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Your pennies are now collector’s items. The last penny was minted Wednesday at the U.S. Mint in Philadelphia, spelling the end of America’s longest-running coin design. More than Andy Warhol’s Marilyn Monroe or Leonardo da Vinci’s Mona Lisa, it’s sculptor and medalist Victor David Brenner’s profile of Abraham Lincoln on the humble penny that’s actually believed to be the most-reproduced piece of art in the history of the world: the U.S. Mint estimates some 300 billion pennies remain in circulation. And even though no new pennies will be minted, the coin will remain legal tendergood news for those inclined to give a penny, take a penny at their local gas station. The penny’s rise to government-issued pop art status begins in 1793 when the Mint’s first one-cent coin went into circulation. That first copper coin showed an image of a long-haired woman representing liberty, a design element that was mandated by law. The Coinage Act of 1792 required coins have an “impression emblematic of liberty,” though it was later changed, paving the way for Lincoln to be featured. [Photo: National Museum of American History] The design of the reverse side of the first one-cent coins in 1793 showed a chain of 15 links representing the 15 states in the Union at the time, but the links were swapped out for a wreath in later coins because the chains were misinterpreted for symbolizing slavery, according to the Mint. The Mint says early coins from before 1909 showed personifications of liberty in the form of a woman rather than showing U.S. presidents in part because some lawmakers thought putting the head of state on a coin was too similar to the U.K. where the monarch is pictured on currency. [Photo: Lost Dutchman Rare Coins/Wiki Commons] In 1909, then-President Teddy Roosevelt marked the occasion of Lincoln’s 100th birthday by putting his likeness on the penny. Roosevelt selected the rendering by Brenner, a Jewish, Lithuanian immigrant who was then considered one of the best relief artists in the country, and who had designed a bas-relief of Lincoln based on a photo by Mathew Brady. It was the first time a President’s likeness appeared on a U.S. coin. Since Lincoln took over, the reverse or “tails” side of the penny has rotated through different designs, including an image of the Lincoln Memorial by Frank Gasparro from 1959 to 2008. After that, the Mint introduced four designs representing Lincoln’s life in 2009 for his 200th birthday, like a log cabin, followed by the Union Shield to symbolize Lincoln preserving the Union in 2010. [Photo: US Mint] The Mint says the cost of producing a single penny has risen from 1.42 cents in 2015 to 3.69 cents in 2025, and President Donald Trump said in February he asked the Treasury Department to stop producing new pennies. With the billions of pennies still in circulation, it will be some time before Brenner’s famed Lincoln portrait will completely be history. Even if you melted down every penny on Earth, you couldn’t get rid of it, because in 2012, NASA’s Curiosity rover took one to Mars.
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E-Commerce
How do you explain the laws of physics to a toddler? A new children’s book, titled Simple Machines Made Simple, wants to demystify mechanical engineering for kids as young as a year old. It recently beat its Kickstarter goal by 700%raising more than seven times its target. It will be available to ship early next year. But Simple Machines Made Simple isn’t your typical picture book. Instead of drawings, the book features working models that kids can interact with, like spinning a wheel, sliding a knob up an inclined plane, and pushing a wedge into a block that splits into two. The kids may not graduate with a physics degree, but they might come away with a curiosity for the world around them. “Maybe they can’t explain it, but it starts to build intuition for how things work,” says Chase Roberts, a computer engineer who created the book. [Photo: courtesy Chase Roberts] Roberts, who spent the better part of a decade making phone apps, moved away from technology in 2021 to more tangible objects that can teach kids basic and useful skills. His first book, Computer Engineering for Babies (2021), used buttons and LEDs to explain to kids how computers think by teaching them basic logic gates like NOT, AND, OR, and XOR. The sequel, Computer Engineering for Big Babies (2023), swapped buttons for rocker switches and introduced more LEDs to challenge slightly older readers. Roberts was planning a third sequel when he caught one of his three young children catapulting cereal off a spoon one morning. The idea for a book about mechanical engineering was born. [Photo: courtesy Chase Roberts] Book vs. machine Sooner or later, our children will find out they can learn how something works by simply prompting ChatGPT or asking Gemini. What, then, is the point of teaching them how pulleys or wedges or even computers work? For Roberts, it’s about instilling fundamental skills from a young age. “We still learn to add and multiply even though we have calculators,” he says. “My kids in elementary school are learning how to multiply and divide on paper because weve decided it’s still important.” [Image: courtesy Chase Roberts] To help both kids and parents look for “simple machines” in their everyday lives, Roberts has included examples for each machine in the book. Wheels appear in scooters, roller skates, and pizza slicers. Escalators and ramps are nothing but inclined planes. Shovels, knives, and axes act as wedges. [Image: courtesy