Why Full Autonomy Beats Co-Pilots for AI in Banking with Dimitri Masin, CEO of Gradient Labs

Dimitri Masin was one of the first 30 employees at Monzo, where he led AI and data science as the bank grew from 30 to 4,000 people. That vantage point showed him where the real work in financial services still lives: the manual, repetitive customer operations running behind the app. In 2023 he co-founded Gradient Labs to automate that work with fully autonomous AI agents, and the company now serves more than 30 fintech and financial services customers. In this conversation we get into why co-pilots can quietly degrade quality and compliance, why Dimitri believes full autonomy is the safer path, and the story behind what may be the largest known AI agent deployment in banking.

What We Covered

• From Google to one of the first 30 people at Monzo
• The second half of the fintech transformation
• Why customer operations never got reinvented
• What GPT-4 unlocked at the start of 2023
• Putting banks on autopilot
• Sitting as an orchestration layer over existing systems
• The 15% customer experience uplift over human teams
• Why cost savings are more nuanced than people expect
• How bank implementations and bake-offs actually work
• Why co-pilots can degrade quality and compliance
• The case for full autonomy over a human in the loop
• Benchmarking agents against the human team, not perfection
• Redeploying staff instead of cutting headcount
• The largest known AI agent deployment in banking
• Why banks aren’t seeing productivity gains yet
• The build-it-ourselves mindset shift
• A five to ten year view of the transformation
• How the US bake-off culture plays to a specialist’s advantage

Key Takeaways

• The overlooked opportunity in banking is not the app experience but the manual operational work behind it: customer support, AML, fraud, KYC, onboarding, and screening.
• Co-pilots can backfire. When suggestions are right 90% of the time, people start accepting them blindly, which degrades quality and compliance in the other 10%.
• No agent is correct 100% of the time, and that is the wrong bar. The right question is whether the system beats the human team it replaces, which becomes the benchmark.
• Automation has not meant layoffs at any of Gradient Labs’ customers. Teams get redeployed to complex, higher-empathy work like vulnerability and financial difficulty cases.
• The bottleneck on transformation is not the technology, which has existed since GPT-4, but how slowly organizations diffuse and adopt it. Dimitri’s horizon is five to ten years.

About Dimitri Masin

Dimitri Masin is the CEO and co-founder of Gradient Labs, a London-based startup building autonomous AI agents that run customer operations for regulated financial services companies. Before founding the company in 2023 with two former Monzo colleagues, he was among the first 30 employees at Monzo, where he led AI, data science, financial crime, and fraud as the bank scaled to roughly 4,000 people. He started his career at Google.

Cleaned Transcript

Dimitri (00:10): Nothing is correct 100% of the time, right? So I think that’s the most important bit here too, that companies that want to adopt AI need to accept. But that’s fine, right? Nothing is ever 100% of the time correct. These are non-deterministic systems, and humans make a lot of mistakes. So really the real question is, can you create a significant uplift on the quality that a human can produce? Because then you can make an argument: look, I’m automating this process, but it’s much faster, the customer gets a loan or mortgage decision instantly, or close to real time. It’s also a lot more compliant, because you can objectively show that your process complies a lot more often with a set-out standard and policy. So really the way you’re treating it, we typically start in the project and we say, okay, the human team is a benchmark. What we want to do, we want to do it significantly better than the current benchmark.

Peter (01:04): This is the Fintech One-on-One Podcast, the show for fintech enthusiasts looking to better understand the leaders shaping fintech and banking today. My name is Peter Renton, and since 2013, I’ve been conducting in-depth interviews with fintech founders and banking executives. My guest today is Dimitri Masin, co-founder and CEO of Gradient Labs, a company building autonomous AI agents for customer operations in financial services.

Dimitri was one of the first 30 employees at Monzo, where he led AI and data science from day one and watched the bank grow from 30 to 4,000 people. In our conversation, we talk about why the real opportunity in banking is automating the manual operational work behind the scenes, not just the app experience. We get into why co-pilots can actually degrade quality and compliance, and why Dimitri believes full autonomy is the safer path.

He shares the story behind what may be the largest known AI agent deployment in banking, serving millions of customers. We also cover why banks aren’t seeing productivity gains, his five to ten year view on the transformation, and how the US bake-off culture plays to his advantage. Now let’s get on with the show.

Peter (02:28): Welcome to the podcast, Dimitri.

