I've Got Questions with Sinead Bovell - The Job Market Is Going Away (Here’s What’s Replacing It)

Episode Date: April 2, 2026

In this episode, I explore what the data is revealing about the future of work in an AI-driven economy and why the “learn, work, retire” era is ending. I discuss how the unbundling of jobs impacts each of us and what it will take to move through this transition in a way that is participatory without leaving people behind. Follow my work here: Substack: ⁠⁠⁠⁠⁠⁠⁠https://sineadbovell.substack.com⁠⁠⁠⁠⁠⁠⁠ Website: ⁠⁠⁠⁠⁠⁠⁠https://www.sineadbovell.com⁠⁠⁠⁠⁠⁠⁠ Instagram: ⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/sineadbovell⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/sineadbovell⁠⁠⁠⁠⁠⁠⁠ Twitter / X: ⁠⁠⁠⁠⁠⁠⁠https://twitter.com/SineadBovell⁠⁠⁠⁠⁠⁠⁠ YouTube: ⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/Sineadbovell⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@sineadbovell

How is artificial intelligence going to impact work? We've spent the past few decades building our modern workforce around the idea that we learn, we work, and then we retire. The data is starting to show that chapter of the workforce is likely going to close. And instead, the future of work will be much more about skills than just defined job titles and much more about lifelong learning. So let's dive into what we can see about how work is likely going to change. I'm Shane Beauvel and this is I've Got Questions.

A few weeks ago, I was a guest on the Oprah podcast to talk about the future of work. And it, of course, feels quite surreal to be a guest on her podcast. So as the futurist into Tsuistic Forsyad Advisor, I was there to paint a picture of work. And as you know, if you've been following our channel for a while, we talk about the future of work a lot. And one thing you hear me say is you can't really make predictions. Nobody can say for sure. But I follow the signals in the data. So reading the white papers, reading the paper, reading the.

patents, tracking AI's capabilities over time. And the first thing that we can see quite clearly in the data is the types of jobs that AI is much more suited to impact. And that is white-collar jobs or knowledge work. So if your job is conducted primarily on a computer, right, so maybe you are in marketing, sales, software engineering, you are in accounting, finance, customer service, you are in any sort of content creation that happens in the computer. All of that type of data, and all of those types of tasks that happen within those jobs are what AI is being optimized for. And we can see this quite clearly. The last few chapters of automation were much more about automation in manufacturing plant and in warehouses towards physical labor.

This chapter is about much more cognitive labor. So that's one thing that we can see more clearly. But the second thing we can see more clearly in the data is that AI isn't capable of automating an entire workflow. At least not yet, it's nowhere close to that. It makes tons of mistakes. It's not the best at taking instructions over long arcs of time and so forth. But what it can do is automate tasks within a job. So maybe you're a financial analyst.

And six of the 10 tasks you do in a day, let's say, AI can actually do those pretty well. So maybe some of it is crunching some of the weekly financial reports that come in and figuring out when cash flow is at a point that it should be flagged and discussed, whatever that may be six of those tasks out of the 11. So when we see that AI is best suited to do more tasks within a job than the job itself, it starts to call into question what that role becomes. It leads towards this unbundling of jobs into tasks, tasks that AI is best to complete and tasks that humans are best to complete.

Now, this starts to change the nature of any particular job, because if AI is doing, say, 60% of the tasks and the human is doing the remaining 40. What does that job become? If we're to look at the job of a financial analyst, maybe the number crunching AI is much better at. But the human then, their role evolves towards directing these supercomputers to solve these problems, evaluating the tradeoffs the company is going to have to make based on the different results. That role looks much more like a financial strategist in a couple of years than it does a financial analyst. And again, a couple of years after that, AI will learn new tricks over time. So that role continues to change. And that's why the idea of these fixed job titles, these steady job roles, where you can have a description and that description is pretty, pretty honest and pretty consistent with years to follow, starts to make less sense. And jobs become much more fluid. So that's what I mean when I say the future becomes much more about skills than about jobs. And as we learned with the AI economist Adj. Agarwal, who's been studying transformative and disruptive technologies for this. decades, the skills that made you dominant before AI may not be the same skills that make you

