EP 579: 40 Jobs Microsoft Says will be replaced by AI and 5 Underlying Trends

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. Microsoft just released one of the most consequential reports on AI's impact on jobs. And that's because it's not based on predictions or trends or forecasts. It's based on hundreds of thousands of conversations on how people are actually using AI in their jobs. And I think it's a pretty telling and kind of shocking study. So on today's episode, going to be talking about the 40 jobs that Microsoft's report says could be displaced first by AI. And I'm going to give you five underlying trends that I don't think anyone's talking enough about. All right, let's get into it. If you're new here, welcome to Everyday AI. My name is Jordan Wilson and Everyday AI is for you. 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I think it's an important one because Microsoft's report literally listed the 40 jobs that would be most likely to be disrupted by artificial intelligence, specifically based on if those users right those 200,000 different pieces of AI chats that were studied, if they actually got the right answers that were that they were looking for and if those answers were in fact just doing their job for them. So this is a pretty consequential study that we're going to get straight into. So in short, here's what Microsoft's study revealed and how it worked. So it was based on 200,000 real world conversations with their Bing slash copilot chatbot. And these aren't just abstract predictions. So researchers observed how real people like you and me are actually using their AI chatbots for their daily professional tasks. And they created an AI applicability score which we're going to go over later based on real world jobs data and their study findings that showed areas most ripe for AI disruption. And here's what we're going to be going over over the course of today's episode. So we're going to specifically talk about how they analyzed those 200,000 real world, real workplace conversations and the shocking automation patterns that they revealed. We're going to give you the complete list of 40 jobs with the highest AI task overlap scores. And I'm going to give you five pretty provocative truths and trends that I don't think people are talking about as it relates to this study. And then also tell you what you can do about it. Right? What if your job is one of those 40s on the list? What are you supposed to do? Stick your head in the sand and just wait until an AI can do your job better or you get replaced? No, I'm going to tell you exactly what you should be doing, whether your job is on this official list of Microsoft or not. All right, it's Thursday, y', all, but might get some hot take Tuesdays in here. So I'm excited for today's episode. Live stream audience do you have thoughts on this? Do you have questions? Please leave them. Love sharing those sometimes here on the screen and in the newsletter. Podcast audience Appreciate you all tuning in. Sometimes we get questions if you're listening on, you know, YouTube podcast if you want to watch the video. There's nothing overly visual today. I have some slides up on the screen. I'm going to be showing you the chart from Microsoft, but you can always go to our website if you want to watch the video version of this podcast at your everyday AI dot com. So thanks for joining us. Live stream of audience, big bogey face on the YouTube machine, as well as Bronson and Michelle, you LinkedIn crew. Thanks for dropping by. Brian, Jay, Bria Marie, Christopher, Andrew, got a big group in the house this morning. Rolando's In South Florida, McDonald, my Chicago guy, I believe. Jose joining us from Santiago. All right, let's get into it. I want to, before we get into this, I'm not going to do a, a ten minute sidebar here, but I want to tell you kind of my truths or my thoughts on AI job displacement. This is obviously a very sensitive subject. It's something that I think the majority of professional knowledge workers, which is many of us listening to this podcast. These are things that we constantly talk about or, or think, you know, might keep you up at night in some instances. Just going to give you the truth. All right? AI will eliminate more jobs than it will create, period. I've done many shows on this. I've probably read and studied more on this than most people in the world, right? I've been following AI daily for almost three years. This is my job. I talk to the smartest people in the world. I read all the most consequential studies. I advise big research groups, they reach out to me and they're like, hey, we want to understand something better before we put out a study, right? And I'm telling you, AI will eliminate more jobs than it will create. Everyone wants to paint this, you know, overly rosy picture of AI. It's not like that the future of employment will look much different in five years than it looks today. Traditional full time jobs, you know, working a 9 to 5 in front of a computer, it's not going to look the same in five years. Also, your knowledge, don't worry, your knowledge isn't going to become worthless. Your domain expertise, it's not going to become worthless, but it's going to become worth less. And it's all about how you can drive agentic AI systems with that knowledge. And you have to be pivoting into an AI first world. Whether you feel this disruption creeping up on you or not, you have to be pivoting. Now try. So let's get into this study and talk about how this worked. So Microsoft researchers analyzed 200,000 real anonymous. That's the thing, you know, it's not like they, they knew who these conversations were from, but looking at their different AI chatbots over the course of time, 200,000 real anonymous conversations. So they use an AI to identify two things in each conversation. What the human user was actually trying to