Dimitri (02:30): Thank you, Peter. Nice to be here.

Peter (02:32): Great to have you. So let’s get started by giving the listeners a little bit of background about yourself. I know that you’ve worked at Google and you were early at Monzo. Tell us a little bit about what you’ve done in your career to date.

Dimitri (02:44): Yeah, amazing. Let’s start with Monzo. That’s probably the most exciting part. I joined them quite early on, probably as one of the first 30 people, and was lucky to bet on the right horse, to be honest. I mean, nobody can predict which companies will succeed in the end, but Monzo was a very, very successful bet. I joined them really to lead all things AI and data science from day one. I spent seven or eight years there literally building the bank from 30 people up to 4,000 people, I think, by the time I left. So really, I had an amazing experience throughout those seven years, looking into every single area of the bank very deeply and understanding how banks work from the ground up, and building one yourself. It was an amazing experience.

Peter (03:28): That’s great. And Monzo obviously is one of the most successful UK digital banks. They’ve tried to make it here in the US and haven’t really been successful. So then what was it that you saw? You were still at Monzo, I believe, when you had the idea for Gradient Labs. What was the thing that needed addressing?

Dimitri (03:46): That’s a great question. Retrospectively, if I look at this, there’s obviously this big transformation that fintechs were trying to achieve in financial services, right? They introduced the “at your fingertips,” amazing, flawless experiences where you can really bank just by a few taps on your mobile phone, whereas previously you would need to go into a branch or call somebody up, which is even worse probably. The whole purpose behind fintechs like Monzo was to reinvent that experience, to make it really amazing. And I think what most of those companies, like Monzo and many others, have realized is that while you could reinvent the happy path and the experience, and create very slick apps and very good customer support, a big part of their financial services organization could not be solved very easily. And that’s the manual, repetitive operations, let’s call it customer operations, behind the scenes.

That part is needed to run any financial services organization, and it remained the same, right? In every larger retail bank, you would have probably more than half of the staff really responsible for keeping the lights on of accounts behind the scenes. Even those fintechs attacked first that part, i.e. customer operations. And by the way, when I say customer operations, I mean things like customer support, AML, fraud investigations, KYC, onboarding, screening. So really all the processes that are needed to make a bank work, they were pretty much not reinvented. They were fairly similar to how they were before. And at the beginning of 2023, when GPT-4 came around, it became very, very clear to us that that part of the problem, i.e. the second half of the transformation of financial services to very slick modern experiences, is now possible as well. The first leg was putting your experience into the app, making everything very nice and easy. The second part of the transformation is now creating a significantly more scalable, more efficient organization by doing all of the customer operational work in a much more efficient and much more delightful way for the customers.

Peter (05:59): So you started the company, I believe, with two other co-founders, all from Monzo. What did you bring from your time at Monzo that really helped you get this off the ground?

Dimitri (06:12): Both of my co-founders, Neal and Danai, who are both amazing, were part of my wider team as well at Monzo. So I knew them amazingly well. We worked together for many, many years. We really decided together to build a new company. What triggered it is that we were very, very deep in automating customer operations processes, right? Like probably in every other bank, we had our successes, achieving 10% efficiency gains here and there. But then we acquired essentially a very deep expertise of how everything works under the hood. And then, as I mentioned, when GPT-4 came around at the beginning of 2023, we essentially realized that this whole area will be completely reinvented over the next five to ten years. And so we wanted to drive that change in the industry, and hence started Gradient Labs, which is focusing on, if you wish, in a nutshell, putting banks on autopilot. I.e. creating almost like a customer operations platform where you can write down your processes and procedures and they will be executed by an AI agent in a completely autonomous way, but as safe as humans, or ideally much safer than human teams can, but also with much better customer experience at the end.

Peter (07:25): Okay. So then take us through that, if we could go a little bit deeper there. When you’re in conversations with banks, are you selling a suite of tools that AI agents can go out and do those things, or what are you actually providing to the market?

