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People have long worried about robots automating the jobs of truck drivers and restaurant servers. After all, from the invention of the cotton gin to the washing machine, we’re used to an economy where technology transforms low-wage, physically arduous work.

But the past few years have shown that highly educated white-collar workers should be the ones bracing for artificial intelligence to fundamentally transform their—I should probably say our—professions. The angst this has spurred from all corners of white-collar America has been intense, and not without merit. AI has the potential to take over much of our creative life, and the risks to humanity are well documented.

The discourse around AI has focused so squarely on the terrifying risks and potential job losses that I’ve noticed there’s been very little discussion around why so many people are working so hard to create this doom monster in the first place.

On today’s episode of Good on Paper, I’m joined by someone researching what happens when AI enters a workplace. Aidan Toner-Rodgers is a Ph.D. student of economics at MIT and has a working paper out on what happened to scientific discovery (and the jobs of scientists) when an R&D lab at a U.S. firm introduced artificial intelligence to aid in the discovery of new materials.

Materials science is an area of research where we can see the direct applications of scientific innovation. Materials scientists were the ones who developed graphene, thus transforming “numerous products ranging from batteries to desalination filters” and photovoltaic structures that “have enhanced solar panel efficiency, driving down the steep decline in renewable energy costs,” Toner-Rodgers writes. There are also countless more applications in fields such as medicine and industrial manufacturing.

New discoveries in this field have the potential to transform human life, making us happier, healthier, and richer. And when scientists at this company were required to integrate an AI assistant in generating new ideas, they became more productive, discovering 44 percent more materials.

“I think a big takeaway from economic-growth models is that in the long run, really, productivity is the key driver of improvements in living standards and in health,” Toner-Rodgers argued when we spoke. “So I think all the big improvements in living standards we’ve seen over the last 250 years or so really are driven fundamentally by improvements in productivity. And those come, really, from advances in science and innovation driving new technologies.”

The following is a transcript of the episode:

[Music]

Jerusalem Demsas: What is the point of artificial intelligence? Why, when there is so much concern about the potential consequences, are we hurtling towards a technology that could be a mass job killer? Why, when we face so many competing energy and land-use needs, are we devoting ever more resources to data centers for AI?

There are good reasons to worry about its negative consequences, and the media has a bias toward negativity. As a result, we don’t tend to explore these questions.

My name’s Jerusalem Demsas. I’m a staff writer at The Atlantic, and this is Good on Paper, a policy show that questions what we really know about popular narratives.

Today’s episode is about one of the best applications of AI: helping push the boundaries of science forward to make life better for billions of people. This isn’t a Pollyannaish conversation that skates past concerns with AI, but I do want to spend some time investigating the ways that this technology could improve our lives before we get into the business of complicating it.

In some ways, this conversation isn’t just about AI. It’s about technological progress and the trade-offs that come with it. Are the productivity benefits of AI worth all the downstream consequences? How can we know?

My guest today is Aidan Toner-Rodgers. He’s a Ph.D. student in economics at MIT with a fascinating new working paper that shows what happens when scientists are required to begin using AI in their work.

Aidan, welcome to the show!

Aidan Toner-Rodgers: Thanks so much for having me.

Demsas: You have a really great paper that I’m interested in talking to you about, but first I want us to sort of set the stage here a bit about productivity. So productivity is something that economists talk about a lot, and I think it can be ephemeral to people about why it’s so important.

So why do economists care about productivity?

Toner-Rodgers: Yeah, so I think a big takeaway from economic-growth models is that in the long run, really, productivity is the key driver of improvements in living standards and in health. So I think all the big improvements in living standards we’ve seen over the last, like, 250 years or so really are driven fundamentally by improvements in productivity.

And those come, really, from advances in science and innovation driving new technologies. So when economists think about what are the most important drivers of living standards, it really is kind of coming back to productivity.

Demsas: Yeah, and I think that sometimes it’s useful to think about ways in which society gets better, right?

Like, most increases in inputs—so if you increase labor, it means you have less leisure time. And if you increase investments in capital, that means you’re lowering your current consumption. So you’re moving away from buying things that you may want in order to invest in the future, and if you’re increasing material inputs, that reduces natural resources.

So the idea is: How can we get more efficient? And one stat that I like to point to is that “productivity increases have enabled the U.S. business sector to produce nine times more goods and services since 1947 with a [pretty] small increase in hours worked.” So we’re just getting a lot more stuff without having to kill ourselves working to get it. And that can be, you know, just clothes and things like that, but that can also be services. Like now, because it’s really easy to produce a T-shirt, you need less people making T-shirts, and they can teach yoga or do other things. And so I think that’s really important to set the stage here.

But I want to ask you, because your paper is about AI, about this bet that I wonder which side you take on. There’s this bet—I don’t know if you’ve heard about it. It’s between Robert Gordon and Erik Brynjolfsson. Have you heard about this bet?

Toner-Rodgers: I don’t think so, actually.

Demsas: Okay, yeah. It’s basically a $400 bet to GiveWell, so I don’t know if it really has the impact of me making people put their money where their mouth is.

But Robert Gordon is an economist. He’s kind of a longtime skeptic of digital technology’s ability to match the impact of things like electricity or the internal combustion engine. And his argument, basically, is just that he doesn’t expect AI to have a significant impact on productivity. And he argues that because, you know—he points at things like how the U.S. stock of robots has doubled in the past decade, but you haven’t seen this massive revolution in production, productivity growth, and manufacturing. And he also says that AI is really nothing new. You know, we’ve had human customer-service representatives replaced by digital systems without much to show for it. And then he also says things like a lot of economic activity that is relevant to people’s lives, like home construction, isn’t really going to be impacted by AI.

So it’s one side of the debate. It’s kind of more pessimistic on AI. And the other is kind of represented by Erik Brynjolfsson—he’s more of a techno-optimist—and he argues that recent breakthroughs in machine learning will boost productivity in places like biotech, medicine, energy, finance, but it’ll take a few years to show up in the official statistics, because organizations need time to adjust.

Again, they’re only betting $400, so I don’t know if they’re putting their money where their mouth is, but whose side do you kind of take in this debate?

Toner-Rodgers I mean, I think I’m probably more on Erik’s side. So Robert Gordon’s research, I think, has done a great job showing that over the past 40 years or so there’s been this big stagnation, kind of, in innovation in the physical world.

But I think something I’m really excited about in AI is that all these advances in digital technologies, computing power, and algorithms maybe can now, finally, have this impact kind of back to physical infrastructure and physical things in the world. So I think, actually, materials science is a great example of this, where we have these kinds of new AI algorithms that can maybe come up with new important materials that can then be used in physical things.

Because I think a lot of the advances in information technology so far haven’t had big productivity improvements, because they were kind of confined just to the digital world, but now maybe we can use these breakthroughs to actually create new things in the world. And I do think the point—that there’s a lot of constraints to building things, and a lot of the barriers to productivity growth are not, like, we don’t know how to do things, but there’s just big either regulatory or other barriers to building things in the world—is very important.

And I think that’s why the people who are super optimistic about AI’s impact—I think I’m a bit more pessimistic than them because of these kind of bottlenecks in the world. But I’m very excited about things—like biomedicine, drug discovery, or materials science—where we can maybe create new actual things with AI.

Demsas: So materials science, I think, is the place where your research really is focused. So can you just set the stage for us? What type of company were you looking at, and what kind of work are the employees doing?

Toner-Rodgers: Yeah, so the setting of my paper is the R & D lab of a large U.S. firm which focuses on materials discovery. So this involves coming up with new materials that are then incorporated into products. And so this lab focuses on applications in areas like healthcare, optics, or industrial manufacturing.

And so the scientists in this lab, many hold Ph.D.s or other advanced degrees in areas like chemical engineering or materials science or physics. And what they’re doing is trying to come up with materials that have useful properties and then incorporate these into products that are then going to be sold to consumers or other firms.

Demsas: And help us set—what do you mean by materials? Like, what are we trying to find here?

Toner-Rodgers: So in some sense, everything in every product uses materials in important ways. Like, one estimate I have in the paper: Someone was kind of looking at all-new technologies and products—How important were new materials to these?—and he found that two-thirds of new technologies really relied on some advance in discovering or manufacturing at scale some new material. So this could be anything from the glass in your iPhone, to the metals in semiconductors, to different kinds of methods for drug delivery. So this is like a lot of the technologies in the world really are relying on new materials.

Demsas: Yeah. I mean, you note in your paper that materials science is kind of the unsung hero of technological progress. And when you start to think about it, it really just adds up. Like, basically every single thing that you could care about, it ends up boiling down to specific materials that you want to find—so whether it’s computing or it’s biomedical innovation, like you said, but also just stuff that we’ve been surprised by recently, like the lowering costs of solar panels. Like, new photovoltaic structures being found is helping drive down the cost of those renewables.

So all these different things—and I think it’s funny, because, I mean, we are an increasingly service-sector-based economy. So I think that we’re kind of abstracted away from some of the materials’ impact on our lives, because we just don’t really see it in our day-to-day. But it’s just as important. I think the pandemic really showed this one when we were missing semiconductor chips.

Toner-Rodgers: Yeah, maybe an economics way to put this is that materials science is very central in the innovation network. So there’s been some papers looking at which other fields rely on research from materials science. And it’s really one that’s very central in this network, where things like biomedicine to manufacturing are really relying on new discoveries in materials science. And so kind of focusing on this is a key driver of growth in a lot of areas.

Demsas: And so the scientists in this firm—can you just walk us through what they’re actually doing? Like, what is the process of their work? And then we can get into how AI changed it.

Toner-Rodgers: Sure. So a lot of what they’re doing is basically coming up with ideas, designs for new materials. And then because materials discovery is very hard, many, many of these materials don’t end up having the properties that they hope they do or don’t yield a viable, stable compound. So a lot of what they’re doing is doing tests either in silico tests—like doing simulations—or actually kind of making these materials and testing their properties to see which ones are actually going to be helpful and can later be incorporated into products.