Chase Roberts] “Being able to play with these machines, all together in one place, we’re giving it a name and drawing attention to how magical they are,” he says. “It’s pretty amazing that we figured out these ways to leverage the world. Theres this [lever] you can’t turn, but if we add a huge rod to it, it’s not that hard.” Making engineering fun Roberts’s books appear to have struck a chord. “I get emails from people all the time saying ‘This is my daughter’s favorite book, he says, even though his actual target audience is less the kids but the adults who buy the books for them. [Image: courtesy Chase Roberts] More often than not, his target audience is made up of engineers. In fact, Computer Engineering for Babies went viral after Roberts posted about the book on Reddit, specifically the Arduino subreddit, where people discuss everything related to the popular microcontroller that Roberts used in his first book. “I thought, Those are my people. If anybody’s going to appreciate it, it’s these guys.” According to Roberts, his books tend to resonate with engineers not only because they speak the same language but also because they manage to repackage complex systems into something fun that engineers can finally share with their kids. As it turns out, the best way to teach kids how things work is to play with them.
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Data is an omnipresent facet of modern existence, yet the current discourse around it is often too technical, academic, and inaccessible to the average person. Speak Data, the book I’ve just published with my coauthor Phillip Cox, emerges from more than 15 years of living and working with data, both as designers and as human beings.Instead of a textbook or how-to manual for designers, we imagined a more accessible exploration of the human side of data, enlivened by the perspectives of experts and practitioners from many disciplinesfrom medicine and science to art, culture, and advocacy. In an era when we are all talking about AI, the climate crisis, surveillance and privacy, and how technology shapes our choices, we wanted to reframe data not as something cold or distant, but as something deeply personal: a tool we (as human beings) can wield to understand ourselves and the world better. The book explores what we call Data Humanism, an approach that brings context, nuance, narrative, and imperfection back to the center of how we collect, design, and communicate data. In this excerpt, organizational psychologist and best-selling author Adam Grant reflects on how we interpret and communicate data, especially in moments of uncertainty, and why stories and emotions are just as essential to understanding information as statistics themselves. Adam Grant is the Saul P. Steinberg Professor of Management and Professor of Psychology at the Wharton School of the University of Pennsylvania. Yet that impressive title barely covers the full breadth of his activities. Adam is an academic researcher, an award-winning teacher, a best-selling author, a podcaster, and a public intellectual. Hes interested in big human topics like motivation, generosity, rethinking, and potential. Hes also the author of six books, including the best-selling Think Again: The Power of Knowing What You Dont Know. In this conversation, Adam talks about learning lessons from the pandemic; datum versus data; and how abstract numbers can lead to very real human outcomes. [Photo: courtesy Pentagram] As a psychologist studying organizational behavior, data is a tool that you use every day. What do you think people get wrong about data the most? People often have a very hard time accepting data that challenge their intuition or experience. I always want to tell them that if the evidence disagrees with your experience, you shouldnt immediately say the data are wrong. It might be that youre an outlier, that your experience is not representative, and the data are actually revealing a trend that you simply dont fit. A lot of my work relates to how people interpret social science research, because thats where I confront the general public. One thing I see a lot is people reading a study and then figuring, well, that study was done with a sample of only a few thousand people in this industry or that country, and dismissing the results because of that. This is basic confirmation bias and desirability bias. You shouldnt trust your personal opinion over rigorous evidence gathered across many people. [Photo: courtesy Pentagram] In an article you wrote for The Guardian, you describe arguing with a friend on the efficacy and safety of the COVID-19 vaccine. You wrote, I had fallen victim to what psychologists call binary bias. Its when we take a complex spectrum and oversimplify it into two categories. If we want to have better arguments, we need to look for the shades of grey. This is more or less what youre talking about. With all that in mind, what is the utility of data? The analogy I use is medicine. Today we have evidence-based medicine, but once upon a time, medical professionals tried to solve problems via bloodletting and lobotomies. Thanks to randomized controlled trials and careful longitudinal studies, we now have much safer and more reliable treatments. With evidence-based medicine, people are living longer and are healthier. So now look at how we interpret data from medicine. If you were to summarize all the randomized controlled trials of the average effect of ibuprofen on pain reduction and express the findings in the form of a correlation from -1 to +1, most people would think the correlation would be 0.7 