AI & Jobs: Disruption Now, or Not Yet?

In this episode, Gary and Scott unpack one of the most critical questions for business leaders today: Is AI actually disrupting the labor market—or are we still waiting for impact to show up in the data? 

They dive deep into Yale University’s Budget Lab study, “Evaluating the Impact of AI on the Labor Market: Current State of Affairs” (October 2025), which concludes that there has been no discernible economy-wide labor disruption since the launch of ChatGPT in late 2022. Using decades of labor data, the Yale team found that the pace of occupational change today looks remarkably similar to earlier waves of innovation like the PC and Internet eras. 

But Gary and Scott don’t stop there. They explore contradictory findings from other top institutions: 

• Stanford’s Digital Economy Lab (Aug 2025): Early-career workers in AI-exposed jobs have seen employment drop by roughly 13%, signaling localized disruption. 
• IMF (2024): Up to 40% of jobs globally are exposed to AI, especially in advanced economies. 
• OECD & WEF (2024–25): AI is already reshaping skills demand, with executives expecting major restructuring by 2030. 

Throughout the episode, Gary and Scott translate these insights into an executive playbook for 2025: 
✅ Build an internal AI exposure map by task. 
✅ Track real adoption and productivity telemetry. 
✅ Reinvent early-career roles through apprenticeships. 
✅ Reinvest AI gains into upskilling and responsible adoption. 

The takeaway?
No broad labor shock yet—but localized tremors are real. 
The smartest leaders are already using data to navigate the gray zone between augmentation and automation. 

Referenced Research: 

• Yale Budget Lab (2025): Evaluating the Impact of AI on the Labor Market: Current State of Affairs 
• Stanford Digital Economy Lab (2025): AI Exposure and Early-Career Employment Effects (working paper) 
• IMF (2024): Generative AI and the Future of Work 
• OECD Employment Outlook (2024): AI, Skills, and the Changing Labor Market 
• World Economic Forum (2025): Future of Jobs Report 

Takeaway:
AI is transforming how we work, not yet how many of us work. Stay adaptive, build visibility into your workforce data, and lead with metrics—not headlines. 

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website: 

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:  

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog

Transcript

00:00
Welcome to the Macro AI Podcast,  where your expert guides Gary Sloper and Scott Bryan navigate the ever-evolving world of artificial intelligence.  Step into the future with us  as we uncover how AI is revolutionizing the global business landscape  from nimble startups to Fortune 500 giants.  Whether you're a seasoned executive,  an ambitious entrepreneur,

00:27
or simply eager to harness AI's potential,  we've got you covered.  Expect actionable insights,  conversations with industry trailblazers  and service providers,  and proven strategies to keep you ahead in a world being shaped rapidly by innovation.  Gary and Scott are here to decode the complexities of AI  and to bring forward ideas that can transform cutting-edge technology  into real-world business success.

00:57
So join us,  let's explore, learn  and lead together.  Welcome back to the Macro AI podcast. I'm Gary Sloper. And as always, I'm joined by my cohost, Scott Bryan. Today, we're diving into one of the most talked about and misunderstood topics in business right now. Is artificial intelligence really disrupting the labor market? Yeah, this one's fascinating. It is a big question mark out there  because

01:23
everywhere you look, LinkedIn, the news, board meetings, you hear the same anxiety is AI is going to eliminate millions of jobs. But a brand new study out of Yale's budget lab paints a little bit of a different picture. Yeah, exactly. The report published October 1st, 2025 is called Evaluating the Impact of AI on the Labor Market, Current State of Affairs. And their conclusion is really crystal clear. So far, there's no

01:53
economy-wide evidence  of any disruption from AI. Yeah, exactly.  Really 30-something months, 33 months after Chet GPT launched,  the  data say  the labor market looks remarkably stable.  But today, uh we're going to take look at that from a couple of different angles.  What Yale found,  why does it matter, and what some other studies like uh Stamford's Digital Economy Lab and the IMF and the OECD uh

02:23
what they say might be coming next. So whether you're a CEO, a CHRO, or just trying to understand how AI fits into your workforce strategy, this episode should be a good one for you. And we will be following these reports closely and summarizing reports on the Macri podcast in the future episodes, so it won't just be today. Yeah,  obviously a hot topic, so we'll stay on top of that, keep the listeners updated with findings that come out. um But let's start with what Yale

