Who Will Adapt Best to AI Disruption?

Today on the AI Daily Brief, a new study that looks at who is best suited to deal with AI driven job displacement. And before that in the headlines, OpenAI joins Microsoft in making new commitments to the communities in which they're building out AI infrastructure. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors, kpmg, zencoder, Robots and Pencils and Super Intelligent. To get an ad free version of the show go to patreon.com aidaily Brief or you can subscribe on Apple Podcasts and if you are interested in sponsoring the show send us a Note@ SponsorsideailyBrief AI lastly, if you want to up your skills, it is not too late to join our New Year's AI resolution. We've got over 5,000 people participating now. You can find all about it@aidvnewyear.com welcome back to the AI Daily Brief Headlines Edition. All the Daily AI news you need in around five minutes. We kick off today with the latest AI company to commit to making sure their data centers are good neighbors. Recently we've been tracking what hopefully becomes a wave of commitments from companies that are building these data centers to ensure that there aren't negative externalities for the communities in which those data centers are located in. If you are a regular listener, you will know that I think this is coming too late, but I am glad to see it happening and frankly I think that we should aspire not just to not being disruptive, but to actually being a positive partner. The latest company to make these commitments is OpenAI, who in a blog post introducing the new initiative which they call Stargate Community, they Across all our Stargate Community plans, we commit to paying our own way on energy so that our operations don't increase your electricity prices. They noted that every community will require efforts tailored to their unique conditions, but OpenAI said that their plans could include bringing their own power resources or paying for local grid upgrades. Turning to water use, OpenAI said their impact would be minimized by using modern closed loop or low water cooling systems. They wrote that these are innovations in cooling water systems designed that drastically reduce the water use. Compared to traditional data centers, water required by our facilities should be a fraction of the community's overall water use. For their first data center in Abilene, Texas, they quoted the local mayor stating that a year's worth of water use for the data center would be half as much as the county uses in a single day. In addition, OpenAI is committing to regional workforce development in the communities around their data centers by establishing OpenAI academies. They said this initiative would include credentialing and clear pathways to high quality jobs aligned to local employers and the regional AI industry. The company said that they would also engage with local labor unions and other partners to support the skilled trades required to build and operate the facilities. The post concludes Stargate is a physical infrastructure program that requires deep partnership. We are reliant on and grateful to the communities that make it possible and we're committed to showing up as long term partners. In a somewhat related story, the White House is pushing for an emergency power auction in the Northeast to deal with spiraling energy costs. Last Friday, the administration published a plan to require tech companies to indirectly fund the construction of new power plants. In agreement with multiple state governors, the White House intends to compel the nation's largest grid operator, PJM Interconnection, to hold an emergency wholesale power auction later this year. The auction would allow tech companies to bid on 15 year contracts for new electricity generation capacity. The goal is to provide PJM with the certainty they require to accelerate construction while also moving the cost burden on to the tech companies. As the regulations currently stand, expansion is largely financed by increasing rates on existing customers. New power contracts help, but until now they've only been offered for 12 month terms. PJM services more than 67 million people stretching from the Northeast all the way to parts of the Midwest. They are currently forecasting a 17% jump in peak demand across their system by 2030. Now the involvement of bipartisan governors is noteworthy as electricity has become a major issue in multiple local elections. Democrat Pennsylvania Governor Josh Shapiro claimed that PJM has been, quote, slow to let new generation onto the grid at a time when energy demand is going up. Shapiro is up for reelection in November and energy costs have been one of the key issues on the campaign trail. The question ultimately will be whether an emergency auction will bring meaningful relief for consumers given the long lead times for new power plants. The long term nature of the plan is absolutely a welcome change, but who knows if it'll be enough to defuse the issue by November. Now, moving back to OpenAI, in addition to Stargate Communities, the company also announced their new Education for Countries program. They wrote about the need for such a program in the context of the capability overhang which we covered earlier this week. They wrote, education systems are a critical route through which this gap is closed. Studies project that by 2030 nearly 40% of the core skills workers rely on today will change, driven largely by AI by embedding AI tools, training and research into the core infrastructure of schools and universities. And education systems can evolve alongside these shifts and better prepare students to thrive in a world with AI. The program will see OpenAI work with foreign governments and universities to bring AI into education systems, and in addition, OpenAI will provide tailored training through their OpenAI Academy and certification