So their time is split. Maybe, like, 40 percent or so is on this initial idea-generation phase, and then the rest is testing these things and seeing which materials are actually viable.

Demsas: When I was reading your paper, I analogized it to coming up with recipes in a kitchen. And you can have a test kitchen or something like that, where basically, if your goal is to come up with a bunch of new recipes for food or for baking or whatever, you may come up with some on paper, and then you’re like, Okay, well, I have to pick which one is potentially going to be a really good recipe, and then you would, you know, test it. And probably you don’t do a simulation. You probably just go make the donut or whatever it is. Is that kind of a good analogy for this?

Toner-Rodgers: Yeah, I think it is, and also just in the sense that we know a lot about the ingredients or sets of elements and their bonds, and we know a lot about that at a small scale, but it becomes very hard to predict what a material’s property will be as these materials become bigger and more complicated. And so even though we know a lot in some small sense, actually prediction gets pretty hard.

Demsas: So AI gets introduced at this company because they want to figure out if that can help their scientists be more productive at coming up with new materials. At what point in the process is AI coming in? What is it actually doing? How does it change the scientists’ jobs?

Toner-Rodgers: Yeah, so AI’s role is really in this initial idea-generation phase. And so how it works is that scientists are going to input to the tool some set of desired properties that they want a material to possess. So in this setting, this is really driven by commercial application because this is a corporate R & D lab. So they want to come up with something that’s going to be used in a product. And then they’re going to input these desired properties to the AI tool, which is then going to generate a large set of suggested compounds that are predicted by the AI to possess these properties.

And so before, scientists would have been coming up with these material designs themselves. And now this part is automated by the tool.

Jerusalem Demsas: So it’s like, Now I’m having an AI tool give me a bunch of potential donut recipes instead of me coming up with them myself.

Toner-Rodgers: Exactly. And I think it’s important to note that this whole prediction process is very hard. And so even though I’m going to find pretty large improvements from the AI tool on average, many, many of its suggestions are just not that good and either aren’t going to yield a stable compound or aren’t going to actually have the other properties that you wanted to begin with.

Demsas: Yeah. And so before we get into your results, which are really shocking to me actually, it’s kind of cool—the company set up a natural experiment, basically, for you. Can you walk us through what they did and how they randomized researchers?

Toner-Rodgers: Yeah. So I think the lab had just a lot of uncertainty going in about whether this tool was going to be actually helpful. Like, you could have thought, Maybe it’s going to generate a lot of stuff, and it’s all bad, or it’s going to kind of slow people down as they have to sort through all these AI suggestions.

So I think they just had a lot of questions about: Is this tool going to work, and are we going to get actually helpful compounds? So what they did, instead of just rolling it out all at once, was to do three waves of adoption where they randomly assigned teams of scientists to waves. And so this allows me, as a researcher, to look at treated and not-yet-treated scientists and identify the effects of the tool.

Demsas: And did they control for different things? Like, did they control for, you know, what types of research they were working on or how many years of experience they had?

Toner-Rodgers: Yeah, so there’s a lot of balance between waves because of the randomization on what exactly these scientists are working on, which types of technologies and materials, as well as just the team composition in terms of their areas of expertise and tenure in the lab and so on.

Demsas: So now I want to turn to the results. What did you find?

Toner-Rodgers: So my first result is just looking, on average, at how this tool impacted both the discovery of new materials as well as downstream innovation in terms of patent filings and product prototypes. So I find that researchers with access to the AI tool discover 44 percent more materials, and then this results in a 39 percent increase in patent filings and then a 17 percent rise in downstream product innovation, which I measure using the creation of new product prototypes that incorporate those materials.

Demsas: These are, like, massive numbers.

Toner-Rodgers: Yeah, I think they’re pretty big. And also, I think it’s helpful to kind of step back and look at the underlying rate of productivity growth in terms of the output of these researchers. So I look back at the last five years before the tool was introduced, and output per researcher had actually declined over this period. So these are huge numbers relative to the baseline rate of improvement.

Demsas: So it’s interesting—well, I guess first: How? Like, why are people becoming more productive here?

Toner-Rodgers: I think there’s two things. So one is just that the tool is pretty good at coming up with new compounds. So being able to train a model on a huge set of existing compounds is able to give a lot of good suggestions.

And then second: Not having to do that compound design part of the process themselves frees scientists to spend more time on those second two categories, kind of deciding which materials to test and then actually going and testing their properties.

Demsas: It’s interesting when I was looking at your results because you’re able to kind of look at, you know, one month after, four months after the adoption of this new AI tool, how it changes things. Things look kind of grim in the short run, right? Like, four months after AI adoption, the number of new materials actually drops. And it’s not until eight months after that you see a significant increase in new materials. And that’s around when you see the patent filings increase. And it’s not until 20 months after that you actually see it show up in product prototypes.

And, you know, part of the problem of trying to figure out if new technology like AI is having a big impact is that it might take a while to show up in statistics. Is that why you think maybe we’re not seeing a massive jump in productivity right now in the U.S., despite the rollout of a ton of new machine-learning tools?

Toner-Rodgers: Yeah, I think that’s partly true. Like, you definitely need some forms of organizational adaptation or people learning to actually utilize these tools well. So part of why there’s this lag in the results is just that materials discovery takes a while. So it takes a little bit to actually go and kind of synthesize these compounds and then go and find their properties.

But another thing I find is that in the first couple months after the tool’s introduction, scientists are very bad, across the board, at determining which of the AI suggestions are good and which are bad. And this is part of the reason we don’t see effects right away.

Demsas: So it’s like your job has changed significantly, and you just need time to adjust to that.

Toner-Rodgers: Yeah, totally.

Demsas: So I want to ask you about material quality, though, because what you’re measuring, largely, is the number of materials made. But has the quality of the materials improved or declined, and how would we know?

Toner-Rodgers: So I think that’s a key concern when you’re doing these things, is we don’t only care about how many new discoveries we’re getting, but what they are. So a very nice thing about my setting and materials science, in general, is that there’s direct measures of quality in terms of the properties of these compounds. And in particular, at the beginning of the discovery phase, scientists define a set of target properties that they want materials to possess.

And so I can compare those target properties to the measured properties of materials that are actually created. And so when I do this, I find that, in fact, quality increases in the treatment group, which is showing that we’re not actually having this compromised quality as a result of faster discovery.

Demsas: So there’s this joke that I was looking up, and apparently Wikipedia tells me it’s attributed to this character from Muslim folklore called Nasreddin, but I could not independently verify this. Most people have probably heard some version of this. It goes: A policeman sees a drunk man searching for his keys under a streetlight, and he tries to help him find it. They look for it for a bit of time, and then he’s like, Are you sure you dropped them here? And the drunk guy is like, No, I lost them in a park somewhere else. The policeman is kind of incredulous; he’s like, Why are you looking for them here? And the drunk guy goes, This is where the light is.

And this has been, you know, referred to by a lot of researchers as the streetlight effect, right? So it’s a phenomenon that people tend to work where the light is or like easiest problems, even if those aren’t the ones that are actually likely to bear the most fruit. Do you think that AI helps us avoid the streetlight effect or it exacerbates the problem?

Toner-Rodgers: So I think talking to people before this project, I would have guessed that it would exacerbate the problem. And the reason is that the tool is trained on a huge set of existing compounds. So you might expect that the things it suggests are going to be just very similar to what we already know. So you might think that because of that, the streetlight effect is going to get worse. We’re not going to come up with the best things but rather just things that look very similar to what we already know.

And I think, surprisingly to me, I find that, in my setting, this is not the case. And so to do that, I measure novelty at each stage of R & D. So first I look at the novelty of the new materials themselves. And to do that, I look at their chemical structures—so the sets of atoms in a material, as well as how they’re arranged geometrically. And I can compare this to existing compounds and see, like, Are we creating things that look very similar to existing materials, or are they very novel?

So on this measure, AI decreases average material similarity by 0.4 standard deviation. So these things are becoming more novel. And it also increases the share of materials that are highly distinct—which I define as being in the bottom quartile of the similarity distribution—by four percentage points. So it seems like, both on average and in terms of coming up with highly distinct things, we’re getting more.

Demsas: This is kind of surprising to me, right? There’s a paper by some researchers at NYU and Tel Aviv University called “The Impact of Large Language Models on Open-Source Innovation,” and they sort of raised this question about whether AI has asymmetric impact on outside-the-box thinking and inside-the-box thinking. And you know, the thing is that most AI systems are evaluated on tasks with well-defined solutions, rather than open-ended exploration. And, you know, models are predicting the most likely next response. Like, what’s happening with ChatGPT is it’s just predicting what the next word is going to be. Or that’s what most of these systems are trying to do. And they’re trained on this corpus of existing stuff, and it’s not like they’re independent minds.

And so they kind of theorize that, you know, AI might be good at finding answers to questions that have right answers or ones where there’s clearly defined evaluation metrics. But can it really push the bounds of human understanding, and does our reliance on it really reduce innovation in the long term? So I mean, this seems to be a really big problem in the field of AI, and I wonder: How confident are you that your findings are really pushing against this? Or is it kind of like, maybe in the short term, there’s some low-hanging fruit that looks really novel, and in the long term, you’re not really going to have that?

Toner-Rodgers: Yeah, so I think one drawback of the measurements I have is that I can see that, on average, novelty increases, but what I can’t see is whether the likelihood of coming up with really truly revolutionary discoveries has changed. And so if you think of science as being driven, really, by these far-right-tail breakthroughs, you’re just not going to see much of these in your data. This has been an issue highlighted by Michael Nielsen in some essays that I like a lot.