02:51
actually found what's actually in the Yale study itself. uh So it's the work of Gimbel, Kinder, Kendall, and Lee from the Yale Budget Lab. And they asked a pretty simple question. Has the mix of jobs in the United States changed faster since the rise of generative AI was their question? Yeah. And what was interesting is their answer, surprisingly, is no. They looked at the occupational mix of the US labor force.  I think it was from

03:21
November 2022 through mid 2025. And they were using a metric called the Duncan and Duncan dissimilarity index. They compared that period to past waves of tech innovation, which we'll talk a little bit about examples like the PC boom of the 1980s and the internet era of the 90s, for example. And so they're putting a stake in the ground saying that there hasn't been an impact.

03:48
Yeah. So it's really a period from November 22nd through mid 2025. So not quite three years, but they expected to already see a uh spike in job churn if AI was really upending work. Instead, they found the pace of change is normal. So maybe a little faster, uh but well within historic norms.  think  what was even more interesting is they tried correlating the level

04:17
of artificial intelligence exposure by industry,  based on automation indices with employment and unemployment rates. They found no meaningful relationship there, I was not expecting when I read that. Right.  And just keep in mind that obviously, like we just said, this  study was to date. So  we'll see what happens in the near future and the long term. But the bottom line from Yale is we don't see

04:46
economy-wide disruption. So not yet. And as they put it, the broader labor market has not experienced a discernible disruption since chat GPT's release. Yeah, that's a quote worth repeating. No discernible disruption. And you can't see my air quotes. uh So, you know, I it's just one thing to call out there. Yep. Yeah. And so if we peel back, you know, how did they reach that conclusion? I think that their methodology matters a lot.

05:16
They didn't rely on hype, headlines, or case studies. They used monthly data from the current population survey and ran comparisons across multiple time windows. Yeah, exactly what I was just talking about. So they went and they benchmarked the post-AI period against three control errors, the PC adoption wave, 1984 to 1989, which I remember.

05:42
Good old days. Yeah, I remember well, it's where I got exposed, right, in the PC adoption wave. The internet diffusion era, was 1996 to 2002, and then the pre-pandemic baseline, which was 2016 to 2019. So those are the three areas that they benchmarked against the post-AI period. Yeah. Yeah, and I think that's a pretty clever design. It lets them see whether the...

06:10
the rate of occupational change since chat GPT looks abnormal. And according to them, it doesn't.  Yeah. Now I would say the nuance sectors like information,  financial activities,  um, professional business services did see above average shifts, but Yale notes those trends started before gen AI took off. Yeah. And that, and I think that's a

06:37
started before GEN.AI took off. that's a big key because those are definitely areas where you think it would tick up very quickly where you can automate quite a bit. And I think they've been cautious about overinterpreting it. So the data on AI usage like prompt frequency or model adoption really isn't representative yet. So most usage stats come from OpenAI or Anthropic logs, which might

07:06
over-represent certain sectors like white-collar text-based work. Yeah, that's a good point. So I think their takeaway is modest, it's powerful. ah The macro data doesn't show a disruption, but the data itself needs to get better before anyone can really declare victory or doom in this  type of area that we're talking about today. Right, yeah, and like we said, it unfolds, we'll obviously  bring out the current data. uh

07:34
But still, they did notice a few pockets worth watching. uh So for example, uh younger workers, especially ages 20 to 24, are seeing slightly more volatility in their employment patterns.  Yeah, I've heard that from  several folks.  And that could reflect two things, either early AI displacement and entry-level rules  are occurring or just the natural cooling of a very hot post-pandemic labor market.

08:03
The authors really don't make a hard call either way, but those are other areas to consider. Yeah, there was definitely a hot post-pandemic labor market. So, could be just cooling.  But if you drill down into industries like tech, media, and professional services, like you mentioned,  the exposure is  clearly higher. ah But again, so far, that exposure hasn't translated into measurable  job loss at the macro level.  Yeah.

08:32
So I think the message for executives  is this, really, you know your business, don't overreact to anecdotes,  look at your internal data, your task composition, your hiring  mix, ah turnover, before you really assume AI is upending your organization, because you know it very well. So there's the data, and then there's the tribal knowledge, and try to make your decision that way.