Dimitri (07:41): I think the closest analogy probably would be if you think about the human operator’s role within the customer operations system, right? If we take a KYC process, there will typically be some KYC system already in place that does straight-through processing for the majority of the accounts. But then there will be, let’s say, 15% of cases which cannot be straight-through processed, because they need a deeper look. Maybe there’s a PEP or sanctions suspicion, and you really need to investigate it in the account. And typically you would have a human team that looks at this exception case and does all the manual work, goes to different websites, maybe applies some decisioning metrics on the information that they’re gathering. And the way to think about us is, essentially, we’re trying to mimic how humans work within the operations teams today. We’re assuming that all the existing systems, so the systems of record, are staying as they are today. But then, if you think about how a human operator works, they go from system to system, collect data here and there to complete an end-to-end workflow with a particular purpose, like KYC exception handling. And that’s exactly how we try to mimic it. We essentially sit on top of the existing systems. You can think about us like an orchestration layer of end-to-end workflows across those different systems.

Peter (09:02): I know Monzo is one of your clients. I read about that. What are they actually doing? And do you have any metrics or something that you can share about how much they’re improving their efficiency?

Dimitri (09:17): I can’t talk about Monzo, but I’m happy to talk about many of the other customers. So the primary motivation for working with us, by any of those customers, you would think maybe it’s cost-cutting, but it’s actually not. I think the primary motivation is typically to improve customer experience. And what we were able to show in every single deployment so far, and that’s literally, we have about 30 customers within the fintech and financial services space that we’re working with, in every single deployment you can achieve a significantly better customer experience in support, for example, in customer service and customer support, than the human team can provide. So typically you see an uplift of around 15% on top of whatever the human team can achieve in that organization, through automation of AI agents. So really, for most customers we’re working with, they primarily care about improving customer experience, but also making themselves more scalable, because most of them are fast-growing fintechs. They want to be able to say, look, we want to keep headcount steady, but we want to be able to support twice as many customers through that headcount. So being able to scale easier with the same headcount and improved customer experience is what really drives the motivation. On the cost side, frankly, we can go deeper into that, the picture is very nuanced. So I try to advise prospects on what they can expect on the cost side. But I would say, if you do the job really, really well, you can save maybe up to 70% of handling time, operational handling time, on a particular process. And if you just scratch the surface and do the bare minimum, maybe you save 20%. So it’s not quite a step change every time.

Peter (10:58): So then when you’re going into a bank or a fintech for the first time and they say, yes, we want to implement, what does that look like? Tell us a little bit about how long that takes and what it looks like.

Dimitri (11:12): So by now we have quite a wide suite of products, right? Because our vision is really to put operations on autopilot. And operations is essentially a long tail of all kinds of processes within a bank. But every company has usually, at any given point in time, some sort of focus area or burning pain point that they’re focusing on. So really, typically the process starts, we start a conversation and try to uncover what are the two or three most important bits that really are expensive or somehow not working well from a customer experience perspective. An example can be, we are talking to quite a few lenders, mortgage lenders, right now. And one of the key problems is it just takes so much time to review a mortgage application, right? Because you need to submit like 20 different documents and whatnot. And for them it’s about, okay, how do we shorten that time and maybe do it close to real time? Somebody uploads documents, gives us all the information, and we can tell them, okay, XYZ is missing, or if nothing is missing, here’s an offer, type of thing. So as a first step, we try to discover what is the most burning thing, because our range of services by now is so wide. And then once we narrow down on two or three very specific high-value use cases, we essentially say, okay, let’s go and try to run a POC. Typically there are also multiple vendors involved, or often enough there are multiple vendors involved, where you almost bake off for a particular use case, right? So a bank would try one or two vendors and compare the performance that they can achieve. That POC phase is typically about four weeks. But obviously before the POC stage, you want to have all the legal work and security work done, and so on. So that’s probably the longest time. But really the implementation is very, very short. The actual implementing of the product to show that it works is like four weeks, initially at least for the initial rollout. And then it depends how deep you want to go, because typically our motion is we implement the first use case, let’s say within the four weeks, and then we essentially say, okay, let’s implement the second and the third and the fourth, because everything is built on the same platform.

Peter (13:23): I’m sure you’ve seen this in the press this year, where there’s a lot of talk about banks implementing AI and they’re not seeing the productivity gains. What do you think is the reason that we’re seeing so much talk about AI and the lack of productivity gains?