And so one kind of thing you might be worried about is, Well, we got, on average, more novel things, but maybe these very revolutionary discoveries have a lower probability of being discovered by the AI, and that in the long term this is not a good trade-off. And because you’re just never going to see very many of these right-tail discoveries in your data, you just can’t say much about this using these types of methods.

Demsas: I mean, how confident, then, are you that we can even test whether this is happening?

Toner-Rodgers: Yeah, I think one answer is that we’ll just need some time to see, like, do these new materials open up new avenues for research? Like, are there other materials that are going to be built on these new ideas that the AI generated? But one thing I’d say is just that I think a lot of people would have said beforehand that, even on average, I expect novelty to go down. And the fact that it went up, I think, does push back somewhat against the view that these things are going to be bad for novelty.

Demsas: And then I guess, kind of on this question of generalizability to other fields, like, materials science is a place, of course, where you can measure productivity pretty cleanly. Like, you can see what the compounds are. You can see what people are trying to look for. A lot of fields, even in science, are not like this. They’re not super easy to measure what exactly you’re trying to find, and innovations can have spurts and stops for long periods of time, even if a lot of work is happening. So I guess, do you expect AI to be as helpful in fields that look a lot less like materials science?

Toner-Rodgers: So I think in the short run, I would say probably not, right? I think there’s areas where it does look a lot like this, like things like drug discovery, but then there’s a lot of areas where it doesn’t look like this at all. I would say, I think kind of fundamentally, this comes down to how much of science is about prediction versus maybe coming up with new theories or something like that. And I think maybe I’ve been surprised over the last several years how many parts of science, at least in part, can have big impacts from AI, right?

So we see in things like math, where maybe it really feels like it’s not a prediction problem at all, like doing a proof, but we see things like large language models and other more specialized tools really being able to make progress in these areas. And I think they’re not at the frontier of research by any means, but I think we’ve seen huge improvements.

So this is absolutely an open question how much these tools can generalize to other fields and come up with new discoveries more broadly. But I would say that betting against deep learning has not had a great track record in recent years.

Demsas: Yeah, fair.

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After the break: AI doesn’t benefit everyone equally, even when we’re talking about brilliant scientists.

[Break]

Demsas: I want to ask you about the distributional impacts. I think this is probably the most pessimistic, concerning part of your paper. You find that the bottom third of researchers see minimal gains to productivity, while the top 10 percent have their productivity increase by 81 percent. Can you talk through how you’re measuring the sort of productivity of these researchers and this finding, in particular?

Toner-Rodgers: Yeah. So first I kind of just look at scientists’ discoveries in the two years before the tool was introduced. And there’s a fair amount of heterogeneity across scientists and their rate of discovery. And I do some tests showing that these are kind of correlated over time, so it’s not like some scientists are just particularly lucky. And, instead, there do seem to be these kinds of persistent productivity differences across scientists. And then I just look at each decile of initial productivity: How much do those scientists’ output change once the tool is introduced? And we see these just massive gains at the high end. And at the low end, on average, they do see some improvement, maybe 10 percent or so, but nowhere near as much as the kind of initially high-productivity scientists.

Demsas: Why? Like, at what stage are the low-productivity scientists getting caught up? Because, you know, if this tool is just giving them a bunch of potential recipes for new materials, are they just worse at selecting which ones to test, or what’s happening?

Toner-Rodgers: Yeah, so I think the key mechanism that I identify in the paper is that it’s really this ability to discern between the AI suggestions that are going to be actually yielding a compound that’s helpful versus not. So I think just the vast majority of AI suggestions are bad. They’re not going to yield a stable compound, or it’s not going to have desirable properties. And so because actually synthesizing and testing these things is very costly, being able to determine the good from the bad is very important in this setting. And I find that it’s exactly these initially high-performing scientists that are good at doing this. And so the lower-performing scientists spend a lot of time testing false positives, while these high-ability ones are able to kind of pick out the good suggestions and see their productivity improve a lot.

Demsas: But lower-performing scientists aren’t getting worse at their jobs, right? They’re just not really helped by the tool.

Toner-Rodgers: Yeah, that’s true. But I think it’s worth saying that it’s not like they’re not using the tool. So it really is that their research process changed a lot, but because their discernment is not great, it ended up being kind of a similar productivity level to before.

Demsas: And were you able to observe this inequality over time? Was it stagnant? Did it widen? Did it decrease? Was there learning that you were able to see happen with less-productive researchers?

Toner-Rodgers: Yeah. So I think something very interesting is, like, if I look in the first five months after the tool was introduced, across the productivity distribution, scientists are pretty bad at this discernment. So all of them are kind of doing something that looks like testing at random. They’re not really able to pick out the best AI suggestions. But as we look further on, scientists in the top quartile of initial productivity do seem to start being able to prioritize the best ones, while scientists in the bottom quartile show basically no improvement at all. And so I think this is pretty striking. And there’s just something about these scientists that’s allowing some to learn and some to see no improvement.

Demsas: And how long were you able to observe this for? Like, is it possible that maybe they just needed more time?

Toner-Rodgers: Yeah, so I think I see, like, two years of post-treatment observations. So in that time, I don’t see improvement. I think it’s possible either they need more time, or maybe they need some sort of training to be able to learn to do this better. So I think one question: Is this something fundamental about these scientists that’s not allowing them to do this? Or is there some form of either training or different kind of hiring characteristics the firm could look at to identify scientists that are good at this task?

Demsas: So were you surprised by this finding? After reading your paper, our CEO here at The Atlantic, Nicholas Thompson—he pointed out that in studies of call centers, the opposite is often true. For instance, the guy we mentioned earlier, Erik Brynjolfsson, who’s kind of a techno-optimist, and two of his co-authors recently put out a working paper that looks at over 5,000 customer-service agents and found that AI increased worker productivity. And they’re measuring that as issues resolved per hour. And it increases their productivity by 14 percent, with less-experienced and lower-skilled workers improving the speed and quality of their output, while the most experienced and the highest skilled saw only small gains. So I guess, looking at the field, in general, is it strange that you’re seeing the biggest impact happening with the most-skilled people? Should we expect the opposite?

Toner-Rodgers: Yeah, so I think a lot of the early results on AI have found that result that you just mentioned, where the productivity kind of compresses, and it’s these lower-performing people that benefit the most. And I think in that call-center paper, for example, I think one thing that’s going on is just that the top performers are already maybe nearly as good as you’re going to get at being a call-center person. Like, there’s kind of just a cap on how good you can do in this job.

Demsas: You can’t resolve an issue every second. You actually have to have a conversation.

Toner-Rodgers: Right. You kind of have to do it. And they’re maybe close to the productivity frontier in that setting. So that’s one thing.

And I think in materials science, this is just not the case at all. Like, this is just super hard, and these are very expert scientists struggling to come up with things, is one thing. And then I think the second thing is that in the call-center setting, AI is going to give you some suggestions of what to say to your customer. And it’s probably not that hard to kind of evaluate whether that suggestion is good or bad. Like, you kind of read the text and, like, All right, I’m gonna say this.

And in materials science, that’s also not the case—where, like, you’re getting some new compound. It’s very hard to tell if this thing is good or bad. Many, many of them are bad. And so this kind of judgment step, where you’re deciding whether to trust the suggestion or not, is very important. And I think in a lot of the settings where we’ve seen productivity compression, this step is just not there at all, and you can kind of out-of-the-box use the AI suggestion.

Demsas: So do you think a good heuristic is if AI is being applied to a job where there’s a right way to do things that we kind of basically know how to do, or there’s very little sort of experimentation or imagination or creativity necessary to do that job, that you will see the lower-skilled, the less-experienced people gain the most? And then when it’s the opposite, when a lot of creativity is needed, high-skilled people are going to get the most out of AI?

Toner-Rodgers: Yeah, I think that sounds true to me. And I think maybe one way I’d put it is it’s something about the variation and the quality of the AI’s output that’s very important. So even in materials science, I’m not sure that, say, in three years or something, the AI could just be incredibly good and, like, 90 percent of its suggestions are awesome, and you’re not going to see this effect where this judgment step is very important.

So I think it really depends on the quality of the AI output relative to your goal. And if there’s a lot of variation, and it’s hard to tell the good suggestions from the bad, that seems to be the type of setting where we’re seeing the top performers benefit the most.

Demsas: And I assume that with this tool at this company, like, when they come up with successful materials, they’re feeding that information back into the model. Did you observe that the tool was getting better at providing more high-quality suggestions over time?

Toner-Rodgers: Yeah, so they’re definitely doing that. There’s definitely some reinforcement learning with the actual tests. Like, I think over this period, I don’t see huge results like that. I think, relative to the amount of data it was trained on initially and the previous test results that went into the first version of the model, it’s just not that much data. But I think as these things are adopted at scale, we could absolutely see something like that.

Demsas: If that sort of reinforcement learning happens, do you think that that increases the likelihood that AI kind of pushes us down the same sorts of paths? Like, so you get kind of path dependent because you’re basically telling the model, Oh, good job. You did really good on these things, and then it becomes trained to sort of do those sorts of things over and over, and it gets less creative over time?

Toner-Rodgers: Yeah, I think that is definitely a concern. And I think something that people are thinking about is maybe there’s ways to reward novel output, per se. Because I think in these settings, one thing that’s helpful with novel output, even if it’s not actually a good compound, is that you learn about new areas of the design space. And even getting a result that’s very novel and not good is pretty helpful information. So I think rewarding the model for novelty, per se, is maybe one kind of avenue for fixing that problem.