achieve and what the AI itself was doing. Right? What the user was trying to get out of the AI. And did the AI essentially deliver what they were looking for? And was it directly related to their job? And they matched these actions and goals to specific work activities from a government maintained job database, encoded everything. And we'll talk about that here in a minute. And then with this information, they calculated an AI applicability score for various jobs by measuring how often AI was used for relevant activities and how successful tasks were completed and how broad AI's impact was. All right, so here's ultimately what the study found and we're going to be unpacking this more, don't worry. So they found that users most often relied on AI for gathering information and drafting text. The AI primarily provided detailed information, advice and acted as a teaching resource and a supportive role in jobs centered on knowledge creation, which is what so many people do, such as analysis and communication, education, computer science, sales, administrative support, et cetera, show the greatest potential for AI impact. And the study found little connection between a job's AI applicability and its average wage. That's something I think a lot of people have this misconception about, right? People think, oh, I'm highly educated, I have a senior role, I make a lot of money. AI job disruption is more for not frontline workers, but for junior workers. And that's not the case. And they actually said there was a slightly stronger link between AI job disruption and higher education requirements. So yeah, a lot of times historically we've paid people a lot of money, right? I'm not just picking on lawyers, but that's one example. We've paid lawyers a lot of money because they were able to memorize facts and understand case law and they were able to synthesize information and personalize it to a certain case. Right? These are all things obviously generative AI and large language models do really, really well now, right? And yes, we are getting over the hallucination hump, I think, with reasoning models and easier rag so that, you know, just because you have a higher up role or higher education doesn't mean, you know, according to this study, that your job won't be disrupted by AI. And here's why I think this specific study is actually very important and worth paying attention to versus a lot of other studies. A lot of other studies, I'll read them. And it's, you know, it's economists and analysts and futurists and trend spotters, right? And all these people. And then I look, you know, a lot of times I'll go and look at these people and I'm like, these people don't have the credentials to actually be putting out these kind of forecasts, right? Some people do, but a lot of people don't. This is completely different. This is, this Microsoft study is one of the first large sample studies based on how people of all different career types are using AI. And if the AI, and this is the big thing, if the AI is actually doing major parts of their job for them, right? And I love actually how Microsoft put this study together by essentially coding all individual conversations and aligning them with actual tasks. All right? So, you know, they're essentially creating a very structured data approach to potential job displacement. And you'll see that here in a couple of seconds. And the study highlights tax task exposure and functional alignment. And it's not guaranteed job like your job's going to be obsolete, right? That's not what they're saying. They're saying it is a likelihood that AI may disrupt a certain job. Right? So keep that in mind. This is more aligning tasks, right? And saying, hey, you know, I'll just throw out one job right now. Because you can probably see how a job like this could be easily replaced or might be easily replaced ultimately by AI, right? So people who are translators or interpreters, right? In this study of 200,000 real world conversations, they were able to see, hey, these people who are asking for these specific tasks, the AI delivers them correctly at a very high rate. And that means high exposure to AI disruption. So there were a lot of hidden patterns. And one of them that researchers called out was essentially vulnerable. Jobs function as human APIs, right? And that's the most impacted roles or people have jobs where they essentially act as intermediaries or human APIs, right? Essentially grabbing information, a lot of times online, synthesizing it, and then personalizing it, and then ultimately just changing that information. Right? So information in ingest it, synthesize it, personalize it, create something of value. Right? But essentially synthesizing online content, repurposing it, creating value, and then ultimately it's online content. Right? So it's almost like a human API. Right? So I love that analogy. But they said those type of roles are obviously the most likely to be disrupted by artificial intelligence because their core value in these roles is taking complex unstructured information and creating a digestible output. And this function of translation and summarization is precisely what large language models are good at. Right. You know, a lot of times we overgeneralize these roles and just call them knowledge workers. I don't think that's necessarily the t the the best way to say it, because a lot of times there's knowledge workers who are interfacing in real life. There's knowledge workers who are, you know, who need to write in certain roles in industries, who need to be creating, you know, shaking real hands, touching real grass. Right? So what this is finding is the jobs most susceptibles are those that are literally just in front of a computer, grabbing information and then putting information out, information in, information out, human APIs, and that's it. So there's also, between these 40 