system. The first cohort of partner countries includes Estonia, Greece, Italy, Jordan, Kazakhstan, Slovakia, Trinidad and Tobago and the uae. Google is also doubling down on education as a major focus for their AI organization. In collaboration with the Princeton Review, Gemini can now serve free full length practice exams on demand. The feature will begin with practice sats, with Gemini providing instant feedback for students. In addition, Google has awarded half a million dollars in funding to Cal State Fullerton to support AI literacy training for educators. Associate Professor Bridget Drukin said, when teachers understand how AI systems work, including how to build, evaluate and use them thoughtfully and responsibly, they can guide students in asking good questions about technology rather than just consuming it. Now, as you can tell, all of these headlines are sort of all part of a larger story, which is society's adaptation to AI. And that, of course, has been a major topic of conversation at Davos this week as well. The latest to comment on that is Microsoft CEO Satya Nadella, who warned at Davos that AI risks losing public support if the technology doesn't deliver clear benefits to everyday people. In an interview from the wef, he said, we as a global community have to get to a point where we're using this to do something useful that changes the outcomes of people in communities and countries and industries. Otherwise, I don't think this makes much sense. In fact, I would say we will quickly lose even the social permission to actually take something like energy, which is a scarce resource, and use it to generate these tokens. Discussing AI job disruption, Nadella rejected the idea that this is something external happening to society beyond intervention, commenting, going and thinking of these as somehow living outside of the realm of human agency is probably not the right way to think about it. He's also not super convinced that AI leads to a world with no human work required. In fact, he believes the opposite. Comparing this moment to the adoption of the personal computer, Nadella said, In the early 80s, if someone had come to us and said that 4 billion people are going to wake up every morning and start typing, you would have said why? We have a typist tool and that's good enough. We don't need 4 billion people. Nadella is also a firm believer in Jayvon's paradox when it comes to AI. This axiom implies that cheaper AI will drive up demand rather than lead to a market crash, he said. If you buy my entire argument that we've got a new commodity, it's tokens, and the job of every economy and every firm in the economy is to translate these tokens into economic growth. Then if you have a cheaper commodity, it's better overall. Nadella's view is that AI is a technology that will become deeply ingrained in society and the economy rather than an external actor disrupting human flourishing, he said. All of these token factories are part of the real economy, connected to the grid, connected to the telco network. That's what's going to drive at scale, whether it's the global south or in the developed world. Two others who commented on the speed of AI disruption at Davos were JPMorgan CEO Jamie Dimon and Nvidia CEO Jensen Huang. In a very frank and stark discussion at Davos, Dimon said that companies and governments cannot ignore AI or put their head in the sand, he said. It is what it is. We're going to deploy it. Will it eliminate jobs? Yes. Will it change jobs? Yes. Will it add some jobs? Probably. It is what it is and you can hope for the world you want, but you're going to get the world you've got, he added. Your competitors are going to use it and countries are going to use it. However, it may go too fast for society, and if it goes too fast for society, that's where governments and businesses need to, in a collaborative way, step in together and come up with a way to retrain people and move it over time. Dimon gave the example of the 2 million truck drivers in America that are likely to be pushed out by autonomous driving in the medium to long term, he said. Should you do it all at once? If 2 million people go from driving a truck and making $150,000 a year to a next job that might be 25,000, no, you will have civil unrest. So phase it in. Now playing his role at Optimist in Chief, Nvidia CEO Jensen Huang argued that labor shortages rather than mass layoffs were going to be the issue that society needs to face, he said. Energy is creating jobs. The chip industry is creating jobs. The infrastructure layer is creating jobs. Jobs, jobs, jobs. This is the largest infrastructure buildout in human history. That's going to create a lot of jobs. He also reiterated a point that he's made before, that this particular tech revolution was actually creating a ton of opportunity in the physical trades, he said. It's wonderful that the jobs are related to tradecraft, and we're going to have plumbers and electricians and construction and steel workers in the United States. We're seeing a significant boom in this area. Everyone should be able to make a great living. You don't need a degree in computer science to do so. And while some people were quick to argue that the jobs that Jensen is talking about are very temporary and will only last until data centers are built, others from very different walks of life embrace the message. Mike Rowe, who you might recognize from the Dirty Job show, talked about this discussion between Jensen and Larry Fink and said, I couldn't make it to Davos this year, but I'm delighted to see that my message has Obviously our workforce is nowhere near ready for what's coming. In fact, we're not ready for what's already here. We're going to need to dramatically rethink the way we train the men and women who will build the infrastructure in question and the speed with which we do so. I'm heartened and encouraged to see Silicon Valley at the table now. Of course this debate is going to continue and in fact AI disruption is also the topic of our main episode, so that is going to do it for the headlines. And to that other topic we will now move. Sure, there's hype about AI, but KPMG is turning AI potential into business value. They've embedded AI in agents across their entire enterprise to boost efficiency, improve quality, and create better experiences for clients and employees. KPMG has done it themselves. Now they can help you do the same. Discover how their journey can accelerate yours at www.kpmg usagents. That's www.kpmg.us agents. If you're using AI to code, ask yourself, are you building software or are you just playing prompt roulette? 