere. So, like, there's a certain point when AI news starts to hit the mainstream outside of our little, you know, AI, you know, closed circle here. I'm like, okay, this is worth diving into a little bit more, but let's first kind of zoom out and talk about this new study and what it found from Anthropic. So researchers tracked U.S. employment data from 2020, 2016 through, well, the post chat GPT era. And the biggest finding was, well, there's no mass AI unemployment. Right. So AI isn't taking millions or tens of millions of jobs yet. That's because while their findings over the last 10 ish years show that that hasn't happened. And the unemployment estimate for highly AI exposed workers was statistically nothing. Right. So even in areas where AI has been shown capable to automate a lot of jobs, we're not see, right. Millions of people being laid off. Right. I think early, you know, very early on in the early generative AI large language model days, you know, there's a lot of these predictions that, you know, there's going to be tens of millions of fewer jobs fairly soon. And well, we haven't seen that happen yet. And Anthropic's study, I think was one of the best that really dug into this. But here is one thing that this study found that is kind of concerning, especially if you are a younger person, but I think it has larger implications even if you are mid career running a company. And this is actually something I talked about last year on the show because it's something that I spotted a long time ago and I've been talking about it quite a bit. But this study found that hiring of workers age 22 to 25 into AI exposed fields quietly dropped by roughly 14%. Let's think about that percentage point right now because I think when people are looking at AI and its impact on jobs and unemployment, the thing that most people look at is the unemployment rate, which seems to be smart. Right. Because if AI is impacting millions of workers, well then that means the net number of Americans with jobs is going to go down, correct? Well, yes and no. Right. Because what we've seen happening. And you know, this study from Anthropic showed as much a 14% drop, right? For the most part, if you don't follow, you know, unemployment numbers like I do, you know, it, it varies by a little bit, but for the most part, you know, the unemployment rate in the US is between 4 to 5%. Aside from know anomalies like, you know, the pandemic, the financial crisis of, you know, 2028, 2009, right? But for the most part, for the last 25 years, the unemployment rate in the US has teetered, you know, 4%, give or take, right? So when people still see that 4%, they're like, okay, AI is not causing, you know, mass UN unemployment. But what I do think it has already started to cause, especially in the younger generation, is mass underemployment. Because if we had an UN unemployment rate at 14%, that would be a global disaster, right? People would be in the streets probably rioting, right? Hey, big AI, you took away my job, right? But that's essentially kind of what's happening for the younger generation. They, in those highly exposed areas at least, companies are just not hiring anymore. I've been talking about this Since I believe 2024, this process of quiet hiring, how there's been this kind of thing, you know, as post pandemic, right? More and more people are remote and hybrid. There's this thing called quiet quitting, right? Where employees, whether they're using AI, augmenting with AI or not, they're kind of doing the bare minimum and just scathing by, I think companies have already started and the, the anthropic study shows this. They've kind of done this quiet hiring. They're just no longer hiring for junior people or when people leave, right, to avoid having these, all right, we got to cut 10,000 jobs, 20,000 jobs, right? We heard reports Oracle might go up to 30,000. We've seen tens of thousands from Amazon. So for bigger companies to avoid this, well, they're just not hiring, right? So when the Silver Tsunami hits and we have millions of seniors, you know, people in their 60s retiring, well, they're just not going to refill those roles. And those younger people that are in these highly exposed areas are just not going to be able to get jobs. Also, some key findings, and we're going to dig into all of these a little bit more. But there's a massive gap, the capability gap between what AI could do versus what it's actually doing and what it's actually being used for. And the most exposed workers Right now are female educated and higher paid. So the Great Recession for white collar workers. Well, that scenario, even though it hasn't happened yet, it is still very much so on the table because that is where the exposure and the risk is. And I think with this report, Anthropic actually built an early warning system to track where disruption may show up first before any unemployment numbers can diagnose it. So let's talk about the biggest finding and that is the massive gap, right? And this kind of chart obviously went, you know, very, very viral online, right? Whether you're reading Twitter, LinkedIn or the news, you probably saw this chart. So for a podcast audience, you can always, you know, check this out obviously in the newsletter, but you can go watch the video version of this@your everydayai.com. nothing we can't describe here, but essentially this chart showed the different occupational categories, right? So everything from management, business and finance, legal, health care, food and services, personal care, office admin. Right. All these different sectors of work. And then you had a theoretical AI coverage line, right, which