Economics

Everyone Loves Economics Podcast Series

Episode 2: The Hidden Costs of Innovation Transcript 

Larkin: Welcome to today’s episode: “The Hidden Costs of Innovation: How Engineers Are Paying the Price for Progress.”  I’m your host, Kate Larkins, and this is Everybody Loves Economics. Imagine this: engineers are celebrated for their groundbreaking ideas, pushing the limits of technology, and solving some of the world’s biggest problems. But behind every new invention, there’s a side we don’t often see—the hidden costs. These costs are the unexpected financial struggles that arise as engineers push the boundaries—like project delays, budget overruns, and unforeseen risks.

Now, you might be thinking, why should this matter to you? Well, here’s the twist: these hidden costs don’t just impact engineers; they ripple out and affect all of us. From the prices you pay at the store to the jobs that are created or lost in your community, the economic struggles behind innovation shape the world around us. Whether you're buying the latest tech, waiting for faster services, or navigating the job market, understanding these hidden costs gives you insight into why progress can feel slow and why the costs of new ideas often show up in surprising ways.

As technology evolves faster than ever, engineers are under immense pressure to create cutting-edge solutions. But it’s not just about creativity, it’s about balancing that creativity with the reality of keeping costs in check. In this episode, we’ll uncover the key financial risks engineers face when working on innovative projects, and how those risks don’t just affect engineers; they impact all of us. Stick with us, because by the end of this episode, you’ll have a clearer picture of the hidden costs of innovation and how they’re shaping the world we live in.

Now, imagine this: Back in 2009, the UK government introduced HS2, an ambitious high-speed rail system meant to connect London to the north of England, aiming to reduce travel times and boost economic growth. The initial estimated cost? $45 billion— and it seemed reasonable for such a transformative project.

However, as construction progressed, unforeseen challenges emerged. Engineers faced difficult ground conditions, complicating excavation and inflating costs. Inflation also played a role, pushing up labor and material prices beyond initial estimates. What began as a manageable project soon ballooned in complexity, driven by design changes, political pressure, and environmental concerns, further driving up costs.

By 2023, the estimated cost of HS2 had skyrocketed to over $120 billion, far surpassing the original estimate. The government had no choice but to scale back the project, canceling parts like the Birmingham to Manchester leg.

So, what went wrong? Was it poor planning or were these hidden costs always lurking beneath the surface? The truth is, a combination of factors—unexpected ground conditions, rising inflation, and an ever-expanding project scope—led to this runaway cost increase. The HS2 saga is a powerful reminder that even the most ambitious projects can veer off track when the true costs of innovation remain hidden, until it's too late.

Consider Nokia, once the dominant force in mobile phones. Despite its well-engineered products, it was eventually overshadowed by the iPhone. This shift highlights a hidden cost engineers face: market competition. Even the most technically advanced products can fail if they don’t align with evolving consumer preferences or the competitive landscape. While Nokia focused on hardware features, Apple prioritized user experience, showing that simply adding more features doesn’t always create value. Consumer needs are ever evolving, and what was once considered an innovation may no longer meet market demand. Engineers must also balance scalability and production efficiency- a well-designed product can still struggle if mass production isn’t cost-effective.

Innovation isn't just about new technology- it's about navigating the hidden costs of intellectual property, public perception, and regulation. Take agricultural biotechnology as an example. Technology may be groundbreaking, but behind the scenes, innovators face battles over intellectual property. In places with weak protection, a product can be vulnerable to theft, risking years of effort and investment.

Public resistance also plays a critical role. Despite the scientific evidence supporting genetically modified organisms, consumer fears make them a tough sell. To overcome this skepticism, companies must invest in labeling and marketing, turning what should be a breakthrough into a costly challenge.

Then there’s regulation. The approval processes for new technology, especially in biotechnology, are long and costly, often stalling progress. Whether in agriculture, engineering, or other fields, innovators must navigate these invisible forces.

So, how many innovations have never seen the light of day because of these obstacles? Would we have more groundbreaking technology if these hidden costs didn’t exist, or are they an inherent part of the progress we strive for? 

Today, I’m sitting down with Dr. Tim Roberson, an economist whose work focuses on the financial risks tied to engineering innovation- especially in the energy sector. He works closely with engineers to analyze the economic side of new technologies, helping companies and policymakers understand the real price of progress. In industries where one financial misstep can mean the difference between success and failure, his expertise is invaluable. Dr. Roberson, thanks for joining me.

Roberson: Thank you for having me.

Larkins: Before we dive deeper, I’d love for our listeners to hear a bit about your background. Could you tell us a little more about what drew you to this intersection of economics and engineering?

