2025 Ripple Rewind

Dan Loney: Hello, and welcome to a special edition of the Ripple Effect podcast. Today, we're doing something a little different. We've hand-picked some of the most impactful, thought-provoking moments from our episodes over the past year and compiled them into this “year in review” episode. So whether you're a regular or tuning in for the first time, prepare for a journey through what the Ripple Effect has offered this year. Let's dive into these memorable moments and relive the insights and inspirations together.

We kick off our rewind in January with Kevin Werbach, who helped us understand where blockchain technology stands after more than 15 years of development. From Bitcoin to Web3, Kevin explored whether we're looking at the future of finance or still searching for blockchain’s killer application.

Loney: You've done a lot of research and work in and around blockchain. Where do you think we stand right now?

Kevin Werbach: Blockchain technology has been around since 2008, when Satoshi Nakamoto — and we still don't know who that is or was — issued a white paper developing the idea of bitcoin. Bitcoin is a cryptocurrency. It's a form of digital asset. But it rides on this ledger technology called blockchain. And blockchain is much more general in terms of its applications.

We're something like 16 years in to blockchain technology being widely available. It was built on even earlier foundations, and the question is whether that's a long time or a short time. We see a great deal of interest. We see lots of experimentation. We see lots of trading activity around digital assets. We see building of distributed platforms on the vision of Web3, a kind of distributed internet, powered by tokens. We also see use of blockchain in payments. And we see companies using it as a record-keeping technology for understanding information as it flows across their networks and between companies. We don't yet see any real scaled use cases that are not predominantly powered by financial investment-type incentives.

Loney: How do we take it from where it is now to get to that point of a larger scale use of blockchain?

Werbach: It's not clear exactly what blockchain is going to be best for. I've been studying this for a decade at least. I always say there are these different application categories, these different use cases. It may be that the significant use case is just as an investment asset, and that's really the one that has the most heft around it in terms of the activity. Or it may be the case that when we look back 20 or 30 or 50 years from now, we say, “This is the new rails. The underlying technology that powers the financial system as it evolved to the next stage.” Or we might say something else.

I don't think we should assume that we know the answer to that. We should think about looking at applications and use cases as they develop. Then if it actually is a better technology, if it really has advantages, then it'll ultimately succeed in the market.

Loney: Kevin's perspective reminds us that transformative technologies don't always reveal their true purpose immediately. Speaking of transformation, let's move to February, where we explored how AI is reshaping productivity.

In February, Daniel Rock joined us to tackle what he calls the productivity paradox. Why does it take so long for revolutionary technologies like AI to show up in productivity statistics? Daniel's insights help us understand the lag between innovation and measurable impact.

Loney: Daniel Rock is an assistant professor of Operations, Information and Decisions here at the Wharton School. He and colleagues have looked at how AI can impact productivity. Dan, great to have you here today. Thanks for your time.

When I bring up AI, doesn't it seem like productivity is a natural first thing for people to think about?

Daniel Rock: Oh, absolutely. And I think when we talk about productivity, it's important to define terms here. For economists, productivity can be a few different things. It can be how much output per worker you have, how much revenue per unit of input. But generally, all of it points to one big idea. Which is, what are the number of outputs we get per unit of input? It's not, how do we cut jobs, necessarily, or reduce the resource use. It's also, how do we create more? I think with these tools empowering people to do greater and more interesting things, productivity in the long run has to be positively impacted by what we can do with them.

Loney: You say that there's a paradox when you think of this.

Rock: Here's the core thing with a sufficiently transformative technology, what economists would call a general purpose technology. That is, it's pervasive. It improves over time. And then it necessitates and spawns complementary innovation. That is, the other stuff you need to build to get this stuff to go.

So yeah, there's a lag. It takes a long time to build up those additional assets, to reconfigure your organization, to train people to use stuff. Over time, that's going to pay off in a big way. And we're seeing people make huge investments in that. But it's not going to be right off the bat super powerful. Actually, it's funny. With AI, there are some applications that are right off the bat super powerful. But the long-run implications are going to take a while to play out, I think.

Loney: You have four areas of potential impact that you've come up with in the work that you've done, the first being false hopes. Explain that a little bit.