is in blue. And then kind of the, the spider graph that shoots out and if you, the AI could theoretically do a hundred percent of the job, then it goes all the way to, well, the exterior of this circle. So in the blue you have your theoretical AI coverage, right? And then you have your observed AI coverage in the red. All right, we're going to talk a little bit more. How Anthropic got to that essentially is a combination of U.S. employment data and millions of anonymized chats with Anthropic's Claude Chatbot. Right? So what you essentially see is, well, AI is theoretically extremely capable in many areas and right now does not have a lot of capabilities in others. Right? So some of the biggest areas where in theory, right, AI could do, you know, 80 to 90% of the work comes in fields like management, business and finance, computer and math, legal arts and media. Right? Those are areas where there's at least 80% coverage up to the mid-90s. And then there's areas, at least right now, that large language model in their current capabilities, well, they don't really touch, right. Sectors like production, installation and repair, construction, agriculture. Right. Those jobs that for the most part require you to use your hands away from a computer for the majority of the time that you're working. And that's kind of how we got to this capabilities gap. But when we talk about the theoretical AI coverage and the observed AI coverage throughout the rest of the show, the this is essentially what we are talking about it is what AI can actually do, right, According to benchmarks and AI's actual capabilities. And then the observed AI coverage, which is what Anthropic found through millions of anonymized chats. Well, what people are actually using it for. And even in those areas, right, Management, business and finance, computer and math. Nothing. Nothing hit the 40%, right? In the majority of those, even with high theoretical AI coverage, right, where, hey, technically AI could do 90% of this right now out of the box with nothing else happening, right? A lot of those areas, right, like management, legal, right. The actual observed AI coverage was low. It was, you know, less than 20% in many instances. So let's talk about one of the most obvious categories and that's in computer and math roles. So the observed capability was 90 or, sorry, the theoretical capability was 94%. So anthropic found by matching it with US jobs data that AI's capabilities right now can do 94% of tasks, but it's only being 33% observed. And that is of all the different columns, that is actually the highest observed AI coverage. So maybe you're thinking, oh well, yeah, people are just using AI for different things that maybe aren't falling on this map. Absolutely not. That is the highest observed AI coverage. And the gap there is still enormous, right? I've been talking about this capability gap. I talked about it a lot on our 2026 AI and roadmap series about this huge gap. And that people especially, I think since quarter four of 2025, they still are looking at AI like a fun little chatbot, not realizing its agentic nature, the improved scaffolding and harnessing. Well, it can probably do the majority of your work and you just don't know it. So not only is there capabilities gap that comes from training well, there's also just the education side. People don't even know. I think a lot of people understand that they maybe don't, you know, can't get behind a computer and you know, fully use a chatbot like Claude or Chat GPT or Gemini or Copilot to its fullest capabilities. But I think the majority of people, even those that follow the technology fairly closely, don't even understand what those capabilities actually are. Right? So the 61 point divide in the computer and math roles as an example, that just defines the current state of AI at work. So here's kind of my hot take I kind of already talked about a little bit. Say it's Tuesday, right? We're not doing as many hot take Tuesdays, but I'm going to go ahead and throw my hot take opinion in here. This is going to happen. We are going to see the, the great, you know, white collar work recession. It is a lagging factor, right, that 30, what was it, the 31% or, sorry, the 33% observed coverage in math and computer right now, that's going to go up. It's going to go up. It's going to go to 40, 50, 60 in the coming, in the coming months and quarters. The same thing with these other, these other areas, right? I talked about this on the prediction and roadmap series, but I think especially in anything that costs people a lot of money, right? Legal, management, business and finance, that gap is going to shrink. I think we're going to start to see measurable shrink in 2026, but I think by 2027, that gap is going to close very quickly. Quickly. I think that's how long it takes for the average, you know, US company. It takes nine to 18 months for companies to truly, number one, realize that there's a gap. I think studies like this one from Anthropic that put it into concrete terms obviously help executives in boardrooms understand that, oh wait, there is a gap, right? And you can see the methodology which we're going to talk about here in a second. But then it takes them time to start to learn to close the gap. That is, unless you and your company listen to the show every single day. Because I've been talking about this gap before Anthropic or anyone else you know, officially identified it. Granted, I didn't have millions of anonymized anthropic chats to really bring teeth into it, but this is something I've been observing, well, for the last three years since I've been doing this show.