08:59
Let's take a look at the contrasting views. So kind of flipping the coin. Obviously Yale isn't the only voice in this conversation. There'll be a lot of other voices jumping into this conversation, I'm sure. Yeah. So, you know,  it wouldn't be a  discussion without some friendly competition. if you were to look at Yale's competition, what Stanford has to say, ah Stanford's Digital Economy Lab released a working paper this summer in August 2025 that

09:27
tells a more cautionary story. They tracked employment from early career workers, people aged, I think it was 22 to 25, across different occupations for their study. Yeah, yeah, that's an interesting one. What they found that in the most AI exposed jobs, employment was down about 13 % relative to peers. And that's a big number, even after adjusting for...

09:54
uh education, geography,  and other macro trends. Yeah, that's a point.  And the effect was concentrated in roles where AI is more likely to automate rather than augment. So think of roles such as  data entry or junior copywriting and  some entry-level coding tasks and roles.  That's  something to keep  in the back of your mind as we kind of go through this.

10:22
Yeah, a lot of those post-colors types of jobs for sure. um And then you have the IMF's global analysis  from early 2024, so ah early last year. They estimated that 40 % of jobs worldwide are exposed to AI in some form. ah So with advanced economies like the US, Western Europe, Japan are the most vulnerable uh right out of the gate.

10:50
The OECD's employment outlook 2024 made a similar point to that one, Scott. So unemployment is still low, but AI is clearly reshaping skill demand. I wrote that down. So while jobs aren't vanishing, the skills required to do those jobs are shifting very quickly. Right. Yep. And finally, the World Economic Forum's Future of Jobs 2025 report. It's not about

11:20
actual disruption yet. It's about expected transformation. So employers anticipate major restructuring by 2030. So that's the window that they were expecting. And then in particular around information processing and administrative roles, kind of like some of those roles that we've been mentioning. So if you look at the emerging picture, we'll start at the top from when we spoke at beginning of the episode, Yale says no broad shock yet.

11:50
ah to the outlook. Stanford says,  localized pain is happening. IMF and OECD say the exposure is massive. And WEF says leaders are planning for big changes. So it's a little bit across the board, but ah I guess getting back to what we said before, you really have to look at what's going on  and  transforming in your organization. Yeah. So let's  think about what

12:19
business leaders should do and translate that  into action.  And it goes along with a lot of things we've been talking about on the show. So if you're a business leader,  hearing both sides, what do you do? Well, I always say first measure intelligently. Don't just look at job counts, look at tasks, break each role into the task artificial intelligence can augment  or automate or leave untouched. And that's your initial exposure map.

12:48
to start there by measuring intelligently. Yeah. And I'd say second, uh track adoption metrics. Are your teams actually  using AI tools? How are they using them? uh What is the productivity gain or the risk?  And set up dashboards to monitor usage and quality over time. Yeah, that's a good one. I also think too, when you get to the quality, right?

13:15
Leaders need to focus on skills, not titles. think they need to move to a skills first hiring model. If a role is partially automated, shift those people into upskilling tracks, know, prompt engineering, workflow design, data validation, ah AI oversight. These are the things that will not only help your organization, but if you have folks internally that are willing to upskill themselves, that just builds great culture.

13:46
Yep, agreed. And then I think this  would be the fourth point,  and I think it's a critical one. uh Protect the early career pipeline. So that's where the Stanford study suggests that the pain is emerging. So consider  AI apprenticeships  or hybrid mentorship programs that can help  some of your new grads  adapt.  Yep. I think the last one to round it all out, which every executive always wants to understand is, you know,

14:15
return on investment discipline, use AI savings to reinvest in re-skilling,  treat productivity wins as a talent fund, not as a cost-cutting jackpot. This can really help shape your organization from an ROI perspective. Yep, yep. And I think there'll be a lot more studies coming out pretty quickly about some of the ROI and  AI savings as some of the...

14:41
especially the larger corporations like I think I heard one from IBM this morning. I have the stats in front of me, but they've been seeing some pretty major internal AI savings. So we'll be hearing some more about use case ROI.  Oh, every CFO and board will absolutely want to understand that because that's how they can really relate and understand that this is being impactful.  agree. um So we I know we had written down some some Q &A so let's

15:10
Let's just do kind of a quick rapid fire Q and a abbreviated lightning round session. Gary. All right. All right. So why, why do you think the macro numbers still look calm while the micro pockets are showing some strain? Diffusion lag,  big tech adoption waves always start in niches. uh It takes years for displacement to show up in the aggregate data. I think we're seeing that here. ah It just takes time. Yep.