Rock: This is the explanation for the paradox, why it takes a while. I already preempted what I think is going on. This is sort of a Bob Gordon view. I don't want to put too many words in his mouth. But AI just isn't that big a deal. Or you could broaden this to say any technology just isn't that big a deal. We see lots of promise and hype, but it's just never going to materialize. That's a consistent way to view the world in the early stages, if you don't know what's going to happen. But then you do have to change tack if you see the benefits start to show up. I think with AI, we're starting to see that a bit.

Loney: The idea that transformative technology requires time to reshape organizations is crucial. And speaking of things that impact all of us, but often in unexpected ways, let's turn to March and the psychology of taxes.

March brought us Alex Rees-Jones, who revealed how psychology plays a surprising role in how we think about our taxes. It turns out that our minds process tax losses and gains in ways that aren't always rational. And understanding this can reshape tax policy.

Loney: Did you know that there's an element of psychology that comes into play when we think about paying our taxes? Especially when you look at things like losses and gains. Wharton Professor Alex Rees-Jones has taken a deeper dive into this component of taxes, and he joins me here in studio to talk about it. What was it that first got you thinking about psychology in taxes?

Alex Rees-Jones: When I was in graduate school in the late 2000-aughts, I was very interested in behavioral economics, which is an area of economics that's all about trying to build more models from psychology into the way we do regular economics. At that stage, that was a very well-developed field, but it wasn't yet as successful as it could be, because a lot of it was focused on, small lab experiments. There weren't an enormous number of demonstrations of this stuff being really useful to think about big economic behaviors.

A thought I had is, “I think people could be influenced quite a lot by psychology when thinking about taxes. If that's true, that would count as a big economic behavior. And understanding how to model it better through psychology would be really useful just for doing regular economics.” That turned out to be right, I think. There's been many other examples like that in the years since. But that's what initially was my point of entry into the field.

Loney: I mentioned at the top that component of losses versus gains. I know you wrote about it in part about the losses component, about how we think about working the losses that we have in our tax preparation every year.

Rees-Jones: Let me summarize the idea on how losses and gains map into it. The thing I was trying to get at in this study was how the idea of loss aversion would play out in tax settings. Now, the term “loss aversion” sounds pretty general. You can imagine it applying to a bunch of things. But when behavioral economists use it, they mean a very particular thing. They're referring to this reproducible finding that people seem to value slightly increasing a gain, less than slightly decreasing a loss. To illustrate, let's say I'm asking you how much you value $1 that I'm holding. And you think about it, and you would say, “Oh, $1 is $1. I'll think of my value for that.”

Then to put in different frames, I could say, “Well, what if I already owe you $10, so this dollar is getting mixed in and turning $10 to $11?” Or I could say, “What if you already owe me $10, so this is turning you paying me $10 into you paying me $9?” And in either case, I'm just giving you an extra dollar, $1 is $1. But the thing you see, very reproducibly, is that people care a fair bit more about making the loss a little bit smaller compared to making the gains bigger. That's not so surprising to many people, right? People don't like losing. People don't like losses.

Loney: Loss aversion in taxes is just one example of how human psychology shapes economic behavior. In April, we continued exploring financial psychology through the lens of retirement and Social Security.

April gave us two powerful conversations about retirement. First, Sylvain Catherine showed us how including Social Security wealth completely changes our understanding of wealth inequality in America. Then Stew Friedman reminded us that retirement isn't just about finances. It's about finding purpose and meaning.

Loney: Let's start with the backstory on looking at these aspects of wealth inequality tied to Social Security.

Sylvain Catherine: When people think of retirement, a big part of it is Social Security. For most Americans, most of their income during the retirement period does not come from the stock of wealth that they have at the beginning, but comes from the Social Security benefits that they receive. Now, all those promises that the government makes, they have a value. You could think of it like, if you were to go on the private market, you could buy an annuity. And that annuity would basically offer exactly the same type of terms as Social Security. It would provide a monthly payment until the end of your life. So, there is a market value for what the government provides.

Once you try to value those benefits — the ones that you have already accrued because you have contributed into the system — what's the value of this? How does it change the level of inequalities that we see today? And does it change the trends in wealth inequality? Because when we look at wealth excluding Social Security, we see a steady increase in wealth inequality since, more or less, the mid-1980s. But what we find in our paper is that once you factor in Social Security, this positive trend in wealth inequality basically disappears.

Loney: When you think about value for Social Security, how has that changed over the last several decades?