[Co-host]

AI moves too fast to follow, but you're expected to keep up. Otherwise your career or company might lag behind while AI native competitors leap ahead.

[Jordan]

But you don't have 10 hours a

[Co-host]

day to understand it all. That's what I do for you. But after 700 plus episodes of everyday AI, the most common questions I get

[Jordan]

is, where do I start?

[Co-host]

That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's three ways to jump in. Number one, go scroll back to the first one. In episode 691. Number two, tap the link in your show notes at any time for the Start Here series. Or you can just go to Start Here series which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead.

[Jordan]

College graduates. Here's the problem. They are getting squeezed out, right? If you graduated between 20, late 2024 and 2026 and you have a full time job in your area of study, consider yourself very lucky because you are in the minority, right? And I've talked about it on a couple other shows, right, the majority. And I think one of the reasons why we haven't even seen a huge technical unemployment spike in the younger generation is, well, because they're having to take jobs outside of their college major. We talked about that in one of our, you know, AI's impact on college shows in 2020, the highest, the highest number ever of graduates are having to take jobs full time roles outside of their field of study. Well, because no one's hiring, right? There is this quiet hiring that's going on. And then not only that, when we do see these silver tsunami, the baby boomers that are going to be retiring in mass, right, One of the biggest problems is, well, what's happening with this junior generation, right? If, if companies are hiring fewer and fewer junior researchers, junior analysts. Because all that's happening is the senior people who are sticking around and maybe aren't getting laid off in mass while they're augmenting their job with AI. So they don't need as many junior people. So, well, where are the future middle management? Where are they now? And I think that's actually pretty problematic. All right, so let's quickly talk about how Anthropic got to these conclusions because again, I think this is one of the best studies looking at the kind of capabilities gap that we've seen. So they use the federal, the U.S. federal O NET database and that breaks the essentially 800 U.S. occupations into 20,000 plus specific tasks, right? So this isn't, you know, looking at broadly, right? Guessing and throwing things at the dartboard. This is 20,000 specific day to day tasks that the average American performs in those different sectors, right? So this is the US Census employment survey that tracks who is currently working, unemployed or entering the workforce was also used by Anthropic. And then the big piece here, which I already said is millions of conversations anonymized that they were able to then tie up or map to those 20,000 specific tests. And that's what kind of gave them the ability to look at AI's current theoretical capabilities. And then the observed rate. Right. This isn't guessing, this isn't a vibe. Right, mit. Right. This is an actual legitimate study that shows this huge capability gap when it comes to real world day to day tasks and a little bit more on how they actually did it. So they assigned each kind of task a score. So a score of 1 meant that AI alone could double a worker speed without any extra tools. A score of 0.5 meant that AI could double the speed but required outside tools like browsers or databases. So more augmented. And then a score of zero meant the AI could not meaningfully help. Right. So through this scoring system and matching it up, researchers scored every individual task across those 800 US occupations using that 1.5 or 0 scale. Then they checked those theoretical scores against real CLAUDE usage data to see which tasks workers are actually delegating to AI. Right. And here's the interesting part. They found that 68% of real usage landed on task scored one. So the overwhelming majority of people that were using CLAUDE were using it for tasks that AI was actually really good at and was able to fully automate. And only 29% of usage was on tasks that scored 0.5 and 3% on tasks that scored a 0. Which is interesting that 3% of people were still trying to get a chatbot to do something that it theoretically did not have the capabilities to do. So the scoring acted as a baseline prediction and the real usage data acted as that reality check. And that's where you got the huge gap between the two and how that has turned into the core of the study. So let's look at the most exposed roles. So computer program. And this is those roles where, well, AI has the highest capability. Computer programmers led with 74.5% and then routing out the top five. Right, the top five most exposed current roles. Well, one