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Agent readiness audits are specifically designed to help you figure out one where and how AI and agents can maximize business impact for you, and 2 what you need to do to set up your organization to be best able to leverage those new gains. If you want to truly take advantage of how AI and agents can not only enhance productivity, but actually fundamentally change outcomes in measurable ways in your business this year, go to be super AI. Welcome back to the AI Daily Brief. Very clearly, one of the big Topics heading into 2026 is AI related job disruption. It has been a major topic at the World Economic Forum at Davos. It's something that is clearly on the minds of people across the US and especially in an election year, seems like it could start to become a political issue as well. Now, one of the things that we've seen with some frequency is studies that try to measure the amount of exposure that different jobs or professions have to AI disruption. In other words, which jobs are most likely to be disrupted versus which jobs are least likely to change and be disrupted? A new group from the National Bureau of Economic Research argues that that is actually missing one of the key questions. We need to not only know, the authors suggest, which roles are most susceptible to disruption, but how adaptable different categories of workers are if that job displacement should come. For them to figure this out, the study creates a novel measure that they call adaptive capacity. Adaptive capacity includes a few different factors the first is liquid financial resources. This is perhaps obvious, but the authors write workers with greater savings weather economic storms more effectively. They point to a 2008 study that shows that individuals with greater liquid savings are less financially distressed after job loss and take longer to find better matching jobs. Low wealth individuals, on the other hand, are sometimes forced to take whatever they can get, leading to lower quality employment. The next factor of adaptive capacity is age. They point to a 2017 study that showed that workers aged 55 to 64 who who experienced job loss during the Great Recession were significantly less likely than those who were aged 35 to 44 to find new employment afterwards. And the difference was about 16 percentage points. Everything from retraining to relocation to switching occupations is more difficult for that older cohort. This leads overall to job loss for older workers, leading to greater earnings losses and lower re employment rates overall. A third factor that the authors include is geographic density. And once again, this might seem a little bit obvious, but they point to a 2012 study that found that workers who were in more densely populated areas basically think big cities, had less challenge making work transitions compared to those who were in comparatively low density areas. And intuitively, again, this makes sense. More densely populated areas are going to have more jobs, which means more opportunities for those who lose their jobs. The last factor that they consider is skill transferability. When a person has skills that can be applied across many different jobs, that creates more occupational mobility than if you have a highly specialized skill set. Once again, the authors point to a 2016 study, this time showing that individuals with higher skills transferability had smaller earnings losses after displacement. The authors acknowledged that there are some other things that could impact adaptability, such as income and union representation. But they argued that based on the literature in previous studies, those were less conclusively linked to better outcomes, and so decided to focus on these four areas. The authors then combined a set of six primary data sets to create a composite measure of adaptive capacity by occupation. They looked across something like 350 jobs, representing about 96% of American employment. From there, they were able to group people into four different categories. Basically, they looked at the adaptive capacity index, this new measure that they were creating, and an AI exposure index about how susceptible to disruption their profession was. One quadrant then, in many ways, the most desirable, at least based on the terms of this study, were jobs that had both high adaptive capacity as well as low vulnerability. On the other end of the spectrum were jobs with low adaptive capacity and high vulnerability. In the middle, of course, were roles that have high vulnerability, but also high adaptive capacity or low adaptive capacity, but also low vulnerability. Summing up their findings, the authors write, on average, highly AI exposed workers appear well equipped to handle job transitions relative to the rest of the workforce. Yet 6.1 million workers still face both high exposure and low adaptive capacity. Basically, they found that the quadrant of workers who had high vulnerability to AI disruption but also a high adaptive capacity represented around 26.5 million workers. This included occupations such as software