Catherine: It has changed enormously, and this has implications both for households but also for the government. Because what we consider as an asset for households is going to be a liability for the government. We are talking about, I think right now, something that is close to $50 trillion. Where the total stock of wealth excluding Social Security in the U.S. would be slightly more than $100 trillion. So, you have one-third of the total that is Social Security, which was not considered in inequality statistics before.

Loney: There's probably much more of a reliance on Social Security for lower income families than it is for higher income families.

Catherine: Exactly. In general, as you move up in the income distribution, people receive higher benefits. But that slope, that relationship, is much less pronounced than if you look at wealth in general, and because there is much less inequality in Social Security benefits, adding it to the bucket of the things that you consider as wealth totally changes the picture that you have when you trace the level of inequality over time.

Loney: Sylvain’s research revealed that Social Security represents nearly $50 trillion in household wealth. But as Stew Friedman explained, successful retirement requires more than financial planning.

Stew Friedman: You've hit the nail, in the sense that the most important feature of finding harmony among the different parts of life as a leader in all of them is to be grounded in your values. Your sense of purpose. The meaning of your life.

It's the age-old question. But the answers to those questions change as you grow, as new opportunities emerge, as others disappear. Looking for something that creates a sense of purpose, a sense of meaning, value, like you're doing something that matters, is the most important aspect of anticipating and then working through the phase from full-time work to retirement.

Loney: The intersection of work and life doesn't end at retirement. It's something we navigate throughout our careers. In May, we examined how this plays out in workplace relationships and social media.

Nancy Rothbard joined us to discuss the complex dynamics of workplace relationships in the age of social media. Should you friend your boss on Instagram? How much should you share? Nancy's research offers practical guidance for navigating these modern workplace dilemmas.

Nancy Rothbard: It came up a lot in terms of how people thought about these questions. They might be very comfortable connecting with peers, but very uncomfortable connecting across levels. One of the funniest things to me was when we would talk to people about this, a lot of times, they would equate connecting with a boss on something like Facebook or Instagram as equivalent to connecting with their mother. It was sort of the same horror that they would express as they talked about those relationships.

Loney: How should both companies and the employees think about that process? Because there are some dynamics that could get a little tricky along the way.

Rothbard: Absolutely. I think that a lot of companies, for example, have social media policies. It's not necessarily who you're connecting with, but more what you are posting. Are you allowed to post stuff about work or about people at work? Or sometimes it is about who you're allowed to connect with so that there's no conflicts of interest.

But what I would also say is that connecting on social media  —  especially throughout the last couple of years where we've engaged in more remote work, more hybrid work — is also an opportunity to know what's going on in people's lives. Just banning it entirely may not be the right approach. One has to think judiciously about who do you connect with, and what do you disclose to them? What is that relationship that you're willing to cross that boundary, so that you're willing to blur the line between professional and personal?

Loney: You also talk about this element of self-disclosure, and the fact that it can ease and better the opportunity to have a relationship or a friendship outside of the office. When somebody is maybe talking about their kids or traffic or some component outside of what would normally be discussed in terms of the business.

Rothbard: Absolutely. One of the things that we found in the study is that people will be much more comfortable connecting to other people who disclose personal information. It doesn't have to be deeply intimate personal information. By the way, cute dog pics are a very, very hot commodity. If you have a cute dog and you want to post pictures of them, that's a very good strategy. Because people always love them. They feel like they know you, and they feel connected to you. And it gives them a sense of warmth.

Loney: Self-disclosure and boundary setting at work. These themes became even more relevant as we moved into conversations about entrepreneurship. In June, we explored what it takes to succeed as an entrepreneur.

June featured Lori Rosenkopf, who shared insights from her book on entrepreneurship. One key takeaway? Most successful entrepreneurs aren't the celebrity disruptors we read about. They're resilient problem solvers working in every sector, including social entrepreneurship.

Lori Rosenkopf: I talk about an entrepreneurial mindset, which has six R's, and one of them is resilience. Each of the entrepreneurs that are profiled in my book had many, many obstacles and challenges. Part of their ability to succeed was having a growth mindset, some people call it grit. But a mindset that allows you to say, “I'm going to need to try a lot of things in order to get to the finish line. I'm going to hear negative feedback, and I'm going to treat that as a learning experience and adapt my efforts in order to do better with that. I'm going to deal with crises like Covid or the current economic conditions.” There are many, many ways to solve these sorts of problems. Each of my entrepreneurs are incredibly resilient, and that's something that everybody can work on, understanding that feedback is always a gift.