Dimitri (13:39): Two, three years ago, the answer was clear to that as well, but nobody was asking, well, maybe because people were not spending so much money on AI. The answer is still the same, and we knew it already back then. So essentially, the cost efficiencies that you can create, i.e. productivity, has a fairly nuanced view. So A, co-pilots. Most of the banks implement co-pilots. Co-pilots are essentially meant to make current workers faster. And we actually tried to do it at Monzo years back, even before GPT-4 came around, where essentially we were auto-suggesting responses to people on the frontline to reply back to the customers. An interesting learning back then was already that if you do that, two things happen. Either people use those replies and not necessarily open a new chat and work faster, but they maybe just do something else. So unless you adjust their targets as well and everything else around them, because it’s a complex human system, you might not actually even be able to see that they’re doing more responses or chats per hour, let’s say, or per time frame. So that was one thing. You need to adjust the whole system, and that’s not trivial. It’s not just giving them a tool. But a second thing, which is maybe even more interesting, is that co-pilots have an interesting effect. If they’re 90% of the time correct, what tends to happen is that people start just accepting blindly what it suggests. And so it might be fine in 90% of the cases, but in 10% of the cases it will be wrong. And so what we started seeing, and what we experienced with our customers, is that if you do that, you actually degrade the quality of the service and the compliance that you will achieve, which is counterintuitive, right? You wouldn’t guess it, but we have seen it firsthand. And so that’s why the core decision for us when we were building Gradient Labs was, okay, we have actually seen firsthand that co-pilots degrade the quality of the experience and degrade the compliance rate, because people just at some point start accepting. What the real solution is, in our minds, is complete autonomous autopilot, end-to-end automation, which doesn’t require a human in the loop. But if you want to do that…

Dimitri (15:57): You obviously need to convince many, many different parties that it’s safe to do. You need to have the right controls in place. You need to have a way to roll it out very safely. So you need to think a lot about the controls that you need to have in place in order to have a fully autonomous system. But now, essentially, for the last three years, we have spent the last three years proving that full autonomy is safer and higher quality than co-pilots.

Peter (16:22): So then how are you ensuring that your AI agents are correct 100% of the time?

Dimitri (16:29): The answer here is nothing is correct 100% of the time, right? So I think that’s the most important bit here, that companies that want to adopt AI need to accept. But that’s fine, right? Nothing is ever 100% of the time correct. These are non-deterministic systems, and humans make a lot of mistakes. So really the real question is, can you create a significant uplift on the quality that a human can produce? Because then you can make an argument: look, I’m automating this process, but it’s much faster. The customer gets a loan or mortgage decision instantly, or close to real time. It’s also a lot more compliant, because you can objectively show that your process complies a lot more often with a set-out standard and policy. So really the way you’re treating it, we typically start on the project and we say, okay, the human team is a benchmark. What we want to do, we want to do it significantly better than the current benchmark. And this way, hopefully your chief risk officer and your governance team will be happy, the regulator will be more happy, and the end customers will typically be happy, because something will be in it for them too, because it’s going to be much faster, much clearer, and so on. So really the answer here is to benchmark it to the current human team, because that’s the work that you’re replacing, rather than expecting a completely 100% true and accurate solution.

Peter (17:50): When you’re implementing this end-to-end autonomous solution, what do the people that used to do that manually do? What have you found at the implementations you’ve done? How are they utilizing the employees that used to run these tasks?

Dimitri (18:10): I think the first assumption would be, there must be layoffs happening because of that. Actually, the counterintuitive truth is that none of the companies we have worked with actually planned or laid off any people in that space, which is maybe counterintuitive at first. But in most cases, companies redeploy those people to create better customer experiences for their customers, right? So sometimes it’s services that they didn’t provide before. For example, you might have an account manager from a certain size of account, and that threshold goes down. Or you spend more time actually investigating more complex issues, or dealing with cases which require a lot more human empathy, like financial difficulties and vulnerability. So in reality, for every single customer of ours, typically the teams got relocated onto slightly more complex, more difficult, more customer-centric tasks, in every possible case.

Peter (19:07): So I want to dive into something I read about which I was really interested to get your take here. It was described in the article I read as the largest known AI agent deployment in banking, handling nearly half a million users with quality that beat the bank’s own human agents. So what can you tell us about this largest known AI agent deployment?