different job types, there's kind of these four archetypes of common job types that were the most likely to be replaced by AI. And those are information synthesizers. So those are jobs or roles that take the massive amount of messy data and turn it into clean reports, like market researchers reading thousands of surveys to write trend summaries. Frontline communicators would be the second one. Those are jobs that involve repetitive conversations with customers or clients following certain scripts or call models like customer service reps, SDRs, BDRs, answering the same questions over and over. The third archetype would be knowledge curators. So jobs that work with language and words as their main tools. Right. People that essentially have a grasp on communication. And these are people like translators converting text between languages or writers, journalists, editors. Right. My former profession, I was a journalist for seven years when I kind of started my career. That's the third one. And then the fourth archetype is process coordinators. So those are jobs that follow standard procedures to complete things like paperwork, surveys, administrative tasks. And these are roles like loan office processing people, applications using set criteria. So again, the four archetypes of jobs that these 40 kind of fall under would be in number one, information synthesizers, number two, frontline communicators, number three, knowledge curators, and number four, process coordinators. And here's their scoring methodology explained, because I'm going to here in a minute or two, be reading off the 40 jobs by the most kind of susceptible or the most exposed to AI disruption. So here's exactly how they got to this. And I love this approach because again, a lot of times, a lot of other studies or projections, they're more like, you know, hey, let's just use our best guess. Let's just, you know, think logically, right? Or let's send out surveys to 10,000 people. Right? Again, I love how Microsoft put this together. And also, yeah, hey, you gotta call out the elephant in the room, right? I can't be saying, oh, Microsoft's great for this, this and that, because they. Microsoft is one of those companies when it comes to AI job disruption. They're in the hot seat right now because they've laid off tens, tens of thousands of people recently, or at least more than 10,000 people, I believe, in the last couple of months here. So I'm not overlooking that. But I do believe that this research is pivotal and important for us all to understand. So here is the scoring methodology that Microsoft researchers used. So essentially, there's something, I believe it's called onet. All Right. So this is a US Government database, all right? That is essentially a job dictionary, right? It's like, hey, here's all the most common jobs in the U.S. and here's all of the individual tasks that fall under these jobs. So this o net government directory lists 18,000 specific tasks that define what people do at work across all occupations. Okay? And Microsoft manually went through, along with the help of AI, Right? But the human researchers, this is what they did. They took those 200,000 conversations and they matched them with these 18,000 subtasks of all of these different job types. And in each conversation, and they tagged each AI action to a specific O NET task code. All right? And then they measured three things for each job. Number one, how often people used AI for each of these tasks that they coded. Number two, how successfully AI completed each task. Right? Because if it failed, then, okay, just because it tried probably doesn't mean that that type of job or that subtask is necessarily going to be disrupted by AI. So, number one, how often people use AI for those tasks. Number two, successfully AI completed them. And number three, how much of the job AI could handle based on all of those conversations. All right? And that led to these scores, this own scoring system that they created, and high scores on this applicability. Applicability chart that I'm going to be sharing here in a second. High scores meant high overlap, such as interpreters that got a 98% score, because according to these 200,000 real conversations with Microsoft's copilot, and matching it up to this O NET database of 18,000 specific tasks, 98% of the time, AI successfully handled these jobs or these roles or these tasks that interpreters took on. All right, this has been a lot so far. I got to take a sip here because I'm going to read off these 40 job roles. All right, here's the. Here's the guts of the study. I'm sorry I made you wait 18 minutes to get to this, but I wanted to make sure to give proper context on this. Right? I didn't want to, you know, start the show off by reading these 40 job roles. And, you know, and then people are like, oh, I'm one of the 40, and then they stop listening. So I wanted to make sure that you had the context exactly what this means. All right, so here we go. Are you still running in circles trying to figure out how to actually grow your business with AI? 