In this compilation episode of The Parlor Room Presents: Hello AI, host and Harvard Business School Online Creative Director Chris Linnane explores one of the most pressing questions facing professionals today: Will AI take my job?
Featuring HBS professors Nien-hê Hsieh, Christina Wallace, Joe Fuller, and Iavor Bojinov, the conversation examines how AI is reshaping organizations, changing the nature of work, and influencing hiring, management, and trust. From the rise of AI-powered startups to challenges around adoption, ethics, and inequality, this episode offers practical insights into how professionals can navigate an AI-driven future and adapt to the evolving workplace.
Resources
Catch up on previous episodes of The Parlor Room, featuring faculty from this compilation episode:
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Transcript
Editor's Note: The following was prepared by a machine algorithm and may not perfectly reflect the interview's audio file.
Nien-hê Hsieh: Part of me worries that any kind of problem we already have in the world will basically be exacerbated by the application of artificial intelligence. So here's some examples. So think about, for example, not to sort of make things worse.
Chris Linnane: I got to take a deep breath before we get this. Go ahead.
Voiceover: Everyone is asking the same question: "Will AI take my job?"
Joe Fuller: That's the wrong question. The right question is, "How do I reconfigure my process around this general-purpose technology, around the AI?"
Voiceover: One day, AI is an opportunity. The next day, it's a threat.
Christina Wallace: And so we're seeing teams of two and three founders, maybe one or two employees, able to do the work of a team that used to be 15.
Voiceover: How do you make AI work for you, not against you?
Iavor Bojinov: Did that developer build the system to just replace me? If I believe they just built this just to replace me after six months, of course, I'm not going to use it.
Voiceover: Welcome to The Parlor Room Presents: Hello AI.
Chris Linnane: Welcome to a special episode of The Parlor Room Presents: Hello AI. So we're asking the question you hear almost every day now: Will AI take my job? And that answer may depend on what you do, who you ask, and when you ask them.
In this episode, four Harvard Business School faculty members share how they see the future of work changing in real time. Our first clip comes from Joe Fuller, who studies the future of work and how companies are reorganizing around AI and what that might mean for you.
Why does it feel like AI is forcing companies to rethink how work is organized and not just what tools we're using? So it feels like it's not just like, "Oh, I'll pull this in." It's changing much more.
Joe Fuller: Well, I would actually argue that companies aren't doing that enough—that companies don't have any experience in adopting general-purpose technologies. No one here lived through moving from steam power to electricity. And very few senior management teams really were senior leaders during the launch of the internet, which is probably the closest analogy, although I think the real innovation at that period of time that caused me to sit up and take notice about the internet was when the Mosaic browser that could move the internet from alphanumeric only to have images and photography and that was just, my jaw dropped, and I said, "The world's going to change now."
So what companies have been doing too much is saying, how do I append AI, particularly generative AI, to my existing process? Almost like it's just another big new SaaS system. And in fact, because it's a general-purpose technology, that's the wrong question. The right question is, "How do I reconfigure my process around this general-purpose technology around the AI?"
And yes, you're seeing companies, I would point to companies like Procter & Gamble, and Coca-Cola, and consumer goods, JPMorgan Chase, who are being really pretty aggressive in the way they're deploying AI. Use Google obviously as a core AI innovator, but what those companies are doing is taking very important processes and reconfiguring them around the AI. And that's what this calls for. And I think a lot of the numbers we see about the failure of AI experiments are badly configured experiments fail. You'd be surprised.
Chris Linnane: And when they're putting together through successful experiments and pushing, what part of the org chart is the pressure hitting the most?
Joe Fuller: Right now, it's hitting entry-level and lower managerial jobs. And the reason for that is those jobs often have tasks that are heavily what I'm going to call rules-based. A good example is a credit analyst. If you get hired out of an undergraduate business program to be a credit analyst at a bank or for a company that does what's called vendor financing, I will lend my customer the money to buy my goods. When you start off, you're basically just applying a set of rules.