Roberson: So I was always interested in engineering growing up. I had a very analytical mind growing up, so I was always interested in engineering style questions, but I got interested in economics in college.

So that led me to the field of environmental economics, which led me to electricity production. Because obviously, producing electricity causes a lot of pollution. That led me to work a lot with data on electricity markets and kind of the theory on how electricity markets work, which led me after my Ph.D. to get my first job at the University Tennessee Knoxville working under a grant from the TVA, after two years in Knoxville, I went to New England, where I worked for the New England Independent System Operator And so, I was there for two years, helping them design auctions for electricity before I came back to academia. I came back to Tennessee Tech last year.

Larkins: Yeah, and I mean, that’s such a cool path—starting with engineering questions and ending up shaping how electricity markets work. You’ve really had a front-row seat to see how the technical and financial sides connect.

It seems like engineers are great at tackling technical problems, but the financial constraints can be just as tricky. In your experience, what are some of the hidden costs that engineers and companies don’t always see coming when they’re working on new innovations?

Roberson: Well, I wouldn’t call them so many financial constraints as I would call them people problems. Engineers are great at thinking analytically about technical questions. But a lot of questions may not seem technical on the surface—so I find that a lot of engineers apply their minds to thinking about people's problems in the right way. So, for instance, “What is the best way to hold a meeting?”, “Who should speak and in what order?” I’ve been in a lot of meetings with engineers that ended up lasting for several hours and that were very disorganized and occasionally very emotional. 

I give this advice in my class, and it’s kind of flippant—if you’re an engineer, the only thing you need to remember about economics is that the demand curve slopes down. Because, if you’re trying to launch a product that people can’t use- it’s not going to be successful. So, there’s not really financial problems—it’s more people problems, but it’s really- that’s more what economists think about.

Larkins: That makes a lot of sense — if the people side isn’t working, it’s easy for everything else to get off track too. And sometimes that’s where the real financial trouble sneaks in. A lot of people think cost overruns just mean someone messed up the budget, but even solid plans can blow up. What are some of the unexpected ways projects run into financial trouble?

Roberson: I mean, there’s always uncertainty about how long something is going to take or how much money it’s going to take. One of the things I try to keep in mind is that when starting a new project, there might be one or two really difficult hard problems you want to solve with pen-medium difficulty problems, and then maybe a thousand tiny problems. You might think, “Well I’m going to spend all of my time getting over this difficult step”. But really if you make small mistakes on the simple seeming problems—then that’s going to set you back a long time. Like if you forget to carry through a negative sign in an equation and don’t see it for several months—that’s a tiny mistake on a trivial portion of the problem, but it can create a big setback. So often, what I see when people are planning projects is that they don’t budget time for the tiny mistakes that they don’t know they’re going to make. I think that element of human nature—just ignore the things that you almost always get right but sometimes don’t will lead to underestimating how difficult things become.

Larkins: That’s a great point—those small, overlooked mistakes can have a big impact. When it comes to long-term projects, especially in the energy sector, there’s a lot of uncertainty. What financial risks do engineers and companies face when working on innovations that might take years to become commercially viable, like nuclear energy?

Roberson: For an individual engineer, I think the risk you personally face is kind of disconnected from the risk your company faces. So, for example, right now, a lot of people are very invested in getting nuclear energy restarted. But if you’re a nuclear engineer, you’re not really bearing a lot of that risk if you go work for the TVA and start thinking about how we can build nuclear energy without costing an enormous amount of money. And that’s kind of because, for these really big, complicated projects, they require really big, sophisticated organizations, and you as an employee of that organization won’t bear a lot of that risk yourself. You’ll draw a salary for quite a while and if the investments don’t pan out then after many years, you’ll have to find—you’ll eventually have to find something else to do but you personally won’t be financially ruined if the nuclear investments don’t pan out. Uh you compare that to maybe being a software engineer working for a startup. Whatever the startup is, it might not be the biggest, most sophisticated project, but if it’s just your company and you put all of your time into it and it fails, well, you’re at risk for that. You could’ve worked, maybe for Google, for that entire time and made a lot of money, and instead you put in the exact same work or more into a startup, which didn’t pan out.

Larkins: Yeah, the personal stakes are definitely different depending on where you are. It’s one thing to be part of a big system that can absorb the loss, and another to be out on your own.
I'm curious, though—when it comes to those big projects, like nuclear energy, where mistakes are expensive and hard to walk back, how does that impact future research, especially when it comes to financing new, riskier innovations?