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I'm going to read off all 40 and again, this is one of those. Yes, I have a chart on my on my screen. So if you care about this chart, we'll also be sharing a link to go read the report and you can see this chart in our free daily newsletter. So I'm going to start with the highest applicability score, which is that 98%, essentially from interpreters and translators. So now I'm going to read off these 40 straight. I'm going to try not to say anything else in between. And again, we're going from the highest to the lowest. But one thing to keep in mind, the lowest is still very high. Right? These are just the 40 most the 40 jobs that are most susceptible. Just because something's number 30 on the list or number 40 on the list, it doesn't mean it's not susceptible. It just means it has a lower AI applicability score. All right, here we go. So starting off, interpreters and translators, historians, passenger attendants, sales representatives of services writers and authors, customer service representatives, CNC tool programmers, telephone operators, ticket agents and travel clerks, broadcast announcers and radio DJs, brokerage clerks, farm and home management educators, telemarketers, concierge, political scientists, news analysts, reporters or journalists, mathematicians, technical writers, proofreaders and copy markers, hosts and hostesses, editors, business teachers, post secondary, public relations specialists, demonstrators and product promoters, advertising sales agents, new accounts clerks, statistical assistants, counter and rental clerks, data scientists, personal financial advisors, AR archivists. Archivists, sorry, economics teachers, postsecondary web developers, management analysts, geographers, models, market research analysts, public safety tele telecommunicators, switchboard operators and library science teachers, post secondary all right, so just because something's number 40 on this list, right. Or I'll, I'll pull out management analysts. Right? That's a big one. Which is like number 35. That doesn't mean like, oh, we're safe. No, this is again, there are thousands of types of, of jobs and roles. So if you, if, if a position made this list, it has a pretty high likelihood of being disrupted by AI. And for context, right. The most susceptible role, at least according to this study, is interpreters and translators with a 98% or a 0.98 coverage on this AI applicability score according to this government O net database. And the 200,000 conversations. And then the last one on the list, the library science teachers post secondary was 65. So just for a little bit of context. All right. Yeah, Marie, I was thinking the same thing. There are still switchboard operators around. Yeah, yeah, Writers and authors, Dennis. Who would have thought? Yeah, my former profession, right. I was a writer. And not to interject a personal story, but you know, my, I had a marketing agency before I started everyday AI in two years before ChatGPT came out, you know, they were OpenAI had license, it's or released its technology, its base technology that was a precursor to Chat GPT. And one thing that we were doing at my marketing agency for clients is we were doing a lot of content writing, you know, writing long form SEO blog posts. We were doing, you know, writing social media ads, we were writing Google Ads. Right. And yeah, when we started using the precursor to ChatGPT in 2020, I was like, wait, this profession that I'm in right now, essentially communicating, it is going to be disrupted. And that's one of the reasons why I started Everyday AI. Right. But yeah, you have to be able to pivot and understand the potential disruption. Putting your head in the sand or waiting until your company, you know, potentially stops a certain line of business or starts using AI, Right. Instead of writers and authors. As an example, which has been well documented over the last couple of years, a lot of, you know, freelance writers or journalists losing their job because the parent company or the organization is just, well, they're like, hey, AI is way better and faster once you understand how to use it. All right, so let's keep it going. I'm going to, I'm going to get to this question later. Siha. All right, so I want to talk about five underlying trends that I think hardly no one is talking about based on this report. Support. All right. And don't worry if your job or role was one of those 40. I'm going to end this show with what I hope is some practical advice. All right, so here's five underlying trends that I think people aren't really talking about and we should be paying attention to. Number one, higher education and knowledge work kind of makes you a sitting duck, right? Again, according to this report, there was a higher correlation between higher education and AI applicability. So jobs requiring degrees have a higher AI vulnerability than jobs without degrees. So as an example, data scientists and analysts face more threats than construction workers. Right. And your expensive education actually might make you a bigger target, especially if you were a more recent grad without a background in AI. I'm going to talk about that here in a second. As one of our other trends, the second trend, plumbers beat lawyers in job security. That's, that's the honest truth right now. I think when, you know, 10 years ago, when you would think about AI, artificial intelligence and job replacement, I think people thought humanoids, right? I think they thought, right, oh, you know, it's got to be plumbers. You know, there's going to be a robot doing the plumbing, but you'll always need a brain, you know, to do legal work. And I think what we've seen is kind of the opposite. Humanoids, AI powered robots, although it's obviously a huge growing industry, and I think that is physical AI or embodied AI is ultimately more important and will be more impactful than what we have right now in generative AI and how it impacts knowledge workers. But right now, at least according to this study, physical jobs show almost zero AI threat. And again, that's just in the study, right across these 200,000 conversations. But office workers can are facing immediate replacement threats, while manual labor at least is staying somewhat safe. Again, I know that this is a little tainted because of how the study was set up, right? But presumably you have people with jobs using their hands in the real world are using AI much less in our. Aside from that not getting the correct answer, much less. Right. Whereas people who are essentially human APIs, it's different. So the key there is if you're touching real things, according to this study, you're