Dimitri (19:30): Well, I think what’s worth noting, and I can’t obviously tell the name, that’s why otherwise we would have disclosed it in the article, but essentially at some point at the beginning of 2025, it was already more than a year ago, in January, we realized we had actually had the first full-scale AI agent deployment with a large, one of the largest regulated banks in the UK. And at some point we just glossed over it, we didn’t even clock it, but at some point it’s like, hey, there’s literally no other large regulated bank that would have deployed it at full scale to their customers. And we started writing and talking about it because it was the first, at least the ones that I would know about. Most large regulated banks with tech teams would have played with AI agents, and there were half-niche cases automated. But in this particular case, it was really deployed at full scale to a bank with 12 or 15 million customers. And throughout the process, obviously, we needed to jump through a lot of hoops to prove that it’s going to be safe and working well. But in the end, we realized that the customer experience through doing that had significantly improved for this particular bank. So I think it was like 15% difference, or better than the human team, the human average team in that particular bank previously. So it was really overall a full-on success story. And we started really beating the drum about it because we realized, my God, this is literally the first bank. And everyone is trying to do that. And we actually had one at full scale.

Peter (20:59): I was at New York Fintech Week in April and I was listening to a panel with a bunch of the largest banks in the US. And it was interesting because they said that they’re all doing pilots right now. And these are obviously very big, complicated institutions that do things very carefully. But what was interesting to me, and I’d love to get your take on this, is that they said right now we’re doing pilots, and then someone asked the question, okay, in two years’ time, what percentage of the bank’s processes will be done by AI agents? And they said 50%. And right now it’s zero. So that seemed to me to be a little bit optimistic. What’s your take?

Dimitri (21:47): I’m pretty sure it’s a bit too optimistic. I mean, we’re at it now for three years, and it’s worth saying that the core technology that needed to unlock that use case was there at the beginning of 2023. GPT-4 was powerful enough to do that use case well, right? So whenever the frontier models improve now, it doesn’t necessarily drive too much gain or improvement in products like ours, because the operations space is, you don’t need a PhD LLM model to do that, right? So really, what’s happening is that even though the product is there, and some companies, especially fintechs at the beginning, have proved that it can be very safe, or safer than human, more compelling than human, and create a much better experience, you still wouldn’t say that every bank has somehow adopted it, even though those products exist. And I think it’s more about the technological diffusion, in how decisions and things happen in those companies, right? To give you a very, very specific example, that happens very frequently and, more recently even, started to happen more frequently. The typical conversation that I had with fintechs or banks probably a year and a half ago, one year ago, especially Tier 1, Tier 2 banks, is like, we’re definitely going to build it ourselves. We have thousands of engineers. Why would we ever partner with somebody else? We’re just going to build it, we can build anything. That was the typical answer. And then I think typically companies have spent maybe a year or 18 months on running pilots, and have in many cases learned that while they can build something internally, the technology is evolving so fast that it’s almost impossible to keep up with specialized providers and vendors who are essentially just doing that one thing. And so essentially even companies who build themselves are realizing, okay, we’re nowhere close to the frontier capability, to the safety that you can achieve through some of the providers that are out there. And so really the situation started to flip probably at the beginning of this year, where I’ve started seeing the first cases where, okay, we have done it for 12 or 18 months, we are now realizing that we either invest hundreds of people to do it properly, to keep up with the technological change, which is really happening every two months, or we build infrastructure in such a way that enables us to plug in the best providers from…

Dimitri (24:10): …agent providers from the outside, in a safe and easy way, into our infrastructure. This way we can bet on the best provider. And if they’re not the best anymore, we can switch them out for something else. But we, as a bank, are going to build the core infrastructure to be able to plug those providers in. So that’s the mindset shift that happened. My answer is, it wasn’t like a straight path of just build, deploy and it works. It’s like a bit of a zigzag, where you try one thing, you realize after a year it didn’t quite yield results, and now I’m trying something else.

Peter (24:41): Right. So two years may be optimistic, but I’m guessing you think five years is probably pessimistic, right?