Demsas: So this paper and this field, in general, kind of reminds me of some of the findings in the remote-work space. We had Natalia Emanuel from the New York Fed on the show, actually on our very first, inaugural episode. And you know, we talked about her research on remote work, and one finding that she has is that more-senior people are more productive or have higher gains of productivity when they’re able to go remote, because they stop having to mentor young people, and that is a drain on their productivity in person. They’re having someone younger than you kind of ask you questions, interrupt your day and, like—I’m not saying they hate the job—but that takes away from your ability to just work and not have to focus on other things.

And I wonder if AI becoming the sort of “bouncing off” buddy of scientists, rather than, like, you’re turning to your less-productive lab partner and just kind of tossing out ideas or talking. Instead, you’re sort of engaging with this AI tool, and that’s what you’re using to sort of figure out new methods and materials. Does that change science to become less collaborative with human peers, and does that have those knock-on harms, where maybe these most-productive scientists are getting better, but the less-productive scientists aren’t able to actually get the learning necessary to improve their own productivity?

Toner-Rodgers: Yeah, I think that’s super interesting. And I think a general question about these results are, like: What does this look like in the longer term?

I think something that might absolutely be true is: These people who are very good at judgment might have gotten good at judgment by designing the materials themselves in the past, and this is kind of where you got that expertise. But going forward, if the AI is just used, maybe new scientists that enter the firm never get that experience and maybe never have the ability to get the judgment. And so that’s one reason you could see different effects in the long run.

In terms of the specific question of collaboration, I think that’s something super interesting. I don’t have, really, evidence on that in the paper, because I don’t see good data on how much scientists are communicating with each other. But something I’m very interested in is: We have some scientists that are good at judgment. Like, could they teach whatever that skill is to the people who are worse? And I think one way to get at this, which I haven’t done yet, is: If you have a teammate who’s very good at this task, do you somehow learn, over time, from them? And I think that would be very interesting to look at.

Demsas: And you mentioned, like, how does someone become a high-productivity scientist, and that requires you doing this on your own, potentially. And I wonder—companies, whether they will have the incentive at all to invest in this long-term training when there are these sorts of short- and even medium-run, huge benefits they could get. I mean, you’re talking about massive increases in patents and new technologies they’re able to operationalize and commercialize, even. And if that’s the case, even if everyone knows that there’s this long-term cost to science and to scientists, who is actually incentivized to make sure this training happens until we’re already kind of in a bad place where a lot of technology has stagnated?

Toner-Rodgers: Yeah, I think that makes a lot of sense. Like, there’s kind of a collective-action problem where you don’t want to be the one that’s doing all the training in the short run while all your competitors are, like, coming out with all these amazing materials and products.

Demsas: And then poaching all your people.

Toner-Rodgers: Exactly. I think that’s definitely a concern. But also more generally, I do kind of have some confidence that organizations are going to be able to adapt to these tools and find out new ways to either train scientists for these things, kind of as they’re using them, or be able to, in the selection process for new employees, find predictors of being good at that this new task. Because, in some sense, what we’re saying is that these new technologies are changing the skills required to make scientific discoveries, and I think we’ve seen a long history of technological progress that’s done exactly that—like, changed the returns to different skills—and firms have adjusted to that.

Demsas: What I want to ask you about next is about the survey you did about the scientists’ job satisfaction. Can you tell us about that survey?

Toner-Rodgers: Yeah. So the goal of the survey was just to see both how scientists use the tool and then whether they liked it—how did this impact their job satisfaction?

And so after the whole experiment was completed, I just conducted a survey of all the lab scientists. About half answered. And one thing I found is that, basically across the board, scientists were fairly unhappy with the changes in the content of their work brought on by AI. So what they say is that they found a lot of enjoyment from this process of coming up with ideas for compounds themselves, and when this was automated, their job became a lot less enjoyable. So they say, like, My job became less creative, and some of the key skills that I’d built over time, I’m no longer getting to use.

And I think one thing that’s very striking is this is true both for the scientists that saw huge productivity improvements from AI, as well as the lower performers. And so we really see that it’s not as much dependent on productivity. I also ask, kind of, Well, you’re also getting more productive. Does this somehow somewhat offset your dissatisfaction with the tasks you’re doing at work? And it does somewhat. But overall, I find that 82 percent of scientists report a kind of net reduction in job satisfaction.

Demsas: I mean, that’s kind of depressing, right? Obviously, if you’re told, like, Oh, your work is having a big impact on the world and maybe making life better for people who are sick or who need renewable energy, or whatever it is, that can feel good. But if your day-to-day just sucks, you can imagine there’s gonna be some attrition, right?

Toner-Rodgers: Yeah, absolutely. Because yeah—one thing sometimes people say when they hear this result is, like, Well, scientific discovery is very important. Maybe these new materials are gonna be used by millions of people. Why do we really care about these scientists and how much they’re enjoying their job? But I really think it could have important implications for who chooses to go into these fields and the overall kind of direction of scientific progress. So I think it’s very important to think about these questions of well-being at the subjective, individual level for that reason.

Demsas: I feel like it’s really difficult for me to kind of weigh out what actually happens in the long term here, because I could imagine that the types of scientists who went into these fields were selected for people who really, really enjoyed the creativity aspect of figuring out new materials. Whether or not they’re productive at doing that, like, that’s just the kind of thing you’re selecting for.

And I would analogize it to someone who’s really excited about coming up with new recipes. And I’m someone who likes—I don’t like coming up with new recipes, but some of my favorite recipes are ones where I saw a New York Times Cooking recipe, and then I change some things about it. And as I’ve cooked it a bunch of times, I’ve tweaked some things, and I’ve come up with something that’s sort of my own, sort of already existing. And I can imagine there are a lot of people like that and that the skill of discernment does not necessarily correlate with the skill of loving to be creative.

So you could see shifts happening in the field, right, where the types of people who go into materials science change, and these scientists go do something else where they’re able to be more creative. And you mentioned that a lot of them are thinking about taking on new skills. How do you think that all kind of shakes out?

Toner-Rodgers: This really maybe comes back to the question of training. So I think a lot of these people’s complaints were like, Look—I built up all this expertise for one thing, and now I don’t get to do that thing anymore. And you could think that now if we start training people for this slightly different task, which also requires a lot of expertise, of judgment, that that also is fulfilling. And whether that’s true in the long run, I think I’m not sure.

So one analogy that someone said to me is, like, Well, you’re a Ph.D. student. Imagine if, instead of writing papers, you just did referee reports all the time.

Demsas: Yeah. And sorry—can you explain what a referee report is?

Toner-Rodgers: It’s like you’re looking at someone else’s research and saying, like, It’s good, or, It has these problems.

And that doesn’t sound awesome. Like, it definitely takes a lot of expertise to do a referee report, but it’s not why you got into this—like, you do want to come up with ideas. And so I think I’m very uncertain how this is going to all shake out. I do think that part of it really was, like, I got trained to do a thing, and now I don’t get to do it anymore. And I think that part will go away somewhat, but whether this is just fundamentally a worse job, I think it definitely could be.

Demsas: It’s interesting, the way in which we kind of have always thought of automation as disrupting the jobs of people with less-well-compensated skills—so, like, manufacturing jobs, or, you know, now your job is shifting a lot if you’re someone who works at a restaurant. Now robots are doing some of that work. And you know, there’s just been this kind of pejorative, like, Learn to code! sort of response to some of those people.

And it’s interesting to see that, like, a lot of generative AI is actually really impacting the fields of higher-income individuals, like people who are working in heavily writing fields or like legal fields and now, also, science fields. And it does, really, I think, raise this question of just: Will society be as tolerant of disruptions in those spaces as it has been in disruptions in spaces where workers have had less kind of political and social power?

Toner-Rodgers: Yeah, I totally agree. And I think there really is something different about these technologies where they’re creating novel output based on patterns in their training data, whereas before, like, from industrial robots to computers, it really was about automating routine tasks. And now for the first time, we’re automating the creative tasks. And I think how people feel about this and how we react might look very different.

Demsas: Yeah. I came across this quote from the chief AI officer at Western University, Mark Daley. It’s a blog post. He’s commenting on your paper. He writes, “Because AI isn’t just augmenting human creativity—it’s replacing it. The study found that artificial intelligence now handles 57 percent of ‘idea generation’ tasks, traditionally the most intellectually rewarding part of scientific work. Instead of dreaming up new possibilities, scientists may find themselves relegated to testing AI’s ideas in the lab, reduced to what one might grimly call highly educated lab technicians.”

I don’t know if there’s a survey of scientists or whatever, but I wonder here if you see that there’s a kind of a growing pessimism as a result of findings like this and just, like, the experiences many people are having with AI where they do feel like, Hey, the good part of life—I don’t want AI or robots or technology to be taking away the fun, creative stuff like writing or art or whatever. I want them to take away the drudgery the way that, like, laundry machines took away drudgery or dishwashers took away drudgery. I don’t know how you think about that as a shift in how the discourse is happening on this issue.

Toner-Rodgers: Yeah. I think that’s interesting. And I also think, when I talk to scientists, for example, materials scientists that work on actually building the computational tools, like, they’re super excited about this stuff because they’re coming up with ideas for the tool itself and, like, going and testing it and all these things.

Something in this setting is like: This was a tool that was kind of imposed on these people, not something they kind of created themself. And I think that’s maybe something we’ll see, where the people that are actually having input and creating the new technologies themselves might find, like, they’re very happy with the output, even though these tasks are being automated. Whereas people in this setting, where the tool kind of just came in and changed their job a lot, maybe see kind of big decreases in enjoyment.

Demsas: Well, Aidan, always our last and final question: What is an idea that you thought was good at the time but ended up only being good on paper?

Toner-Rodgers: So I went to undergrad in Minnesota. And for background, I’m from California. So the first winter I was there, me and a couple of friends decided it’d be a great idea to go ice fishing.

Demsas: Okay.

Toner-Rodgers: And so we drive up to this lake. And literally three steps out on the ice, I step on a crack and fall through into this frozen lake. So ice fishing for Californians is good on paper.