probably a little safer. And if you only sit in front of the screen all day, you're a little more endangered in terms of your job or role type. Number three, millions of people using AI are training AI to do the job better than that. That's one thing that I think so many people overlook. And as I read this study, it's something that popped out to me, right? I've talked about it before, but it's one of those things. I, I, I think about this every couple of months and then I kind of forget about it. But every single document that knowledge workers create, every single connection that you bring into these large language models, they ingest it, right? Even in this case, yes, this was a, an anonymous study. But all the big tech companies, right, why are they making this free? Why are they making it so affordable? Well, we're uploading all of our data. Not only that, but we are training, you know, through our prompt engineering process, we are training AI models how to do our jobs, right? Let's say a press release, right? And you upload all your company's information and you say this happened, you know, write 10 different press releases, personalize for these 10 different, you know, markets, whatever, right? And it might get some of them right, it might get some of them wrong. But then the human says, hey, this one's not correct. When we write a press release on crisis management for CPG goods in North America, we do A, B and C. So please update that, right? The human thinks they're being more productive and they are ultimately saving time. But what we overlook is as we give these large language models, not just our data, but our feedback in our process to get the right answer, all we're doing is training models to be better at those exact jobs, right? That's ultimately why it's sometimes free 99 to use these extremely powerful AI models because we are training them. So everything we upload our emails, our reports, right? You're not publicizing that data, Right? It doesn't become available to others. But what you are teaching the models through your prompt engineering process is how to get to that right answer. Right? So we have to think about that. The fourth trend, new employees can't really learn their jobs anymore, or it's much harder because right now, especially enterprise companies, they're using AI and they're doing all of the basic tasks that used to teach you skills in a new role, right? A lot of times some of the first tasks or projects to get automated with AI or augmented with AI are those that an entry level person would take on. Because generally you pass off the lower risk, time consuming manual projects to AI, which in many cases these are what new this, these are where new employees start. So it's getting harder for junior workers, whether those are recent college grads or if you're in a career transition or if you're just starting a new role at a company that's a little different. Junior workers or newer workers are Finding it harder to learn the industry ropes because AI, AI is handling a lot of that grunt work that builds that subject matter expertise. So I think what we're doing here ultimately is creating a future leadership crisis and a future expertise crisis by automating the basics. So it is a catch 22, right? Companies that are, have found great success, you know, using their data, you know, creating vector databases, rag pipelines, right? Training different models using AI in the right way. It's great, right? But what they're doing is they're also making it hard for new employees. Number one, they're making it harder to even justify having those roles, which is, you know, another story for another day. I think in general, the way that companies are reducing headcount because of AI, they're not always just, you know, firing 10,000 people. But you know, if they're normally high rank because of attrition and churn, whatever, if they're normally hiring a thousand people every single year, maybe now they're hiring 500 people and half of those jobs just aren't needed anymore because of AI implementation. So I think we are creating a future leadership crisis and a future expertise crisis. Number five, the fifth trend that I think people aren't paying attention to is, well, cheap AI beats expensive humans every single time. Companies will accept slightly worse outputs for massive cost savings, right? A 5% drop in quality for a 70, 80, 90% cost reduction. That's an easy choice, especially for enterprise companies that really only care about the bottom line. They don't care about the humans, right? They care about the outputs, they care about revenue, they care about their stock price. So, you know, one thing people have always harped on is like, oh, you know, sometimes AI hallucinates and it'll make stuff up. Okay, yeah, humans do too, right? So would you rather, as an example, if, if we look at some of these jobs that, you know, let's say translators, translators and interpreters. Let's say you run a, one of the biggest companies of translators and interpreters, you know, and let's say you're paying a staff total their staff salary of a million dollars, right? And let's say the, for whatever reason, they have a 99.9% accuracy rate, right? These humans, what if all of a sudden they're like, wait, we can do most of this with AI and instead of 99.9% accurate accuracy, we can have 99.5%. Is it a big drop off? Sure. But if we can do that at a 90% cost savings, companies are gonna do it because Good enough. Cheap AI wins over perfect, expensive humans when money talks. And guess what? In the business world, I know we all like to think that our employer cares about us. Oh, I've been at the company 10 years. You know, the company would never, you know, try and automate my role. I've been here for, you know, I've given the company so much of my expertise, yet they IT companies don't care, especially public