You've been told, "Companies of this size, of this history with us, of this volume with us, not more than X dollars, here are terms, here are the interest rates you're going to charge, et cetera, et cetera." And usually for large companies, which are being quicker to adopt AI because they can afford it, they've got a lot of data. So, rules-based decisions where you've got lots of longitudinal data, that's almost a perfect environment for AI.
So a lot of those tasks that have those features tend to reside in lower managerial ranks, and the people they supervise, who tend to be entry-level workers or what a lot of companies call individual contributors, who are out of that entry-level kind of probationary status, but are still doing core work of the company, but they're not decision makers.
Also, a lot of lower-level managers do a lot of routine reporting. They take the sales report, and the inventory report, and the production schedule, and write the bimonthly uncertainties we're facing in consideration for middle management or upper management about changing our plan. Though relating two or three databases and integrating them into a coherent written document, particularly if you can upload 10 or 20 or 2,000 previous versions of it, it'll do a really great draft.
Now it won't be exactly what you want, but rather than looking at a cursive blinking cursor where you have to write the first topic sentence for the first paragraph, it's much easier and quicker to edit than it is to create. And for routine work like that, it's really, really effective.
Chris Linnane: Our next clip comes from Christina Wallace, and she explains both sides of this moment: the incredible power AI gives people to build faster than ever before and the unsettling reality of what that could mean for jobs.
Christina Wallace: About a year ago, kind of top of 2025, it became clear that entrepreneurs were using AI and not just in getting a better version of Googling something, right? When LLMs first came out, and we were all like, "Oh my gosh, ChatGPT is so powerful." The default usage was sort of like, "Well, let me just ask ChatGPT instead of doing my Google." And it was good for short-circuiting research. Of course, you have no idea if you can trust that research, but it allowed you to do things a lot faster than you used to. Write an email, process a set of data, whatever.
But that is still very much in the era of AI as a copilot, or as I think of it, as my intern. It helps me remember I need to review its output. And about a year ago, I started seeing a lot more of this shift toward AI agents where you're not using it to process information and give it to you so you're making decisions, but instead you're setting up key operating systems in your organization so that the agent takes in the information from some sort of a sensor or input, does something to it that you have defined and then makes a decision based on the output of that analysis all without you having to intervene. And it really escalated.
I mean, there's the speed at which this adoption occurred within startup world over the course of 2025 was pretty spectacular. If you think about what the adoption curves used to look like for new technologies, to the point where we are now seeing startups that are skipping their seed round of financing, right? They're going out, maybe they're getting a pre-seed, they need a little bit of money to work with, $50, $100,000, but that round where they used to go raise the million to hire some people and start building things out, they can skip because instead of needing to hire 10 people, they're building 10 agents to do that work.
And so we're seeing teams of two and three founders, maybe one or two employees, able to do the work of a team that used to be 15. And it's been really inspiring, but also like slightly terrifying, this pace of adoption because it requires an eye on what's developing in this technology at the same time as an eye on building your company, and that's a lot to manage and take in as a founder.
Chris Linnane: In our next clip, Iavor Bojinov shares a story from his time at LinkedIn, where he helped build an incredibly advanced AI system, but as he explains, building something powerful is only part of the equation.
Iavor Bojinov: We launched it, and no one used it besides like the three or four people that I actually trained up on it, and we waited for sort of a month, no one used it. And this was a bit of a shock to me because my background is in statistics. We learned how to build methods. We learned how to build AI systems. No one told us that if you build it, they won't come. So I was in this mindset that if I build it and if it's good, people are going to come to use it. And so that actually led to a whole stream of research and trying to understand why on earth, if you've got this amazing AI system, this amazing technology that works really, really well, why aren't people using it?
And so here's the thing, what we found is, and this is going to sound so basic, but it really came down to trust, but trust is a very overloaded word. It means many, many different things. And if you ask people, "What do you mean by trust? Trust, what exactly does that mean?" But what we found is that there are basically three dimensions of trust in this setting when it comes to AI.
There's trust in the algorithm itself, the model that's being used. And these are things that you can guess, like, is it accurate, right? Does it give you consistent results, right? If I ask it twice, is it going to give me the same thing or is it going to give me something completely different? Is it transparent? Is it fair? Is it biased? All of those things around the model, that's one pillar, but it turns out that's actually not enough.