Roberson: Well, this actually gets directly into some of my subject matter knowledge in electricity and electricity markets. So, if you think about the TVA, if they make a big investment and that investment proves to be much more costly than they anticipated, they’re not going to scrap that plant and build another one. They are going to keep using it, and those costs are going to be passed onto consumers eventually. If you live in an area with a deregulated power market, like uh, Texas, or California, or New England or, frankly most of the world at this point. What happens is private companies choose what to invest in. They raise financing, either through selling shares of their project or by borrowing money. They build their project. And then if generating out of that facility proves to be much more expensive than they thought, then they won’t be competitive in the market and they’ll fail, but the cost of that will never be passed onto the consumer. Whether the project is profitable or not, the profits are going to go to the investors.

Larkins: That’s an interesting point. So, when engineers rely on outside funding, it can definitely influence how they approach their projects. The balance between the speed of repayment and long-term growth must be tricky to navigate. How do financial pressures change depending on whether engineers are relying on loans versus selling shares in their company?

Roberson: It kind of depends on the type of outside investment and the project. So, if you’re working for one of these big companies, ultimately, you’re probably just drawing a salary and you’re probably not worried about how you’re salary is financed. It really becomes an issue if you have an idea and you want to start your own company, so there are various tradeoffs. You can try and sell a share of your company to them in return for financing. “What if you take out a loan from a bank?”. Well, the advantage of that is you get a lot of money now, and the bank never owns a share of your company. The disadvantage is you’ll have payments to make on that loan. So, you take out a million-dollar loan to start a company, there’s a lot of pressure to make money quickly to start paying off that loan. Now, some projects might take 10 years before they make money. I think Amazon.com famously took 15 years before they ever turned a profit. So, if they had tried to finance that startup solely with borrowed money, they would’ve been in a lot of trouble just because they wouldn’t have been making enough to cover their debt payments. Now, if you go to a venture capitalist or maybe your rich neighbor and try to sell a share of your idea, the advantage is that if your company fails, you don’t have to pay them back anything. So, what’s best to do kind of depends on the time horizon. If you think you won’t be profitable for 5 or 10 years, it’s often better to sell shares of your idea. 

Larkins: It sounds like engineers really have to think carefully about how they structure their funding, depending on how quickly they expect their idea to take off. Since financial decisions can have such a big impact down the road, is there any other advice you’d give to engineers who want to avoid falling into financial traps early on?

Roberson: 1) You’re all smart people- use your minds to think about organizing people, not just building things. So, take how to schedule the most efficient meetings, how to send the best emails, how to manage projects- take those things seriously. The next big piece of advice I will give is never let your skills slip, and that means taking a little bit of time to make sure that your math skills are still sharp, you know. If you get a job where you’re not doing calculus, you’re not doing statistics every day, you don’t want to forget how to do calculus and statistics. Don’t shy away from the technical side of things just because you work in a technical job. And I think you’ll find over the course of your career that a lot of the most successful people, maybe aren’t necessarily the ones that started as the smartest, but they’re the ones who didn’t fall into the trap of just getting a job and repeating the same things forever and eventually after a long period of time not having the skills that were competitive with newer engineers fresh out of college. Because if you get laid off in a recession in thirty years, you don’t want that to be the end of your career. You want to still have the skills to go somewhere else.

Larkins: Today, we explored the hidden costs of engineering innovation—the financial challenges that often surface only after major projects are underway. From rising expenses in the HS2 rail project to market shifts like Nokia’s downfall, even well-planned innovations can face unexpected hurdles. Dr. Roberson highlighted how managing cost and risk is just as important as creativity, with impacts reaching beyond engineers to consumers and job markets alike.

A special thank you to Dr. Roberson for sharing his insights, and to Dr. Chelsea Dowell and Ms. Debe Yu for their help in making this episode possible.

So, as we look to the future, remember: the next great innovation may come with a hidden price—what steps can we take to ensure it’s one we can afford?I'm Kate Larkins, and this is The Hidden Costs of Innovation on Everybody Loves Economics. Thank you for listening, and we’ll see you next time as we continue exploring the world of engineering and innovation.

Everyone Loves Economics Podcast Series Episode 1: Who Owns Your Voice Transcript

Hamm: Hi everyone, and welcome to "Everyone Loves Economics" and this episode, "Who owns my voice?" I’m your host, Melina Hamm, a student here at Tennessee Tech, and this podcast is part of a series of episodes created by Tennessee Tech Honors students. This episode is going to look at some questions that have arisen since AI has become a frequently used tool in our modern world, as well as in the music industry. All of which ultimately leads to the big question: Who owns my voice?