Demsas: This is like the scene in Little Women where, like, Amy falls into the lake or whatever. What happened? Was it actually dangerous, or did you just immediately pull yourself out?

Toner-Rodgers: Luckily, we weren’t far from civilization. Like, we were near the car, so we ran back to the car.

Demsas: Oh my God.

Toner-Rodgers: And that was the end of my ice-fishing career.

Demsas: I’m glad you learned this early in your Minnesota life and did not get too adventurous. Well, Aidan, thank you so much for coming on the show.

Toner-Rodgers: Yeah, it was great. Thanks so much.

[Music]

Demsas: Good on Paper is produced by Rosie Hughes. It was edited by Dave Shaw, fact-checked by Ena Alvarado, and engineered by Erica Huang. Our theme music is composed by Rob Smierciak. Claudine Ebeid is the executive producer of Atlantic audio. Andrea Valdez is our managing editor.

And hey, if you like what you’re hearing, please leave us a rating and review on Apple Podcasts.

I’m Jerusalem Demsas, and we’ll see you next week.

On a Wednesday morning in October 2014, in a garage in the woods of Pennsylvania, Tommy Trotta tried on some new jewelry: a set of rings belonging to the baseball great Yogi Berra. Each hunk of gold bore a half-carat diamond and the words NEW YORK YANKEES WORLD CHAMPIONS. The team had given them to Berra for each of his 10 World Series victories—no player had ever won more.

Trotta, a balding 39-year-old who lived with his wife and two kids in Scranton, had grown up a Yankees fan. He’d dreamed as a boy of one day joining the team. Berra had been the favorite player of his beloved godmother, who gave Trotta his first Yankees uniform when he was a toddler and took him to games at Yankee Stadium.

Trotta never competed past Little League. But there was more than one way into a hall of fame. In a methodically planned heist in the dark and rain of that October morning, he’d climbed onto a balcony at the Yogi Berra Museum & Learning Center, in Little Falls, New Jersey, carrying a duffel bag of tools and dressed entirely in black. He’d cut through a double-reinforced window built to withstand foul balls from an adjoining stadium. Then he’d used a 20-volt DeWalt grinder, with a fire-rescue blade, to slice open a bulletproof display case labeled BASEBALL’S RING LEADER.

Berra’s rings now glinted on Trotta’s hands. They evoked for him a magnificent time before his own birth: the mid-century years when Berra had won World Series after World Series with teammates such as Joe DiMaggio, Roger Maris, and Mickey Mantle. How many men besides Berra—and now Trotta—would ever know the feeling of those rings on their fingers? How many besides Trotta could sense the weight of all those victories, then destroy every last ounce of it for cash?

In the garage in the Pennsylvania woods, an electric melting furnace was reaching a programmed temperature of more than 1,900 degrees Fahrenheit. Trotta handed Berra’s rings to a friend, who used jewelers’ tools to pluck out the diamonds and cut up the rings. The dismembered rings were then dropped into the furnace, where they liquefied into a featureless mass of molten gold.

Mining has a proud history in the parts of northeastern Pennsylvania that Trotta and his crew called home. Scranton, the biggest city there, was named after a pair of brothers who exploited the region’s rich deposits of iron and coal. But where earlier generations had descended into the ground for raw minerals, Trotta broke through windows. His mother lode was the championship rings, belts, and trophies—veined with precious metals and gemstones—that sat, almost for the taking, inside low-security sports museums across America.

[From the June 2023 issue: Ariel Sabar on the billion-dollar Ponzi scheme that hooked Wall Street, Warren Buffett, and the U.S. Treasury]

Trotta so perfected this niche line of burglary that he evaded the FBI and more than a dozen police agencies for two decades. His longevity was all the more remarkable given the size and makeup of his crew: three friends he’d known since grade school; his cousin’s fiancé; Trotta’s eldest sister, Dawn; two of her ex-boyfriends; and a neighbor of one of the exes. By day, they had normal jobs: plumber, carpenter, building contractor, bar owner, mechanic, Uber driver, real-estate closing agent. By night, they allegedly served as Trotta’s getaway drivers, toolmakers, and assistants.

Trotta told me his story last year, while he was on pretrial release, awaiting sentencing. He has pleaded guilty to a single count of theft of major artwork, as part of a cooperation agreement with federal prosecutors who have indicted and are seeking to convict his entire crew. I interviewed Trotta in his lawyer’s office, at the apartment he shared with Dawn, and over a few meals and car rides. This article draws on those conversations and on details in the federal indictments, police records, and other public documents.

Trotta stands at about 5 foot 8 and has a round, expressive face—cleft chin, narrow eyes, jutting nose and lips. He speaks like an earnest 10-year-old telling of adventures so grand, he can barely sit still. He turns 50 this year. He hides his bald head beneath a newsboy cap; his once lean, CrossFit-hardened body has grown pear-shaped and wobbly. “Fat Elvis,” he told me.

Trotta claims now to know what he never grasped during a lifetime of thieving: the pain he caused people, particularly the families and fans of the sports heroes whose hard-won trophies he’d plundered. Yogi Berra was nearing his 90th birthday—his last—and grieving the recent death of his wife, Carmen, when Trotta broke into the museum that October, stealing 16 of Berra’s baseball rings and two of his MVP plaques.

“I’m hated in the sports world,” Trotta told me. “I’m hated at a very deep level.”

Trotta felt as though he was born to steal. “In the blood” was how he put it, and it wasn’t just a metaphor. His father—Thomas Trotta Sr., known as “Big Tom” to Trotta’s “Little Tommy”—served as a police officer in Passaic, New Jersey, for seven years before discovering that he liked the other side of the law better. In March 1976, nine months after Trotta was born, his father accepted $750 from an associate of the Genovese crime family to torch a Hackensack dance club. A jury convicted him of arson and conspiracy, and a state judge sentenced him to two to three years in prison, rejecting any “sentimental concern for the family of a crooked cop.”

Left: The Trotta siblings pose with their father’s guns. Right: Thomas Trotta Sr., or “Big Tom.” (Courtesy of Tommy Trotta)

Big Tom was a Vietnam War veteran, fond of camouflage, jean shorts, and exotic firearms. Four months after leaving prison, he held up a Rite Aid pharmacy and was quickly caught. Later he ran heroin and cocaine for a New Jersey drug ring. Little Tommy was 15 years old—and watching from the back seat—when FBI agents yanked his dad out of the driver’s seat of the family car to arrest him. (Trotta Sr. agreed to testify against his associates and was sentenced to 22 months in prison.)

Big Tom may not have been cut out to be a successful arsonist, stickup man, or drug trafficker, but he did better as a thief, supporting his family without once getting caught. To steal without violence was a sly art, and Little Tommy loved when his dad asked for help. Where other fathers took their sons fishing, Trotta’s dad took his to steal salmon from a hatchery. Where other dads took their kids to see historical sites, Big Tom took his son to loot them: Little Tommy, at age 11, would look out for rangers at the Gettysburg battlefield at night as his father dug up Civil War artifacts with a metal detector and spade.

They’d moved from northern New Jersey to rural Pennsylvania in 1986, supposedly to escape the corrupting influences of city life. But it was there, in tiny Madison Township—on a former hay farm, off a dirt road, 15 miles east of Scranton—that Trotta’s criminal education began in earnest. His sister Dawn, who was four years older, had started dating a boy named Nicholas Dombek, a floppy-haired blond who’d quit school, robbed a gas station, and moved in with the Trottas after having had enough of his own parents. Dombek became a kind of older brother to Trotta, and a second son to Big Tom. (Dombek did not respond to requests for comment.)

Big Tom mentored Dombek in home and commercial burglary; Dombek, in turn, mentored Trotta; and by high school, Trotta had helped turn a group of boys he’d known since he was about 11 into a surprisingly disciplined band of thieves.

The gang would listen to idle talk among schoolmates and neighbors to figure out when houses might be unoccupied or stores flush with cash. Then they’d strike, syncing their movements over walkie-talkies and fleeing on ATVs and snowmobiles (also stolen) down wooded trails that police cars couldn’t reach. Trotta never used weapons: His code was always to run if spotted. But in other ways he could be ruthless. He stole $6,000 from the home of a schoolmate’s great-grandmother, he told me, then gave the boy a cut for his advice on how to do it. And he stole a safe from a clothing shop managed by his own girlfriend. Police interrogated the girlfriend, who had no idea he was responsible, but Trotta didn’t mind. The suspicion that fell on innocent employees after his burglaries, he said, “was good for me.”

After graduating from North Pocono High School, in 1994, Trotta got a student loan and enrolled in a six-month vocational-training course in alarm-system technology. He learned that you could disable an alarm by ripping its control panel—or “brain”—off the wall. He found out that many motion detectors had a “pet alley,” an unmonitored area near the floor for small animals. And he discovered that most alarms had a built-in delay: 60 to 90 seconds between when a sensor was tripped and when security was called. The feature was designed to reduce false alarms by giving owners time to punch in a code if they triggered the system accidentally. Trotta took away a different lesson: If a burglar got in and out in under 90 seconds, he could vanish into the night before anyone knew he’d been there. This insight, more than any other, became the basis for the next phase of his career.

Clockwise from top left: Tommy Trotta, Dawn Trotta, Nicholas Dombek, Damien Boland, Al Atsus, and Joe Atsus (Photo-illustration by The Atlantic. Sources: 1989 and 1994 North Pocono High School yearbooks; Tommy Trotta.)

In August 1999, Keystone College, in La Plume, Pennsylvania, held a celebration for its most famous alumnus: the Baseball Hall of Famer Christy Mathewson, who pitched for the New York Giants from 1900 to 1916 and won an astonishing 373 games. Mathewson had helped invent the fadeaway pitch and was nicknamed the “Christian Gentleman” for his refusal to play on Sundays.