Dimitri (24:47): Well, for me, when I talk about it, I talk about the five to ten year time horizon, where the larger-scale transformation will happen. Even on the support side, right? To give you an anecdote, the support side, customer support, customer servicing, is the easiest use case that we started with three years ago, right? That’s what you start with typically. Because for most people they perceive, it’s just like a ChatGPT-style application, so what could go wrong? That’s the easiest application. And there it has a very interesting dynamic, right? If you look at all the customer support, even with the companies that we’re working with, you can automate fairly quickly 50% of all the inbound queries that customers are contacting you with, let’s say on the calls or via chat. But those first 50% of the queries that you’re automating actually yield roughly 20% of the time savings that you achieve. So what it means is that those 50% of the queries that you’re automating are just the easiest ones anyway, that would have taken the human very, very quickly to resolve as well. So you automated half of the tickets, but only saved 20% of the time. And so really the whole crux of the problem is essentially, how do you automate the remaining 50%? And we’re doing it with a few very large customers today. We are on the journey from 50 to 80, from 50 to 90, already for close to a year. And they are fast-running, tech-forward companies. And it still takes a year to go from 50% to 80%, where you really see step changes. So based on that, and that’s the easiest possible case, right? Now if you take a step back and say, okay, customer support is the easiest possible case, but there are all kinds of heavier compliance processes, like money laundering reviews or account freezes and things like that, they’re a lot harder to automate. And if you actually go after the long tail of all those things, I can’t see it taking two years. Even for the easiest stuff, it takes already a year, and you’re maybe automating 5% of all the operations in a bank. So really five to ten years is, for me, the horizon. Not because the technology is not there, the technology is there already. It’s about the technological diffusion, in how organizations move and adapt, that is where the bottleneck is, I think.

Peter (27:13): Okay, so you’re based in London, but you have been here in the US, I believe, for almost a year, and you already have some fairly big-name fintech customers. Current and Stash both seem to be publicly named customers of yours. So what’s the plan here in the US?

Dimitri (27:32): So the US is a pretty big market. It’s a very interesting one because it has fairly large fintechs, right? And that’s one of our ICP segments. It’s also interesting from the perspective that US fintechs buy slightly differently. So there’s this difference between the US and Europe. In the US, I found that companies are a lot more performance-oriented. So essentially they do those bake-offs. When we are talking to big, large fintechs in the US, they say, we’re going to try two, three, maybe even four providers sometimes. So they are thinking about, okay, what is the infrastructure that needs to be in place for us to test as many providers as quickly as possible? Because they have learned that it’s very, very difficult to evaluate providers as a tabletop exercise. Because 90% of the agentic work is hidden below the surface, right? You don’t know how it’s going to do in the wild before you actually try it out. And so they made it all about bake-offs. And that actually suits us particularly well, because so far, because we are such a specialized provider, we serve only fintechs and only financial services, we have actually won every single bake-off that we’ve run against any other company. So that suits us very well. That’s why we are going after the US market, because we actually want to have bake-offs. Because whenever we have a bake-off, we know that we can win. Where it’s harder for us is because we are a smaller brand, because we are niche and vertically focused. We obviously don’t have a large tech brand, so it’s harder to get in the door. But once we’re in, we always win on the technical merits. So from that perspective, the US market is a very interesting one for us. Because in Europe you typically pick one provider and go with them and hope for the best, whereas in the US, not always, but many times, the bake-offs are the standard.

Peter (29:19): Okay, so then last question. What’s your vision for Gradient Labs? I mean, the opportunity seems monstrously large, but what’s your vision?

Dimitri (29:29): No, 100%. In a nutshell, if you think about putting a bank on autopilot, essentially making it significantly easier to operate and reinventing the whole industry and how it works today, that would be quite something to achieve, right? Because that’s what drives me every day. So I think about transitioning from traditional banking to fintech, that was the first leg of the journey. And the second leg of the journey is transforming how companies or banks operate from the ground up, and making it infinitely scalable. That’s where the time is going, where the trend is going. And that’s what we want to be driving, right? So we want to invent that future, drive that future, build towards the future. That’s what we are building.

Peter (30:09): It really is interesting. I mean, you are at the absolute cutting edge of technological change here in financial services, and it’s really interesting learning more about the company, Dimitri. Thanks so much for coming on the show, and best of luck.

Dimitri (30:23): Amazing. Thanks a lot for having me.

Peter (30:31): Every day we hear news about one large company or another doing a round of layoffs in response to AI automation. That is why I was so interested in Dimitri’s answer when I asked what happens to the people who used to do this work manually at their fintech and bank clients. He said not one of his customers has actually cut staff in that space. Instead, they redeploy people to the harder, more human-centric work, the complex investigations, the cases involving financial difficulty and vulnerability. It’s a useful counterpoint to the doom narrative around AI and jobs, at least for now. Whether that holds as automation continues to improve remains to be seen. But it is quite possible this doom narrative is overblown. Anyway, that’s it for today’s show. If you enjoy these episodes, please go ahead and subscribe, tell a friend, or leave a review. And thanks so much for listening.