Loney: How many of the group are the so-called disruptor? And in general, is there a ratio of disruptors that you expect to see coming from the role of entrepreneur?

Rosenkopf: I love that question. Because disruptors are the people who we see all the time in the media. These celebrity entrepreneurs, in many cases. It turns out that disruptors are typically venture-backed because they're trying to overturn an entire market. Venture-backed entrepreneurship is far less than 1% of all of that enterprise that's out there, so it's very uncommon. But when done well, the returns are enormous, so they are getting most of the attention.

Most efforts toward disruption fail. There were many, many car sharing services that were attempted. We had one that students were working on at Wharton way back when. And now, of course, Uber dominates that market. Lyft is a very serious second, and Waymo is automating it completely. But there were hundreds of attempts at car sharing. So most disruptors are not successful, but all the attention goes to the small percentage that are.

Loney: We're in a time where social issues have become very, very important across the board of how we live our lives right now. Are we seeing a sharp rise in social entrepreneurism as well?

Rosenkopf: I've been at Wharton for over 30 years. Certainly 30 years ago, we weren't seeing a lot of students saying, “I want to be social entrepreneurs.” These days, in our efforts at Venture Lab, we see about one in seven of our students who are doing something entrepreneurial will raise their hand and say, “I'm a social entrepreneur.” That doesn't sound like a ton. But if you ask the students a different question, “Do you have impact goals in your company?” Meaning, do you have other goals beyond your financial ones? Then 70% of them will say, “Well, of course I have impact goals.” With the emphasis on  the UN Sustainable Development Goals and all the interest in climate tech and other sorts of community issues, we're seeing more and more students and more and more alumni build those aims into their ventures.

Loney: The entrepreneurial mindset of resilience and adaptation applies beyond startups. In July, we explored how these qualities matter when organizations adopt generative AI. Stefano Puntoni brought us crucial research in July about generative AI's impact on employee well-being. While AI can enhance feelings of competence and autonomy, it can also threaten them. Stefano explained why the conversation matters just as much as the technology itself.

Stefano Puntoni: At the same time, you need to marry that technical effort with a management and leadership effort, which is targeted at employees, to understand and explain, what are we doing? Why we're doing it? What's in it for that employee? If it's going to be actually a threat to their career and livelihood, or is it going to be benefiting them in some way, and how? And how can you do that with an authentic voice.

I think it's important to have both going at the same time. If you do only the technical stuff, but you drop the ball on the communication and leadership piece, I think you cannot expect very good results.

Loney: What are some of these threats that you believe are able to come forward here?

Puntoni: In our paper, we adopt a very famous psychological theory that we find useful to help organize our thoughts in this area. Basically, we say that psychological well-being is really a function of experiencing feelings of competence, of autonomy and of relatedness. These are the components of this self-determination theory going back to the ‘80s, so it's been around for a long time. These are three important antecedents of psychological well-being.

Then we basically argue that gen AI can have important benefits for all of those. It can make you feel more competent. You're able to do things that you couldn't do on your own before, because gen AI makes it possible. For example, to do advanced analytics using natural language. It can be empowering. So, it can give you feelings of autonomy when you realize that now you can do this. There is a sense also of being independent and not being able to rely on others. It can help with relatedness, when basically these chatbots are creating these seamless parasocial experiences and can embed themselves into a team or workflow.

So there's these benefits. But at the same time, gen AI can also be a threat to all of this. It can be a threat to competence. There are all these discussions about jobs, so people are wondering about the value of their skills. It can be a threat to autonomy, because now they feel that they have to adopt these tools and are no longer in control of their workflows. They have to delegate to these AI systems. And it can be a threat to relatedness, when you feel alienated from your team or from the company because you feel this has been deployed in a way that is threatening to you.

Loney: Isn't it a fine line between the two, in terms of the impact that an employee could feel?

Puntoni: Yeah. I think the potential is enormous for boosting psychological well-being, productivity, and performance. But the reality is that in an organization, the conversation is not really oriented toward the psychological well-being and career advancements of the employees who start to use this technology. A lot of the conversations in business around gen AI are about cost cutting, about productivity increases to the detriment of headcount. Those conversations are clearly threatening to people. You cannot expect people to hear this stuff and think, “Yeah, that's fine by me.” It seems like to me there's a lot of potential for boosting psychological well-being of employees. But in practice, a way that lots of conversations are going are pointing in exactly the opposite direction.