dominant after AI. So before AI, that financial analyst was the number cruncher. After AI, well, they have to be much more about the problem solver framing these much more complex problems now that you have a supercomputer, your communication skills, your judgment on weighing the different tradeoffs. Now that you can analyze all this data, it becomes even more challenging. What market should we enter into? What product should we discontinue? It may not be the same person. that has those skills as the original financial analyst. In this, that financial analyst continues to learn and update their skills over time and continues to strengthen things like their judgment skills and their critical thinking

and their problem solving and communication, skills that we've talked about in other episodes. So we start to see a market, a future workforce market that becomes much more about skills, jobs become a little bit more blurry to define, and those skills continue to change and evolve over time. So then what becomes more clear with those data points is that if the roles that we know are going to continue to change, it doesn't make sense to have a defined job title, is a company going to hire for a full-time role or much more likely to hire an independent contract because they know that the person who's best for the tasks that need to be completed today may not have the skills for the tasks that need to be completed in a couple years? So we'll start to see the rise of a lot more

independent type contracts and therefore work becomes much more entrepreneurial for all of us because one we're continuing to learn over time two we're not holding a particular role instead we may be offering our skills to a few companies at once so maybe i'm a really i'm really good of financial strategy i do 40% with this company another 40% with another company and then i'm kind of in between different contracts for the rest we all start to adopt much more of an entrepreneurial profile in the workforce. And I've talked about this and written about this much more of an independence era. And we continue to update our skills over time. And that's some of the things that we can start to see about the shape of the workforce. Now, of course, some jobs and some work in

its entirety will likely be automated. The data points to more administrative roles and customer service rules. And then even within certain segments, maybe you don't need as many people in finance. Or maybe you need more people in finance because now that financial services get cheaper, now that legal services get cheaper, we all use a lot more of them. But you may need different types of people with different skill sets. So work becomes much more fluid and becomes much more project-based. People come together to solve certain problems and work on certain projects and then maybe they disband. And it becomes much more about skills. These are some of the things what we can see in the data. Now, of course, there are some big claims about maybe one day AI can take

all jobs. And again, we talked about this with Ajay Agarwal, the AI economist who's been studying transformative technologies for a decade. Nobody can say definitively that by 2060, AI wouldn't be capable of doing all the jobs. Nobody can really make those types of predictions. And as we also chatted about in that episode, work has always evolved in such strange ways that we will probably continue to do things and have scarcity, it will just look unrecognizable. And I mean, that shouldn't be entirely surprising. 60% of us work on occupations that didn't exist 80 years ago. If you are in anything to do with software services, if you are in most of the roles that fall under marketing were invented in the last few decades, a lot of the

roles in finance were invented in the last few decades, the entire social media economy, and not just creators, but anybody that's doing work that involves something at YouTube or podcast, or podcasting, all of these industries were recently invented. So how I see the future of work is that the shape of the economy is going to change quite drastically because AI and what it can complete, the cognitive labor AI can do, and the intelligence AI brings, not the same as human intelligence, it's its own type of intelligence, that's going to get cheaper and cheaper and become much more abundant. So the economy is going to reconfigure around that.

resource becoming cheap. What does the economy look like on the other side of that? It's really hard to say. We now have people who make money filming 90-second videos in a car. That's entirely unrecognizable. And it doesn't mean that everyone's now going to go and become something in communication and entertainment in the future, that it just meant that when entertainment and communication and distribution got very cheap because of the internet, we saw the rise at all this new types of strange industries and types of work, podcasting being a core part of that. So that That's how I see work evolving over time. Where I think my biggest concerns lie, one is in the transition period. So is it possible that jobs disappear faster than new types of work emerge?