There's two other dimensions that are actually really, really important. We really lived this at LinkedIn because we knew it worked. We could show it worked really well, but again, that still wasn't enough. The things that were missing were what I call trust in the developers and trust in the process. Trusting the developers is this idea that do I trust the people who actually build the system? And there's a number of dimensions of that. The first dimension is, "Well, did that developer understand my needs and build something that would work for me?"
And if the answer is, "No, then why would I use that? They didn't listen to me. They didn't speak to me. They have no idea if me as a user, I have no idea if it's going to work for me. I'm just going to do what I know works." And so at LinkedIn, that was a huge failure because, as I said, we worked very closely with a couple of teams, and they knew it worked for them, but everyone else thought, "Oh, it just works in those special cases. It's not going to work for mine. Mine's really special. You didn't speak to me. You don't understand my needs. It's not going to work for me. I'm not going to even try to use it."
So that's one dimension. The second dimension of this developer that's getting even more important now is, are there any hidden intentions? So, for example, did that developer build the system to just replace me? Because if I don't trust the developer and I don't think if I believe they just built this just to replace me after six months, of course, I'm not going to use it. But that's very different from, does the model work? So that's the second lever. And then the third lever, which is sort of even more zoomed out, is trusting the process.
And here this is fundamentally about what happens when it goes wrong? Who's responsible? Am I on the hook for this, or is there someone else that's going to be responsible? Or if I disagree with it, how do I handle that? What is the process for me disagreeing? And so that's why I sort of learned that if you build an AI system, people are probably not going to use it, and if they're not going to use it, it's a failure in trust, and it's usually one of those three dimensions.
Chris Linnane: Our final clip features Nien-hê Hsieh. As always, Nien-hê provides both a reality check and a perspective that may become essential for all of us in the coming years.
Nien-hê Hsieh: At a very basic level, one could think of artificial intelligence as a kind of multiplier, both in terms of the speed at which things happen, the scale at which they happen, and also the scope in terms of our lives. So part of me worries that any kind of problem we already have in the world will basically be exacerbated by the application of artificial intelligence. So here's some examples. So think about, for example, not to sort of make things worse.
Chris Linnane: I've got to take a deep breath before we get this. Go ahead.
Nien-hê Hsieh: But if you think about, for example, something like social media, the malicious use of social media, say, for example, like misinformation, or that's just compounded by AI because AI is very good at producing misinformation or deep fakes or the kinds of things that we already worry about can be done much more quickly and more effectively. So that's sort of an example there. Or if we think about the environment, AI uses a lot of energy, and so the more that we use AI in some sense, we're just multiplying the use of energy that sort of raises challenges there, and you sort of see the whole thing about data centers, the need for water.
So again, just as we grow, that problem is compounded as well. Or if we think about inequality, both in terms of say loss of jobs but also in terms of concentration of ownership of the benefits of AI, that kind of inequality is likely to grow, and on that we don't think about as much, I think is actually the question of linguistic access. So you mentioned earlier that LLMs are basically trained on what exists, but they're also trained on a few languages. So they work really well for those languages, but if one doesn't speak one of those languages, the access to LLMs is really not there.
Or similarly, it'll then probably just sort of concentrate people on focusing on a few languages, and other languages that are sort of left behind will increasingly be left behind because the time and energy that would be spent to develop an LLM for those languages isn't really sort of there. So that's another kind of inequality that we don't talk about much, but that'll also be compounded by the growth of AI. And then I think there's probably some problems that are maybe specific to AI itself as a technology. So not just sort of compounding the kinds of problems that we already have.
Something that I know you and I have talked about before is that because AI is such a good or approximation of engaging with another human being, I think for me, the biggest worry that I have is that we'll sort of forget why we actually want to engage with another human being as opposed to sort of a good approximation of one.
Chris Linnane: Okay. So that wraps up our "Will AI Take My Job?" episode. We hope you found these conversations insightful, thought-provoking, and maybe even a little clarifying, as all of us are trying to understand where this technology is taking us next. Thanks for listening.