companies. You are a number, right? And large language models are going to gobble this all up. Sorry, I want to talk to you straight. I want to give it to you straight. I'm not one of those people that will lie to you and say, oh, AI won't take your job. Someone using AI will. That's a farce. That person using AI might take 5, 10, 15, 20 jobs. Okay, so what do you do about it? It? What happens if I mentioned your job and it's on the list? Or what happens if you feel that your job could be on the list soon as large language models improve? Right. That's the other thing. This is based on some older data because it takes time to go through all these conversations, match them up. I would assume the same job in or this same research if it was from conversations in 2025 only those percentages, number one, are all going to be much higher. And there's going to be other complex, more complex, more knowledge intensive roles on that list, on a future list. All right? So it doesn't mean you're safe just because your role. Again, there's thousands of different job types. Just because your one role didn't make the top 40 doesn't mean it won't. But if it did, and even if it didn't, here's what I want you to do about it, all right? I'm not just going to leave you with a little sense of horror. Actually, I did want to answer this question from Sneha quick. So she said writers and authors seems like a broad category. There's so many different types of content creation roles. Do you think this will really eliminate these roles? What's the best way to adopt and stay relevant? So I will say this and I'll give it to you with a personal example, Sneha. So again, this is pre AI. So this is back in 2019 or 2020. My company was hired to help up a company. They had 20 writers, right? And there's different writers, different levels, but we were managing them all on the front end, in the middle and on the back end. And this is again, I'm Someone with a background in writing. So maybe this one example can help. Yes. AI now would eliminate, out of those 20, I think you could do a better job with three to four humans. Right. So you're eliminating, you know, more than, more than 2/3, you know, almost 3/4 of those roles in this example. Yes. I absolutely think that in so many of these knowledge, especially if you are a human API, those roles, they're like farming, Right. I hate this analogy, but it's true. I saw Mark Zuckerberg bring this analogy up, you know, saying like, oh, 200 years ago, 90 some percent of everyone was farmers and now no one's a farmer. Right. But if you are a human API, right. If all you're doing is sitting in front of a computer synthesizing and repurposing information, large language models do that. So job types, right, are going to change very, very quickly. Yes. New jobs are going to be created that we don't even know about, that we don't understand, that we can't foresee. But I do think it's going to take a little time. Those jobs aren't going to come next year. Right. As you know, it's not like, oh, these, you know, let's say there's a million. There's probably not a million. Let's say there's a hundred thousand translation jobs in the world, you know, it doesn't mean there's going to be a new AI equivalent, a hundred thousand of them tomorrow as these are replaced, it could be a couple months, a couple quarters, couple years or more. And those roles are obviously going to look a little different. But let's talk about now, the best way to adapt and stay relevant. So here's some of my kind of advice on what you can do about it. Number one, become an orchestrator, not a spectator. You need to learn to manage teams of AI agents doing your old tasks. A lot of people think, oh, the best way to be not replaced by AI is to just not use it or make sure my company, my department isn't using it or not learn it. That's the worst way. You need to shift from doing work to orchestrating AI that does your work. I know that sounds weird, but one of the best ways to stay AI proofed is to be the one driving the conversation. You need to master prompt engineering. And yes, I understand prompt engineering is an old term. It doesn't exist anymore. Yes it does. Right. A lot of people thought that every job was going to be prompt engineering. I'm like. Or that they thought like there's going to be millions of people that are hired to be prompt engineers. And I said, no, that's just going to be everyone's role. Prompt engineering is the process a human goes through to get a better output a out of a generative AI system. We're all prompt engineers. And that term is constantly changing as the models in the generative AI systems change. So you need to essentially master the process of getting better results out of generative AI and focus on AI literacy like it's your only job requirement. Right. Those two things, AI literacy. You have to understand what's happening in the world of AI and how it impacts you because tech innovation is going faster than ever before. And you need to master the process of getting better results and more accurate results, more personalized results from generative AI. Number two had physical elements to your digital job. I know this sounds weird, but we had this, right. It was almost like these two perfect or not perfect events happened at the same time. This generative AI wave, which technically started in 2020 but most of the world didn't see it until ChatGPT in 2022 and Covid. Right. That forced a lot of in person organizations to either go hybrid or fully remote. And I know a lot of those are reversing and going back to in office. But one way is add physical elements to your digital job. Right. Which might sound weird. I know I mainly sit in front of a computer, I'm mainly remote or hybrid. And when I go into the office, I don't really do anything. Start doing that because in the future human interaction is going to, the value