was computer programmers. Then you had customer service service, data entry, medical records and marketing analysts. So essentially any those roles are ones where you're constantly in front of a computer, you're doing things that require a screen, they're text heavy and repeatable. So this is why it's hitting that group of people who are higher paid and more educated sitting in front of a desk. That's because these jobs are automated through company API systems, not just individuals chatting with a chatbot. Right. So they can be done and automated with AI at scale. And the other thing. Right, and you have to look at the whole corporate greed thing, right? The most exposed workers earn 47% more per hour than those with zero AI exposure. So in theory, the jobs right now by today's technology that are most replaceable and automatable by AI and haven't been discovered yet are those that, well, are expensive and, but physical jobs obviously like mechanics, lifeguards, cooks, showed zero exposure. The other thing it is that young generation, right? Because the study found that senior workers can stay productive with AI while companies, well, they just have stopped backfilling junior positions almost entirely. Right? And that 14% hiring drop not only appears for workers age 22 to 25, but it's just for them, right? So it's not like there's a 14% hiring gap for any person or any group of people that had that high exposure. It was just the younger generation, right? So what that meant is people with experience, senior people who had been at companies for a while, right? They're not getting, they're not going through that 14% hiring drop because companies still want people with experience. So instead they're just not hiring any entry level people. So that doesn't change the fact that that AI induced layoffs are still happening en masse. So in 2026 already we've seen thousands of AI linked cuts hit big companies like Block, Amazon, Meta, Autodesk and Salesforce. And then in late 2025, same thing, huge. I mean we're talking in the tens of thousands for each of these companies in late 2025 attributed to AI. Accenture, Citigroup, Dell, IBM, Microsoft, right. Reportedly Oracle looking at up to 30,000 jobs. Also Intel, UPS, IBM. Right. The AI induced mass layoffs are still happening and they will, I believe continue to ramp up probably toward the latter end of 2026 and early 2027 as companies understand that gap. Okay? In that gap right now, it is an unclaimed territory. It's not a looming threat, right? So I think that workers and company, my, my, my big takeaway here, right? AI is still going to come for jobs. I've been saying since the literal very first episode of Everyday AI, that yes, AI will create millions of jobs that we don't even know exist yet. But I think ultimately AI is, well, it's going to change the, the face of traditional full time employment. I think it will ultimately, you know, take away more full time roles than it will ultimately create. But right now there is still an opportunity, right? So I don't want, you know, my message, my takeaway, my hot take on this to be one of doom and gloom because I understand that many of our listeners and viewers of Everyday AI, right, myself included, I have a great graduate degree, right? Technically high, highly educated, sitting in front of a computer doing a lot of these tasks that are highly automated by AI, right? This capabilities gap, you need to attack it, right? Because yes, you could say this is a looming threat, but right now I do think companies, departments, individuals have a 9 to 18 month threat window where you can do something about it, right? If you can close that gap first in your organization, in your industry, if you can be first to market that truly right unlearns and rebuilds being AI native in some of those areas and you can exploit it with revenue tied services, you will own the advantage. And the advantage is out there. We saw this with the Anthropic study tying real world jobs data. Real world 20,000 different tasks all mapped out through millions of anonymized claw chats. The opportunity is there if you go seize it. All right, that's it for this episode. I hope it was helpful, but big takeaway here is AI creating a great recession for white collared workers. Well, even though Anthropic's study says no today, it is definitely coming. But if you are listening to this show every day, if you're putting what we teach into practice, you can stay ahead and not be impacted and well, hopefully you can actually capitalize and take advantage. So if this show was helpful, make sure you also go check out episodes 712 and 7 13, our 2026 AI prediction and roadmap series and then go to our website@your everydayai.com Sign up for the free daily newsletter. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks y'. All.

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