The Beatles’ Grammy-winning song, “Now and Then” was produced with the help of AI technology. Everyone is aware that we are living in a world where AI is becoming increasingly relevant and more and more fields are being overtaken by the artificial works of AI. And the fine arts, like music and art, are no exception here. The music industry, especially, finds itself at a crucial turning point where AI is either making a revolutionary difference or threatening to dominate the industry.

AI is already able to produce new music and help speed up recording processes through the access of online databases (Ovsiienko, n.d.). The best example of this is the last Beatles song “Now and Then,” which was published in November 2023 (Semancik, 2025). The track made use of AI in the production process and won the first-ever Grammy for a record produced with the help of AI. All of this sounds great so far; however, because of this rapid evolution, the legal ground is nowhere near being clearly established. In the specific case of vocal recordings, AI can replicate the voices of famous artists by using different mechanisms to write and publish new music. As an example, in 2023, TikTok user Ghostwriter977 was able to produce the song “Heart on the Sleeve” featuring the voice of The Weekend and Drake (Semancik, 2025). With the help of AI, the vocal recordings replicated the unique sound of the popular artists’ voices.

This caused a lot of questions to arise: Can this be legally justified? Who owns the music and the rights to publish it? And, do we still own our voice? Most of these AI-generated projects are created without the consent of the owner of the voice, which allegedly violates the copyrights of millions of singers and artists whose music is being sampled. Current copyright regulations do not cover most of these questions, leaving artists and producers unprotected and potentially unemployed in the future.

So, do we still own our own voice? And how can artists' copyrights be protected while AI-generated music is making use of their voices? Looking at some recent data and studies that have been published regarding the issue, it becomes obvious that AI-generated music and copyright regulations are turning into a pressing matter. For example, a study conducted by the International Confederation of Societies of Authors and Composers in 2024 predicts a 25% decline in income for the music industry because of AI (CISAC, 2024). Another study by the French and German music societies “Society of Authors, Composers and Publishers of Music” (SACEM) and “Gesellschaft für musikalische Aufführungs- und mechanische Vervielfältigungsrecht” (GEMA) supports the urgency of the matter as it reports that over 70% of musicians are concerned with the developments and the implementation of AI into the industry (GEMA, 2024). However, the same study also 5 showed that 35% of those surveyed are already incorporating AI in various aspects of their music production (GEMA, 2024).

So, it is obvious that AI is already a big part of the industry and is creating changes that cannot be undone. Therefore, the question now should be how to deal with the consequences and how to make sure that AI is not going to damage the industry but actually help to improve it instead.

To answer some of these questions, I am joined today by Dr. Sean Alley, who is a professor here at Tennessee Tech. Thank you for joining me, Dr. Alley. Before we start, maybe you could just introduce yourself.

Alley: I am Sean Alley. I am an economist. I am the Department Chair in Economics, Finance, and Marketing at Tech. I have a PhD in Economics, and I went to law school after that. So I have some interest in both of those fields, in Law and Economics.

Hamm: Alright, so the first question I have is: Who should own the copyright to AI-generated songs?

Alley: Well, who should own it? Probably depends on where you stand on those issues depends on where you sit. If you are an artist, I am sure you think you. It is my understanding that the way we currently do it, and this sounds about right to me, is that if it is entirely AI-generated, it is in the public domain, and no one owns it. So, anyone can use it if it is entirely AI-generated. If it was done by a human with some help from AI, then the person owns it.

Hamm: So, it seems that the problem we are facing right now is that AI is incapable of producing songs, and those songs have already been published, but there is still this gray area when it comes to the legal rights. So, could we say that right now no one really owns the right to these songs?

Alley: Well, this is an issue that often occurs when social institutions evolve. Our legal institutions are not ready for it, so the law takes a while. In the United States, this is done. The law develops when there are lawsuits, and there aren’t any lawsuits until after there is a problem. And so, when you have something new like AI that may upend an industry, certainly it is going to cause some transition in the music industry, which they have dealt with a lot during my lifetime. You know, the music industry has had to reorganize itself many times, and this is probably going to be one of those times. But you are right. If we have purely AI-generated things, the way the law is currently, then no one owns them.

Hamm: So, what you are saying is that we basically need these lawsuits right now to draw more attention to the issue, so that in the future we will have clearer regulations regarding the copyright issues.

Alley: Definitely. Yes. That is sort of the way the common law works here is that, like when there is a particular dispute, the court will decide that dispute, and then that is the law going forward. And so a lot of times the actual details of how exactly these things work out – so the way Law and Economics scholars consider the law is that the law is out there and we all understand what the law is and when there are disputes, then that causes us socially to discover what the law is and write it down so everybody knows what to do going forward. So, yeah, these lawsuits will be a big issue.