Trotta was invited to the festivities by a baseball-card dealer he knew. They watched a one-man show about Mathewson’s life, then walked to the gym for an exhibit of memorabilia that Mathewson’s widow had given the school. Keystone’s athletic director was such an exuberant promoter of Mathewson’s legacy that she slid open a case to give visitors close-ups of the most thrilling items: Mathewson’s 1902 contract with the Giants; his 1916 contract with the Cincinnati Reds; and a World’s Champions jersey he wore after winning the 1905 World Series, its right sleeve cut off at the elbow for friction-free pitching.

Trotta didn’t think much of any of it until the car ride home, when his friend casually said that the Mathewson memorabilia in that one case might be worth more than half a million dollars. It was the most interesting thing Trotta had heard all evening.

He called Joe Atsus—a member of the thieving crew he’d known since middle school—the moment he got home. As Atsus made his way to the house, Trotta dug out a ski mask, a crowbar, and a pair of walkie-talkies. When they got to the Keystone gym, after midnight, Trotta noticed a parked car near the glass side door he’d planned to break through, and a plugged-in vacuum cleaner just inside. If a janitor was there, they’d momentarily stepped away. Trotta was reaching for his crowbar to smash the door when it occurred to him to try the handle. The door was unlocked. Trotta ran to the display, jimmied the sliding glass free of its ratchet lock, and grabbed the jersey and contracts. He was in and out, he recalled, in about 25 seconds. “It was like it was meant to just be taken,” he told me.

Nicholas Dombek (left) and Tommy Trotta in the 1990s (Courtesy of Tommy Trotta)

Trotta continued to burglarize homes to cover his day-to-day living expenses; unlike his assistants, he had no other job. But homes were haystacks: Somewhere in all that clutter, you’d maybe find an antique, a gun, some jewelry, but nothing to make you truly rich. Exhibits like Mathewson’s, Trotta realized, were clutter-free—everything in them was precious. If you could snag half a million dollars in memorabilia in half a minute from a college gym, imagine the takings in an actual museum.

The most prestigious museum in northeastern Pennsylvania was right there in Scranton. Founded by a local physician in 1908, the Everhart Museum had a diverse collection that ranged from a Tyrannosaurus rex skull to paintings by important artists. In 2000, a year after the Mathewson heist, the Everhart made headlines when it proposed strengthening its finances by selling its one Matisse. The painting, a 1920 still life called Pink Shrimp, had been appraised at more than $4 million. Trotta and his crew set in motion a plan to steal it; they began by filming the museum’s layout with camcorders while pretending to be tourists.

They lost their chance when the museum abruptly sold the Matisse. But Trotta was undeterred. On a return visit, he started talking about the painting with a guard, who mentioned that another artwork was probably worth more: Springs Winter, a movie-poster-size drip painting attributed to Jackson Pollock.

Over the next five years, Trotta, Dombek, and other members of the crew took turns visiting—and at times filming—the museum. They mapped the location of each security camera and motion detector, each entry and egress. In bed at night, Trotta replayed the footage obsessively, until he felt he could walk the museum blindfolded. The heist’s exact timing would depend, in a sense, on the gods: The crew needed a storm to hit Scranton between 2 a.m. and dawn. From burglarizing houses as teens, they knew that bad weather slowed the police and muffled the sound of breaking glass. Rain or snow was particularly effective an hour or two after bars closed, when police—tired after the usual arrests—tended to lose steam and become, in Trotta’s phrase, less “peppy.”

But all of those plans were set aside one early morning in November 2005, when a giant brawl erupted at a Scranton bar called Whistles. Trotta, Joe Atsus, and another schoolmate, Damien Boland—whose great-grandmother’s house Trotta had burglarized years earlier—were having a drink when the melee (which they’d played no part in) drew seemingly every last police cruiser to Whistles’ front door.

[From the April 2018 issue: An OurTime.com con man and the women who busted him]

Let’s do it now, Trotta told his friends. He had no ax, no crowbar, no ski mask. But a huge bar fight near closing time was a diversion as providential as a 3 a.m. downpour. He had more luck still when his friends dropped him off behind the museum: A large tent—erected for the Everhart’s annual ball the next night—blocked sight lines to the back door.

After failing to kick in the door, Trotta grabbed a ladder beside the tent and used it as a battering ram, bashing a hole in the glass and crawling through it. In the pitch dark, he bounded up the stairs to the second-floor gallery. He removed the Pollock from the wall and, on the spur of the moment, took an Andy Warhol silk screen, La Grande Passion, right near it. He was downstairs, out the hole, and in his pickup in less than a minute. “We’re rich!” Atsus said, according to court documents.

But by sunup, Trotta was so convinced of his imminent arrest that he pulled a lawn chair into his driveway and just sat there, waiting for the police. Lacking a mask, he’d improvised inside the museum by lifting his sweater over his nose, like some Looney Tunes bandit. Worse, it was a colorful sweater, which anyone at the bars he’d visited the previous evening might recognize.

Yet by the end of the day, no police had showed. The Scranton Times-Tribune soon reported that the museum’s surveillance cameras were under repair and not working that night.

Trotta’s relief was replaced by a new anxiety, captured in a front-page Times-Tribune headline the next day: “FBI: ‘No Market for Stolen Art.’ ” “The true art in an art theft is not stealing the material,” Robert Wittman, then the FBI’s lead art-crime investigator, told the newspaper. “It’s selling it.”

No one had linked the Everhart and Mathewson heists, but word of both had spread among museums, dealers, and collectors. Anyone who tried to sell the Pollock and the Warhol—together potentially worth millions—or the Mathewson memorabilia would almost certainly be discovered. Buyers, for their part, could face both civil and criminal liability, having no credible excuse for ignorance.

At first Trotta thought he could sell the items, no problem, once the five-year statute of limitations for theft expired. Later he realized his error: Under federal laws governing museum crime, prosecutors had as many as 20 years to bring charges. In desperation, he sent a videotape of the art and memorabilia to his father, to see if any of Big Tom’s underworld connections might bite. “I can’t move this,” his dad eventually reported back.

A few months after the Everhart job, one of Trotta’s crew saw an article in Electric City, an alternative Scranton weekly. Arthur Byron Phillips, an eccentric artist who had loaned the Pollock to the museum, was offering a biblical-sounding reward. “Return the purported Pollock to him,” the paper said, “and he’ll grace your palms with silver.” Phillips told reward seekers to be prepared to verify their bona fides by naming the gallery on the painting’s reverse side: “Anyone coming up with that name will prove that they have the actual picture.”

Hot art might not be sellable, Trotta realized, but apparently it could be ransomed. He found a gas station with a pay phone, checked for security cameras, and dialed.

“The Parsons Gallery,” Trotta said.

Phillips replied after a long silence. “You have my attention.”

“I want a million dollars in cash. Don’t call the police.”

“I don’t have a million dollars in cash.”

“Then you’ll never see the painting again,” Trotta said, hanging up.

When Trotta passed the gas station the next day, the pay phone was gone. He suspected that Phillips had called the FBI. The phone was likely on its way to a crime lab. Trotta was glad he’d wiped his fingerprints off his quarters. But he’d screwed up again: He’d asked for too much. “If we went, ‘$50,000,’ I tell you what—he pays, he gets his painting back, okay?”

Dombek eventually decided that art was dangerous. According to prosecutors, he burned a painting by the Hudson River School artist Jasper Cropsey—valued at $500,000 and stolen by Trotta from a New Jersey museum—rather than risk getting caught with it. Dombek was like that. Whereas Trotta shone at getting loot, Dombek always had ideas about what to do with it.

The garage Nicholas Dombek had built, on the six acres he lived on in Thornhurst Township, was essentially an improvised chemistry lab. Its long shelves were lined with beakers, droppers, funnels, jugs, calipers, and cookers, alongside containers of various acids, powders, and solutions. Chemical formulas were handwritten on the walls beside what appeared to be personal affirmations. DREAMS ARE EXTREMLY [sic] IMPORTANT YOU CAN’T DO IT UNLESS YOU IMAGINE IT, read one. Another read ALWAYS KEEP SECRETS. It wasn’t necessarily the science that Dombek’s father and older brother taught in the public schools (his dad had a master’s degree in chemistry from Bucknell University), but it reflected at least some of what he’d picked up before quitting school after eighth grade and moving in with the Trottas.

Though Dombek would later testify in court that he was trying to change the bond of water “to cure cancer,” his successes tended to the more pedestrian: S-hooks for attaching stolen license plates to getaway cars; a spiked metal ball for pulverizing reinforced glass; a chain that trucks could use to rip ATMs off their base. Dawn Trotta, who dated Dombek as a teenager and remained friends with him, recalled his particular facility for annihilating cars for her father, who helped people dispose of them for insurance money. “Nick could disappear a vehicle in hours,” she told me. Among the literature Dombek kept handy were The Anarchist Cookbook, A Field Guide to Rocks and Minerals, and Recovery and Refining of Precious Metals.

After the Mathewson and Everhart fiascoes, Trotta told me, Dombek had one of his ideas. The thing that made most museum pieces valuable—their status as unique, instantly recognizable objects—was also the thing that made them unsellable. But what if you scrapped a museum piece, almost like you did a car? Could certain one-of-a-kind objects be remade, in a lab, into tradable commodities?

In March 2011, Trotta stole 13 silver golf trophies from the Country Club of Scranton during an overnight storm, then delivered them to Dombek’s garage. Five had been awarded to the club’s most illustrious member: Art Wall Jr., who had won the 1959 Masters at Augusta National, beating the defending champion, Arnold Palmer.