15:40
I agree. I think it's just going to take a little bit of time. Plus, think a lot of AI is still in the augmenting stage and not fully replacing work. So think of accountants using AI for reconciliations or lawyers using it for drafting. I would put that in the category of productivity enhancement and not elimination. Agreed. Agreed. That's a good point.

16:08
Augmentation of these tools are helping those types of professions move faster. Yep. you know, so another question, what data would give us better visibility out of this? Uh huh. Um, so good one. I think, uh, we need more granular, uh, privacy safe usage telemetry from all AI platforms. And there are a lot of tools and products that are coming out to, to give that type of usage telemetry. So not.

16:37
Not just open AI or anthropic. We also need task level with age and productivity panels  across multiple sectors.  Yeah, it's a point. think right now we're driving with fog lights on seeing some bumps, but not the whole road in front of us. Yep. Good point.  So let's, let's just kind of just deep dive into a couple of sectors for a minute.

17:04
ah Let's just take two sectors. First,  say professional services. Yeah, I think we know that one pretty well because we live it every day. ah Consulting,  law, accounting,  all are seeing workflow augmentation.  But the junior tiers,  think of analysts, paralegals, are being redefined. That's where firms should invest in retraining early. Again, it will allow you to get out in front of this much faster. Yep.

17:32
And I think a lot of folks are kind of think about this all the time because they're on their devices, but let's, let's take a look at media and creative here. AI tools are every day you hear something new and they're rewriting the rules literally. So, you know, prompting, editing QA and curation are some of the new hot skills. Yeah. I mean, if you even look at what's going on with search.

17:59
engines  and optimization and more people are going to  You know the LLMs to ask questions are not going to the traditional search engines. So the media and creative teams have to Adjust to that which I think we're seeing with a lot of the traffic hits  across the the SEOs  And in financial services, it's moving slower because of compliance  but AI copilots for analysts and claims

18:27
ah processors are coming much faster and  that incorporates contact centers and other things.  that's another area that I think a lot of folks haven't really thought about.  Exactly. I think once some of these new tools plug in for compliance, think then there'll be a rapid acceleration in some of these other sectors for sure. uh So I think it's probably good point to start to wrap it up. Let's...

18:53
Gary, let's close with a checklist for business leaders  that are trying to kind of navigate this transition. Yeah. I think if we kind of summarize what we talked about today, ah it's probably four or five key points. So number one, build a task inventory and artificial intelligence exposure map for your organization. ah Two, stand up usage telemetry. You just mentioned that Scott, which I think is excellent. know, privacy safe, of course.

19:23
re uh pilot co-pilot first programs uh with obviously measurable outcomes. uh I'd say for  fund re-skilling, especially at the entry level, no better way to show your organization that AI is not scary by reinvesting. Yeah. It takes some of that ROI, play it right back into re-skilling. Yeah. Yeah. I mean, that's an easy one. And then I'd say the last one, know, number five, review, you know, the  AI impact.

19:52
quarterly to your business, not just annually.  And maybe for some organizations, it's a monthly review. So don't be afraid to continue to see how it's impacting your organization. Yep. Agreed.  I think right now the story isn't, you know, mass layoffs, it's mass adaptation. And we'll see how things  change over the next coming years, up to 2030, but  really it's, we're at the adaptation point.

20:18
Yeah, and we're going off the evidence that we talked about today. There's no broad labor shock yet. ah Maybe some localized tremors are real, but  to your point, it's mass adaptation. Yep,  and always lead with the data.  as like I said a couple of times, as we find more, we will roll it out on the podcast. Absolutely. Well, that's it for the episode today on the Macro AI Podcast. If you found this useful, us on LinkedIn.

20:47
check out the full Yale Budget Lab report we referenced today and stay tuned for our next episode where we'll explore how AI copilots are redefining productivity metrics.  Yep, and we'll put the links to those reports in the show notes. So everybody, thanks for listening and until next time, keep staying curious and also data driven.