Yes, that is entirely possible. And we've seen this with past technological transformations. And that to me is quite concerning. However, we can plan for that. And if people are left vulnerable and exposed, I mean, that should not happen because we can see. today, jobs may disappear more quickly. We've seen historical examples of that. What does it mean to protect people through those times of transition? We need to have that answer now. So I think that that's very, very important. And even areas like health insurance becoming decoupled from work, if we start to have much more fluid types of independent-style contracts and work becomes more entrepreneurial, and we're continuing to learn over time, the second area that concerns me is power asymmetry.

So a lot of these changes are being driven entirely by the private sector. And that, of course, isn't anything new. The private sector has historically been the sector that has been doing the inventing, been doing the automating, and so forth. The challenge is the private sector is optimized for profit and shareholder value, not necessarily the humans within the labor market. And that's why government and regulation is supposed to incentivize the type of behavior that's best for the people. And if we don't see that type of of restructuring, that type of support from government, that type of regulation that actually curbs extreme power asymmetry. We might have a future where companies really just kind of see us as just dispensable short-term pieces of labor and contract and we're just there to fill specific things and we're not protected and we have no bargaining power. We don't want that to happen. We can again see that type of scenario happening if changes don't happen today. We want this transformation of work to be participatory. We want people to be. be empowered. We want people to be skilled. Companies should be investing in those types of skills.

Because after all, it's our cognitive data and our cognitive labor that AI is being trained on. So everybody should be able to take part in the prosperity that AI is generating today, not just some far out UBI type of strategy today. Everybody should be skilled and empowered to participate in how work is going to evolve and have a say in how it gets shaped. And there are many levers that we can do that through, whether that's through our own companies, whether that's through who we vote for. But these are the types of things that I think are really imperative that we've stored out now. In terms of the types of skills that will need for the future of work, we've covered that a lot on previous episodes. And I see the final thing that's becoming increasingly clear about the future of work is that almost all jobs will require AI knowledge. They will require working with artificial intelligence systems, especially if you work in a white-collar industry or

in the knowledge economy, it is imperative that you are starting to work with these technologies now. You're starting to understand how your role is going to evolve and how you can leverage these technologies, how you can leverage AI as a springboard for what you're going to do. But soon, AI will become much more like the computer where it's just going to be an expectation. So it's really important that you're starting to lean in. You're starting to learn how do I direct AI systems to perform certain tasks? And you can think of AI in your role, like an interoperative If you were to give in an intern instructions, you would have to frame the problem properly. You couldn't just say, do this thing and expect it to do it.

You would have to evaluate its work, give it feedback. AI is not going to get the right answer the first time. And that's why a lot of people use AI once. It's terrible. It made all these mistakes. It's just like an intern. You're going to have to coach it, give it an identity, evaluate its work, make sure that you're framing your questions properly and so forth.

And you understand the boundaries of what AI can. It can't do. What would be in its data? What wouldn't be in its data? And we've talked about this again in past episodes. but that is becoming quite clear. And finally, in the data, we can start to see new types of roles already emerging. Being able to build AI agents, and these are systems that can take action on their own,

being able to direct AI agents in a workflow. So maybe you're helping a venture capital firm automate a series of tasks and string them together. So first pull in the financial spreadsheets from the latest market report, build out a deck, send it to this VP. key. If the numbers in the deck are past 10% of our budget, trigger an invoice from accounting. Connecting all of those workflows, using AI, using AI agents, and being able to build that out together, that is a role we're already starting to see a merge, AI agent architect, whatever you may call it. So already new types of jobs are starting to appear in this AI first workflow. As always, the future of work is a topic we discuss monthly on the podcast. We want to make sure we

are with you every step of the way. As the data changes, we keep you. informed on that. I write about it weekly on my substack and we'll continue to have different guests from economists to AI companies themselves helping us explore how work is likely to change and most importantly what we can do today to participate in how things are going to evolve. Thanks so much for tuning in and I look forward to seeing you at the next one.