The trophies buckled under the heat of Dombek’s torches and furnaces, then puddled and cooled into an untraceable blob of silver. (As Dombek refined his methods, the blobs would come to look less “criminal”—as Trotta put it—and more like professionally made ingots, in the shape of bars or pucks.) Lumps of metal might sell for a tiny fraction of what the original objects, with their feel-good history, would have fetched in a legal market, but there was no legal market. Trotta drove the silver blob to precious-metals dealers in Manhattan’s Diamond District, who bought it for about $6,000, no questions asked.

A business model was born, and Trotta—newly married, with a wife to support—dedicated himself to its perfection. He began mornings now on his laptop at the local Starbucks or Dunkin’, Googling for websites where the words gold, silver, or diamonds showed up alongside terms such as museum and display. When a promising target appeared in the search results, he’d immediately drive, for hours sometimes, to see it; some were mining museums, but far more were halls of fame or sports museums, many of them in small towns. (The crew cased the National Baseball Hall of Fame, in Cooperstown, New York, for years, but gave up after discovering that the diamonds on the championship belt they were after had been replaced by inexpensive replicas.)

Trotta’s reconnaissance grew bolder. On return visits to a target, he’d often bring his sister Dawn’s two preschool-age kids, which gave him cover to film its interior under the guise of recording his little loved ones. He’d ask his niece and nephew to walk over to certain windows, to see if their movements made lights blip on nearby motion detectors. (Dawn told me she appreciated the child care and was unaware of her brother’s ulterior motives.)

When it was Dombek’s turn to visit a target, he’d discreetly tap display cases with a penny, Trotta told me, to glean from the sound whether the glass was easily breakable or bulletproof—information that helped Trotta decide whether to bring his DeWalt grinder, Estwing camper’s ax, or center punch on heist night. With each successful job, Trotta became more convinced that his “dorky” face—together with the button-down shirts he wore on museum visits—made him look like the most generic of American tourists.

When the weather app on their phones showed storms nearing a target, Dombek, like a football coach, would chalk arrows and X’s on his garage floor, diagramming Trotta’s path through a museum. Then, in Dombek’s yard, Trotta would rehearse the moves at full speed, tracking his times on a stopwatch.

Dawn did her part by renting cars at the Scranton airport, then handing the keys to her brother. Not only did rentals rarely break down; they were so new and clean that police tended to overlook them, even in the immediate aftermath of a heist.

Trotta’s system left almost no detail unconsidered, from the way he activated burner phones and bleached his burglary tools to the music he psyched up with—AC/DC’s “Thunderstruck” or Metallica’s “Enter Sandman”—before crashing through a window. A trophy could be stolen from a museum at 3 a.m., melted in Dombek’s furnace by 8 a.m., and sold in Manhattan by 1 p.m.—enough time to enjoy a vodka with the Russian dealers they sold to and to pick up a new batch of ski masks at the Army-Navy store on their walk back to the Port Authority garage, where they’d parked.

From the summer of 2011 through late 2013, as gold prices hit record highs, Trotta’s crew made nearly $500,000, more cash than they’d seen in their whole lives. Trotta had launched nearly flawless heists on the Sterling Hill Mining Museum, in Ogdensburg, New Jersey; the United States Golf Association museum, in Liberty Corner, New Jersey; the Harness Racing Museum and Hall of Fame, in Goshen, New York; and the National Museum of Racing and Hall of Fame, in Saratoga Springs, New York. Among the objects melted into oblivion were golf’s historic U.S. Amateur Trophy; a replica of the golfer Ben Hogan’s 1953 Hickok Belt, a diamond-studded gold strip given to the best professional athlete of the year; an 18-karat Memphis Gold Challenge Cup awarded in 1902 to the trotting horse Lou Dillon; a silver Fabergé tureen that Czar Nicholas II gave to Dillon’s American owner in 1912, to thank him for introducing harness racing to Russia; and two 1903 trophies designed by Tiffany, one for the Brighton Cup, the other for the Belmont Stakes.

In 2012, Trotta stole from the United States Golf Association Museum, in New Jersey. (Bernards Township Police Department)

By late 2013, the FBI and the sporting press started to suspect a connection among the heists. Theories ranged “from the common street crime variety to complex schemes worthy of ‘The Sopranos,’ ” The New York Times reported that October. A year later, Trotta was in and out of the Yogi Berra Museum so fast that nothing looked amiss from the front doors when the police got there, about five minutes after the alarm sounded. “Because of the rain and wind,” one officer wrote in a report, “our visibility was limited.” Not until Berra’s son Dale arrived at the museum the next morning—he kept an office there—was the theft discovered.

When Trotta disappeared, sometimes for days, to case or burglarize a museum, he’d lie to his wife. He’d say he was in New Jersey doing HVAC work with Joe Atsus and Joe’s brother, Al, who had a contracting business. On their marriage certificate, in 2009, Trotta listed his profession, falsely, as “plumber.” Trotta told me that his wife never asked questions, so long as money came in. Trotta’s parents’ relationship had worked much the same way.

The trouble began after they had children. Trotta’s wife started to resent his frequent absences, which left her with too little help around the house. One night, while Trotta was on his way to a museum, she called to demand that he immediately return with supplies for their 1-year-old son. “She’s like, ‘Thomas needs diapers, you motherfucker,’ ” Trotta recalled. (His wife, who filed for divorce in 2018, told me that she preferred not to involve herself in this story, writing in an email, “I am ok with whatever Tommy stated.”)

Money was becoming tighter, too. Berra’s 16 rings and two MVP plaques—valued at $1.5 million intact—had grossed Trotta’s crew just $10,300 after melting. The more he and his wife fought, the more he wondered how long he could keep it up: the burglaries, the lies, all of it. He thought about day-trading or opening a restaurant. If he could pull one last job—a really big one—he’d have the capital to start an honest business, draw a steadier income, do better as a husband and dad. He was turning 40. It was time.

Yogi Berra poses with his World Series rings in 2000. (Steve Crandall / Getty)

He found an exit the way he’d found everything else: on Google. In 1894, the Russian Empress Alexandra wore a spectacular crown at her wedding to Czar Nicholas II, the same Nicholas, incidentally, whose Fabergé tureen Trotta had stolen from the racing museum in Goshen. Some 1,535 diamonds covered six velvet-draped silver bands, which converged beneath a cross made of six larger diamonds. Trotta believed he could get $5 million in the Diamond District by scrapping the crown’s stones.

The crown, he discovered, sat shockingly close to a first-floor window in a Washington, D.C., mansion once owned by one of America’s wealthiest women. Marjorie Merriweather Post—the cereal heiress, businesswoman, and philanthropist—had purchased Hillwood, as the 25-acre estate was called, in 1955 and filled it with fine art and collectibles from 18th-century Russia and France. In 1977, four years after her death, Hillwood opened to the public as a museum and gardens.

Trotta had cased it more than a dozen times before returning in the summer of 2015 for a crucial, final step. He called it a “night check”: hours spent in a car or in bushes, searching through binoculars for guards and other nocturnal activity. Trotta was a few minutes from the night-check spot when he got into a shouting match with his wife over the phone. He was jolted out of it by a flash in the street: A speed camera had photographed his vehicle, placing it uncomfortably close to Hillwood. He called off the night check and drove the four hours home, furious.

Back in Pennsylvania, he grew so impatient that he dispensed with his usual caution. Forget the night check, he thought. He’d return to Washington with a single mission: to take the crown and retire. He could misdirect the police by setting fire to—or, as he put it, “cooking”—a Hillwood outbuilding as a diversion. But Boland and Ralph Parry, another friend who’d agreed to accompany him, talked him out of the fire. “You want more charges?” Parry said, according to Trotta.

In the darkness of an August morning in 2015, Trotta used fence cutters to enter Hillwood’s grounds and a grinder to cut a bulletproof window some eight feet from the empress’s crown. As Trotta reached for his ax to smash open the display case, he heard a voice shout “Halt!” In the red glow of his headlamp, he glimpsed a man in uniform down the hall. The Hillwood, it turned out, employed night guards. Trotta leaped out the window he’d entered, yelling, “Pop the smoke.” Boland yanked the pin from a smoke grenade and lobbed it behind them as they ran toward Rock Creek Park and forded the creek to Parry’s waiting car. (In a statement issued through his lawyer, Boland called Trotta an “inveterate liar” with “no credibility.” Parry’s lawyer did not comment.)

The failure drove Trotta into a depression. Why hadn’t he returned for a night check? Why had he let his less experienced friends talk him out of a diversion? What was wrong with him?

Since childhood, he’d tried to abide by the one scruple his father seemed to have: Don’t do drugs. But after Hillwood, as his marriage crumbled, he needed release. He began taking Percocet, a narcotic painkiller, and became hooked, paying as much as $50 on the street for each 30-milligram tablet.

The money he made from a 2016 heist of the Roger Maris Museum—all the way out in Fargo, North Dakota—hardly seemed worth the hours of travel. And the drugs were making him sloppy. He cut himself so badly breaking into the International Boxing Hall of Fame, in Canastota, New York, and the Franklin Mineral Museum, in Franklin, New Jersey, that he trailed blood on the windows and floors. All for nothing; the boxing belts turned out to be made of a cheap alloy, and the mining stones—tourmaline, zircon, alexandrite—were worthless. Some malignant force seemed to be conspiring against him. When he entered Harvard’s Mineralogical & Geological Museum, disguised as a Hasidic Jew and ready to snatch a large diamond in the middle of the day, the stone, which he’d seen every time he’d cased the museum, was no longer on display.

His edge dulled by narcotics, he returned to the petty thievery of his youth: houses in his own neighborhood, dinky antique shops, convenience-store ATMs—whatever, whenever, for another handful of pills.

Trotta was driving to a friend’s apartment in the snowy early morning of March 4, 2019, when a Pennsylvania state-police cruiser came up behind him. His Pontiac had been fishtailing on the slicked roads, but the troopers didn’t make a traffic stop until he inexplicably pulled over. (Trotta told me he’d wanted to let the car pass, not seeing its police markings until too late.)