developers, financial managers, lawyers, and in their words, other professions that benefit from strong pay, financial buffers, diverse skills and deep professional networks. Given that, the authors write, these well positioned workers who observers often cite as being highly threatened by AI automation, likely possess relatively strong means to adjust to AI driven dislocation if it were to occur. The group instead that they are most concerned with is the 6.1 million workers who face both high exposure to AI disruption and low adaptive capacity to manage a new job transition. The authors write, many of these workers occupy administrative and clerical jobs where savings are modest, worker skill transferability is limited, and re employment prospects are narrower. Meaning of course, that if those folks experience AI related job loss, they're likely to be at risk of lower reemployment rates, longer job searches, and more significant relative earnings losses as compared to others. Now, one of the critical findings is that among this group with high exposure and low adaptive capacity, 86% of these most vulnerable workers are women. Whereas some of the occupations like software developers, financial analysts and web developers and marketing managers have high exposure. They also have diverse skills portfolios, they tend to work in dense metro areas, and they have liquid net worth that can be in the hundreds of thousands. There are also geographic patterns in the vulnerability. The authors find that vulnerability is concentrated in places like college towns and state capitals that have lots of administrative positions that are supporting institutions. Places like Laramie, Wyoming Stillwater, Oklahoma, Springfield, Illinois and Carson City, Nevada have something like 5 to 7% of their local workforce. Being in this high vulnerability category. Now, I think this is super interesting analysis, but one of the things that stands out to me is that when it comes to measuring adaptability, a lot of the measures that they're using presuppose a world where there are a similar number of other jobs to adapt to. In other words, my concern is that it might be underestimating the structural changes to work. Now, the authors do note this. They acknowledge in their Limitations section somewhat in passing that if AI quote fundamentally reshapes the economy, these historical relationships may not hold. But I think that that caveat understates the problem. Every component of the adaptive capacity index is calibrated in a world where displacement is localized and destination jobs exist. It basically models AI disruption as a larger version of a plant closure or a trade shock. Discrete localized events where affected workers transition into an otherwise stable economy. Pretty much all the historical evidence they cite describes exactly that type of scenario. AI, of course, could work differently if it affects cognitive task categories rather than specific firms or industries. You could see things like simultaneous pressure across related occupations. The secretary, customer service rep, insurance claims processor and office clerk all face exposure at once, meaning they can't absorb each other's displaced workers. We could also see some pretty significant shifts in skill complementarity. The framework assumes skills are either transferable or not. But AI might make some skills radically more valuable while devaluing others entirely. Transferability becomes a moving target. The question is basically, what if there's no adaptive capacity that prepares you for a world where the category of work you do is being structurally reduced rather than shifted? The framework that the authors provide can tell you that a 58 year old medical secretary in Springfield, Illinois with 3,000 in savings is going to struggle more than a 32 year old software developer in Seattle with 200,000 in liquid assets. What it can't tell you is what happens if there's simply less demand for human cognitive labor in aggregate. And so does that mean this isn't useful? I would argue that no, it is still in fact useful for a very specific reason. Effectively, what these authors are helping provide is triage policy during a transition. Even if there is total structural disruption, human and institutional inertia is likely to draw it out over some time and net. Net. What this research can help show is that the most vulnerable groups identified here might need the fastest and most direct policy response. Even as we figure out the full extent of the disruption, there is a strong argument to be made. In other words, that whatever the endpoint looks like, whether it's structural transformation, a more modest acceleration of existing automation trends, and whatever happens on job creation on the other side, the sequencing of the disruption will likely follow something like the vulnerability gradient that this research identifies. If the conventional framing of this would have been we'll help these workers transition to growing occupations, the more honest version might be, well, we don't know what the labor market looks like in 10 years, but we do know that these workers will face income disruption first, have the least capacity to self insure, and are concentrated enough geographically to reach efficiently. We should get resources to them fast while we figure out the rest. A lot of what you are hearing me advocate for on this show is less fanciful imagined discussion and much more direct and discrete policy discussion. And I think that the findings in this study can contribute to exactly that sort of discrete policy analysis. I will link to the study in the show notes so you can go check it out for yourself. But for now that is going to do it for the AI Daily Brief. Appreciate you listening or watching as always and until next time, peace.

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