of it from a business standpoint is going to increase. Because what we're going to do is we as humans are going to become overly reliant on AI automating and making decisions for us. And I think one of the most important roles in the future is understanding human nuance, being able to capture and leverage human intuition, human decision making, human logic. Right. So you should be right whether you're doing it for an official purpose. But you know, if you're normally just emailing clients, if they're big clients, start meeting them in person. Well, why? Well, if I'm being honest, it's number one, it can help with job security. Right. If you're going to conferences, workshops, interfacing with clients in real life. But the other thing is, well, you need that information for AI. You're going to be able to capture so much better unstructured information that's going to help your business when you're interfacing with people in real life. Because like I said, it's kind of like when the business world zigs, you should zag. Right? We're becoming less and less touching grass. Right? So when everyone else is now burying themselves behind AI, you should be doing that part as well. But you also need to be fostering and building those human connections. Number three, create your own digital exhaust trap. What I mean by that is you should be building proprietary knowledge that only you possess. You should be developing internal company insights that AI can't access right now from public data, or those insights that maybe don't even show up within your own internal data. And you need to make yourself the human API for your company's department, not just for public information and your confidential information. Right. You need to be the. Like what I say, the historian of your company's or your department's decision making. Right. What are all of those things right now that large language models can't touch? Right. And a lot of times it's decision making, it's business logic, it's decades of expertise. You need to be able to get involved in that. You need to be the human API that connects that to whatever AI system you're using. And then I, I believe this is the last one here. Yep. Number four is be the human expert driving the agentic loop. Regardless of what you think, agentic AI is the next frontier. Right. Eventually it'll be embodied AI, but for the foreseeable year or couple of years, it's agentic AI. Human in the loop is passive and dangerous. If you're listening to this podcast, if you're still with me 41 minutes into it, you need to be the AI native leader driving the agentic feedback loop with human intuition, expertise and improvement. Right. A lot of times when we think about agentic AI in the future of jobs, we just think, oh, I'm just going to be watching a bunch of agents and, you know, we're just going to have human in the loop make sure agents don't screw up. That's dangerous, it's passive. It's all wrong. We shouldn't be saying human in the loop. Let's delete it from our vocabulary. You need expertise driving the loop loop, and you need to be that expert. Right? So if you are worried or if your job is on one of those, you know, top 40 lists and your company is investing in AI, you can either run, stick your head in the sand, or you can be one of the ones. You can be in the group of people that are embracing the Future of work. It's happening whether you want to or not. And I think where we're headed, right, you shouldn't be skating to where the puck was two years ago. Start skating to where all the signs are saying we're headed. It's agentic AI, multi agentic AI. And you need to be the expert driving that loop. All right. We covered a ton today, y'. All. We talked about a little bit about the methodology behind this Microsoft study that looked at the 200,000 conversations, matching it up to 18,000 different job tasks from this federal O NET database. We gave you the 40 jobs that Microsoft says will be replaced by AI. We uncovered five of the underlying trends that I think people aren't talking about it. And then I gave you some actual and practical advice on what you can do about it. Right. Instead of just, you know, doom scrolling online or feeling an uneasy feeling in your stomach about the future of your career, I'm giving you actionable, practical advice on what you should be doing. So I hope this was helpful. If it was, please share this with someone. I'd appreciate that. People are always like, saying, like, oh, Jordan, this, this thing has been my secret weapon. You know, you're helping us so much. You know, these guests that you bring on are great. Right? Okay, cool. Tell someone about it. All right, so if you're listening on the podcast, number one, please click that Follow or subscribe button on Spotify or Apple Podcast. I'd appreciate that. Leave us a rating. I'd really appreciate that. It's a lot of work to do this every day. So I do, you know, really appreciate those of you out there who are following the show. But if you're listening on the live stream on LinkedIn, please click that repost button. Share this with someone that needs to hear it because I think this is an important message. I think sometimes, you know, companies, research organizations, etcetera, Are painting an overly rosy and not always accurate picture of the future of AI. Right. I'm again, I'm not trying to be a, you know, a doom and gloom person. I am trying to let you know that you need to be pivoting now. And I think that looking at this Microsoft report, hopefully it was very helpful to you. So make sure if you haven't already, please go to your everydayai.com Sign up for the free daily newsletter. We're going to be recapping today's show and a whole lot more. Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks. Y' all. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. 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