Trotta failed a field sobriety test and was charged with a DUI, illegal possession of controlled substances, the unauthorized use of someone else’s car (his cousin’s), the use of a different car’s license plates (his sister’s), and other motor-vehicle violations. He kept it together enough to refuse a blood-alcohol test: The last thing he needed was anyone tying his DNA to the blood he’d shed at various crime scenes. At the police barracks, however, he asked for water. A trooper fished his cup out of the trash and sent it to a forensics lab.

When troopers opened the Pontiac’s trunk the next day, they realized that the driver might be someone other than their usual yahoo out past his bedtime. Inside the vehicle were bolt cutters, a sledgehammer, a headlamp, ski masks, walkie-talkies, burner phones, bits of jewelry, a checkered shirt that had been caught on security cameras during a recent jewelry-store heist, and brochures for sports museums. When Trotta met his lawyer, he asked what the police had found in the car. The lawyer, a seasoned defense attorney named Joe D’Andrea, replied, “Everything but Jimmy Hoffa.”

Nine days after Trotta’s arrest, FBI agents gathered at a state-police barracks with law-enforcement officers from New Jersey, New York, Pennsylvania, Rhode Island, and Connecticut. They’d come to review a long list of burglaries with similar MOs and to figure out whether Trotta was their missing link. The DNA results that came in after the meeting erased all doubt.

Trotta was turning 44 that year. He’d known the core members of his crew since Ronald Reagan was president. They’d seen one another through graduations, marriages, kids, joblessness, substance abuse, divorce. Joe and Al Atsus were godparents to Trotta’s children. “If you’re robbing stuff at 11 or 12 with people, and at 40 you’re still robbing stuff with these people,” Trotta told me, “you can’t actually get a closer bond than that.” He estimated that over their lives together, they’d done more than 1,500 burglaries.

But when he was arrested, he said, not one of them came to his aid. No real money for lawyers or bail. No sympathy for the years of prison he might face—for crimes that had enriched them all. “He’s a big boy; he’ll eat it,” one of them told his sister. Dombek, claiming to be broke, gave Trotta a handful of screws, suggesting that he scrap them for a bit of cash, according to Trotta. The police, meanwhile, started using evidence from the Pontiac, and interviews with at least one associate, to charge Trotta with a series of local crimes: a 2016 ATM theft, a 2018 house burglary. “My friends,” Trotta concluded, “were prepared to bury me.”

In April 2019, with his lawyer’s encouragement, he began cooperating with state police and prosecutors, and eventually with the FBI. The police fitted him with a listening device, and he recorded damning conversations with Dombek, a man he loved like a brother. In one of those conversations, Dombek said that if anyone turned on him, he’d “sneak on their property, turn off the well cap, and pour a gallon of some kind of substance down their well and would kill that person and their whole family,” according to a police summary of the recording. Dombek also talked about destroying evidence of their crimes, and about plans to kill one witness by mixing fentanyl into his cocaine or by poisoning him with a toxic plant called false hellebore.

In August of that year, the state police raided Dombek’s property, discovered the makeshift chemistry lab, and charged him with burglarizing a house with Trotta. Released on bail, Dombek stormed over to the homes of Trotta’s mother and sister. He called Trotta a “fucking rat,” according to court records, and threatened violence if Trotta didn’t shut up or change his story. The police promptly charged Dombek with five counts of witness intimidation.

U.S. attorneys, meanwhile, persuaded their state counterparts to let them mount a single prosecution of nearly all the museum heists. As federal agents gathered evidence, Dombek and Trotta remained in a Pennsylvania prison on state charges: the former for three years, until he pleaded no contest to a single charge each of witness intimidation and home burglary; the latter for almost four years, until he pleaded guilty to the DUI, the ATM theft, and two home burglaries.

Then, in June 2023, the U.S. attorney for the Middle District of Pennsylvania announced federal charges against Trotta, Dombek, and seven other alleged ring members, including Joe and Al Atsus. (Al Atsus’s lawyer told me that any criminal allegations against his client were “absolutely ridiculous and patently absurd”; Joe Atsus’s lawyer declined to comment for this story.) Investigators had linked the ring to 21 burglaries across five states over more than 20 years. A press release credited 20 state and local police departments, as well as the FBI, for helping solve the case.

Boland and the Atsus brothers have pleaded not guilty. Dombek vanished into the woods when he learned of his arrest warrant but reappeared after six months and pleaded not guilty. Tommy and Dawn Trotta, Ralph Parry, and two others pleaded guilty as part of cooperation deals. “Very guilty, Your Honor,” Trotta assured a federal judge.

“We ripped the guts out of people emotionally,” Trotta told me. “I know that now.” In May 2019, while out on bail on the state charges, Trotta broke into a vacant house in New Jersey that the Atsus brothers owned. It was the last place he’d seen the Mathewson memorabilia, the Pollock, and the Warhol. He’d hoped to restore them to their owners, he said. But if the items were there, he couldn’t find them. Their whereabouts remain unknown.

In the days after the 1999 Mathewson heist, two of the pitcher’s biggest fans were among the most heavily interrogated. Eddie Frierson, an actor who’d written and performed the one-man show about Mathewson that night, endured searches by the state police and grillings by the FBI while still deep in grief himself over the loss to the college and to baseball history. “I was aching,” he told me.

Terry Wise was the Keystone College official in charge of the Mathewson display that weekend. When she’d been hired as athletic director a few years earlier, she’d encouraged the college’s president to do more with the memorabilia: How many schools could claim as an alumnus an inaugural inductee of the Baseball Hall of Fame? It was Wise who had opened the case that night to give visitors—including Trotta, she now realizes—a better look at Mathewson’s contracts and jersey. Worse than being questioned by six cops the next day are the feelings of guilt and naivete she still lives with. “I can’t believe it’s 25 years,” she said as we spoke in the gym parking lot, a few steps from the door where Trotta had let himself in.

Haley Zale launched a “Bring Back the Belts” campaign on social media after her great-uncle’s championship belts were stolen in 2015 from the International Boxing Hall of Fame. Tony Zale had beaten Rocky Graziano in 1948 to become the middleweight champion of the world. After Tony’s death, in 1997, Haley visited the museum every year to say her Hail Marys and Our Fathers, and to remember the shy man who’d come up from nothing, beaten Graziano, and been a grandfather figure to her—teaching her boxing stances, applauding her childhood ballet routines, and calling her Miss America. “Visiting Uncle Tony’s belts,” she told me, “was like visiting his grave.”

Lindsay Berra told me that her grandfather Yogi reacted to the disappearance of his rings and plaques with his familiar good humor. “Well, I know I won them,” he said. He worried only about the schoolchildren who visited the museum: Could the broken glass be cleaned up so the kids didn’t get cut?

Berra’s relatives took the theft harder. “Every one of those rings has a story behind it, and it’s about him and a team and the Yankees and a time in our country,” Lindsay told me. “You’re taking little pieces of American history when you take them. They belong to all Americans, not just the guy who won them.”

When the museum’s director called in 2023 to tell her that the alleged culprits had melted the rings for the sake of a few thousand bucks, Lindsay cried as much in sorrow as for the stupidity and waste. Wouldn’t it have been easier and more lucrative to knock off a Kay Jewelers? she thought. She couldn’t understand how Trotta could try on her grandfather’s rings—claiming to be a fan—only to moments later destroy them.

I asked if she believed Trotta’s professions of remorse. “No,” she said. “I think he’s sorry he got caught.”

Even after the police had found “everything but Jimmy Hoffa” in the Pontiac, even after he’d agreed to cooperate, Trotta burglarized a New York jewelry store and came close to ransacking the Saratoga Springs racing museum a second time. He aborted only because he’d spotted a guard during a night check. Then, in January 2024, as half his crew was headed for trial—with himself the star witness for the prosecution—Trotta allegedly stole gift cards, cash, and jewelry from the house of a woman he’d driven home from a bar. The police dropped the charges after Trotta’s lawyer gave the home’s owner a $7,500 check for the missing items, the local police chief told me. Trotta claims that it was a misunderstanding, but the federal judge overseeing the heist cases was displeased enough to revoke Trotta’s pretrial release.

The instinct to steal remains so strong, Trotta told me—so “in the blood”—that he feels as if his brain needs rebuilding. Like a recovering addict, he has to stay constantly on guard against his own impulses. He worries, too, about his son. At 11, he’s now the age Trotta was when Big Tom led him into a life of crime. When Trotta calls from jail, he talks with his son about the misery of incarceration: the bad food, the piece of metal with a half-inch mat that passes for a bed.

The only steady paycheck Trotta earned in his adult life was at Walmart in the early 2000s. (Courtesy of Tommy Trotta)

For a couple of years in the early 2000s, to satisfy probation on a minor theft charge, Trotta held a part-time job loading Walmart trucks. It was the only steady paycheck he’d earned in his adult life; he was miserable. “It was like the coal mines of old,” he told me. “It’s honest and you could wake up and feel proud, but, like, you’re in a category now of real broke-ness.” Guys could live paycheck to paycheck there for 20 years and never save enough for even a little self-indulgence.

Trotta had never grown rich as a thief. He’d taken some nice vacations, eaten some expensive steaks. But he drove junkers, dressed plainly, and had owned a house for just a couple of years before the payments became too much for him. His sister’s place, where he’d lived in a basement bedroom before and after his marriage, was perhaps the closest thing he’d had to a stable home. Yet a thief’s life—like a gambler’s—made wealth something other than impossible. Months would pass in which Trotta would scrape by on penny-ante burglaries. “Then, all of a sudden, a big thing would hit: Boom, we’re good.” And for however many days the money lasted, he felt free.

This article appears in the February 2025 print edition with the headline “Trophy Hunters.”