Loney: Technology continues to reshape not just how we work, but also how we shop. In September, we turned our attention to the evolving world of retail and luxury brands.

John Zhang joined us in September to discuss luxury brands, counterfeits, and the strategic choices these companies make. Why do luxury brands sometimes produce more rather than less? John explained how scarcity, innovation and brand loyalty all play into retail strategy.

Loney: Is there an element of how much the luxury company is producing, designed to counteract the levels that the knockoffs are producing? The more you produce of the better product, the more people may stay away from the knockoffs?

John Zhang: There has always been a risk there. If you're the luxury brand company, obviously you want to stay ahead of the game and stay ahead of the curve. You want to produce more, innovate more, so that you basically keep distance between you and the counterfeiters. That's always a good strategy.

Loney: How much does the brand still carry a lot of the weight in this decision process? When you think about some of the names out there — Hermes, Versace, etc. — you have an expectation of a higher quality product, and that brand draws the attention of the consumer.

Zhang: Those brands carry a lot of weight, and they sell a lot of goods. They still command the loyalty of a lot of customers. For Hermes, for instance, you could wait for a couple years before you can buy anything, even if you have the money. Not only that. In fact, nowadays they look at your total purchase before they actually let you buy some of the new products.

Loney: What does this all mean for retail in general?

Zhang: I think the luxury brands are constantly innovative. They want to come up with newer products and better products. They also retain some of the classic ones. I think that in that particular game, the luxury brands will always win. The reason is because, just imagine society without luxury brands, life will be very, very — what would be the word I'm looking for? It would be very —

Loney: Boring.

Zhang: Boring. Probably boring. And colorless.

Loney: From luxury retail to hidden markets, October brought us insights into why some of the most interesting markets are the ones we don't immediately see. We closed out our year with Judd Kessler in October, who introduced us to the concept of hidden markets. From college admissions to restaurant reservations to this summer's Labubu craze, Judd showed us how markets often work in ways that defy traditional economic theory, and why that matters.

Judd Kessler: When I talk about, why do firms choose not to raise the price just to clear the market — what the economists say is the optimal thing to do. Sometimes it doesn't make sense to do it because of what you're selling. If it's admission to an elite college or university like Wharton or the University of Pennsylvania, we don't just raise tuition until we fill a class with the people willing to pay the most, because that's not the point of the institution. The experience of being here is, we want to admit only the best and brightest and have them get to interact with each other. We think our teaching will be most effective for that group. So, we are very selective in that market for picking the group that is right for us.

That same thing is true on the labor market, which is another of these hidden markets where you don't just lower your offered salary until you get one person apply. You have to work through the applicants and pick the right one.

But some of the firms, when they do this strategy, it's not just about finding the right people. It might be about bolstering future demand, right? Maybe the restaurant likes the line around the block, because then it builds demand for the next night when people walk by and are like, “Oh my God, how do I get into that restaurant? It looks so good!”

I think this is true of a lot of fad crazes. This summer it was Labubus. I don't know if you came across this. They're these little stuffed animals. They remind me of Beanie Babies, or when I was a kid, Cabbage Patch dolls, where everybody wants one and they're impossible to get. I don't think it's just that they have trouble producing them. I think they want a mania around them, so that people will get excited by buying it. That creates these hidden markets that might not otherwise exist.

Loney: How common are hidden markets?

Kessler: They're definitely more common than we think. When I started talking about hidden markets and thinking about all the ways that they crop up — I mean, I was writing a book on it, and I was also surprised how often they pop up. And they pop up in these environments that are particularly important.

Loney: From blockchain to AI, from taxes to retirement, from workplace wellness to hidden markets, 2025, has been a year of remarkable insights here on The Ripple Effect. Each conversation reminded us that rigorous academic research has real world implications for how we work, invest, consume, and live.

As 2025 comes to a close, if the insights in today's episode have sparked your curiosity, please remember to subscribe. We're looking forward to another year of delivering unparalleled insights from the Wharton School.

I'm Dan Loney and thank you for joining us for this special Ripple Rewind. We'll see you again in the new year.