Hamm: So, what do you think needs to change in the future so we can ensure that artists own the property rights to their own songs, and that there are still going to be incentives for them to produce original works?

Alley: I don’t know. We will have to see how it develops. I am not sure that there is anything wrong with the way it is. So, if you were involved and you used AI’s help, then I think it should belong to you. If AI did it all by itself, then maybe it shouldn’t belong to anyone. But, that being said, there are things that AIs wouldn’t be allowed to do just like you wouldn’t be allowed to do it. So, if I recorded a song that no one would ever want to hear, but if I recorded a song of me singing and published it, so it is written down in a fixed thing that is copyrightable. My voice is not copyrightable, but the recorded thing is copyrightable. So, you’re not allowed to just go grab that and tell AI to make another song out of it because I have a property interest in that recording. Your voice is not copyrightable because for it to be copyrightable, it has to be a fixed thing. But the law deals with stuff like that in other ways. It is not intellectual property so much as it is intellectual property-related. Like, in the United States, we have had a lot of issues in this area with athletes being used in video games and stuff. It is the same kind of thing. So, there are non-copyright legal protections for your name, image, and likeness, and those would cover you being able to just have AI mimic my voice, which would get you in trouble with that part of the law.

Hamm: Alright. Thank you so much for joining me today, Dr. Alley, and for the interesting insight on the topic.

So, as Dr. Alley was saying, the current situation is going to bring changes to the music industry. The fact that there are lawsuits and artists being scared for their future is going to put more urgency on the matter. AI has developed faster than the legal framework, but it is time for these laws to catch up. I would claim that the whole situation is going to get to a point where having clearly established copyright laws is going to be necessary to incentivize creativity by giving creators ownership over their work. Because if AI-generated music is not accredited to a human creator, it could undermine these incentives, as there would be less control and potential financial rewards. If AI companies or developers own the rights to every AI-generated music, this could centralize ownership in the hands of large corporations, possibly taking out any competition from smaller creators or independent artists. However, attributing copyright to the creators of the AI might also incentivize innovation and creativity. As mentioned earlier, there are several possible solutions, all of which involve more clarity regarding the legal groundworks, whatever they might look like.

Thank you so much for tuning in and listening! Make sure to listen to the other episodes of “Everyone loves Economics” as well, and don’t forget to listen to some good music today!

References

Berger, V. (2025, January 3). AI’s impact on music in 2025: Licensing, creativity and industry survival. Forbes. https://www.forbes.com/sites/virginieberger/2024/12/30/ais-impact-on-musicin-2025-licensing-creativity-and-industry-survival/

Carrero, E. (2024, July 16). Voice Deepfake: Is it possible to detect a fake voice? Mobbeel. https://www.mobbeel.com/en/blog/voice-deepfake/

GEMA. (2024, January 30). Study: AI and music. https://www.gema.de/en/news/ai-study

Ijiga, O. M., Idoko, I. P., Enyejo, L. A., Akoh, O., Ugbane, S. I., & Ibokette, A. I. (2024, February 28). Harmonizing the voices of AI: Exploring generative music models, voice cloning, and voice transfer for creative expression. World Journal of Advanced Engineering Technology and Sciences. https://doi.org/10.30574/wjaets.2024.11.1.0072

International Confederation of Societies of Authors and Composers (CISAC). (2024, December 2). Global economic study shows human creators’ future at risk from generative AI. https://www.cisac.org/Newsroom/news-releases/global-economic-study-shows-human-creators-future-risk-generative-ai

Ovsiienko, V. (n.d.). Unleash the power of AI-generated voices in music production. Voice Cloning Software for Content Creators. https://www.respeecher.com/blog/ai-generated-voices-music-production#:~:text=AI%20voices%20can%20significantly%20reduce,speeding%20up%%2020the%20production%20cycle

Pujari, V., & Wilson, B. (2023, December). Copyright and authorship in AI-generated music. Journal of Emerging Technologies and Innovative Research. https://www.jetir.org/papers/JETIR2312540.pdf

Semancik. (2025, February 4). How AI is transforming the creative economy and music industry. OHIO Today. https://www.ohio.edu/news/2024/04/how-ai-transforming-creative-economymusic-industry  

Sturm, B. L. T., Iglesias, M., Ben-Tal, O., Miron, M., & Gómez, E. (2019, September 6). Artificial Intelligence and music: Open questions of copyright law and engineering praxis. MDPI. https://doi.org/10.3390/arts8030115