or 0.8. After all, we have a lot of Advil in the world. But in actuality, an analysis showed that the average correlation was 0.14. Thats shockingly low to a lot of people, but the fact that its a small effect doesnt mean its insignificant. Thats the first lesson: Patterns in data do not have to be large to be consequential. You play that effect out over millions and millions of people, and a lot of people will benefit. And that benefit will be widely distributed. Secondly, the treatment doesnt have the same effect on everyone. There are contingencies. So instead of asking whether Advil is effective, we want to ask: For whom is it effective? When is it effective? This question of when and for whom allows us to look at the data and say: This is real, but only under certain circumstances. Now we need to know how widespread those circumstances are. This is real for some people. What are the commonalities of those people? The last lesson from medicine is that whats effective evolves over time. The problems were trying to treat can change. We need to update our evidence and ask: What are the best available data on any given question or for solving a given problem? Is there a reason why what was true 10, 20, 30 years ago may not apply today? I would still rather base my opinions on strong evidence thats old than no evidence at all, but we need to keep an eye on how things evolve as our contexts change. Exactly. Whats the context? What are the nuances? Data is a snapshot in time. Tomorrow, or in a month, things might be different. Espcially when we see data represented in a very definite and defined way, we assume it has absolute power to always represent a situation. This became a problem during the pandemic, of course. I think the biggest pandemic takeaway regarding the role of data is that experts and public officials did a remarkably terrible job communicating about uncertainty and contingency. I should have known it was going to happen. Chapter 8 in my book Think Again, which I wrote before the pandemic, was about how you dont lose trust when you say, More research needs to be done, or Here are the initial conclusions, but there are conditions under which they may not hold, or Here is what our initial trials suggest. Once weve done more trials, well update our conclusions. And let people know what that process looks like and how the scientific research is not only done, but accumulated. This is probably the most useful thing Ive said to a friend of mine who is very skeptical about vaccines after three-plus years of debate. He would say to me, One study says this and one study says the opposite! My response is that you shouldnt weigh both sides equally. You should weigh strong evidence more heavily than weak evidence. We need to be much more nuanced in how we communicate. We need to clarify where theres uncertainty. We need to highlight where there are contingencies. We need to be as open about what we dont know as about what we do know. One of the things we saw during COVID-19 is that source credibility dominates message credibility. People will believe a weak argument from someone they trust much more readily than a strong argument from someone they dont trust. One of the ways you become a trusted source is by very clearly admitting your uncertainty, showing intellectual humility, and expressing doubt where appropriate. I hope we dont have to keep relearning that lesson over and over again. Whats your personal definition of data? Data are information gathered through systematic and rigorous observation. We love that you say data are. To us as well, data is plural. A datum, or a data point, is one piece of information. Data are the collections of those observations. [Photo: courtesy Pentagram] To change the subject slightly, youve spoken in the past about the relative power of data versus stories to influence people and change minds. This is also something we think a lot about in our work. When do you think a really powerful statistic is appropriate, versus when a human story is going to be more effective? And when can they be combined? Its a false dichotomy to say they cant be combined. My point of view on the responsible use of stories is that we should start with the data and then find stories that illuminate the data. Stories are often more effective at evoking emotion. They allow us to distance ourselves from our own perspectives a bit. In addition to immersing ourselves in the narrative, they immerse us in a character. We get transported into stories, and we tend to experience them more than we evaluate them. Sometimes that can make people less rigorous in scrutinizing data, and that becomes a problem when the stories arent guided by data. The more surprising data are, the more likely they are to capture attention. If you have data that challenge peoples intuition, youre much more likely to pique their curiosity. But you have to be careful, because, as the sociologist Murray Davis wrote in his classic paper Thats Interesting!, people are intrigued when you challenge their weakly held intuitions, whereas they get defensive when you question their strongly held intuitions. So theres nuance there. From a visual perspective, we try to anchor stories in more aggregated data, but then disaggregate them by pulling out a couple of data points that can explain the context. By doing this in a narrative way, it can become more accessible, like a plot of a book. Thats really fascinating. Another way to tell a story about data is to start with what people would expect, then lead them to overturning their assumptions. People often find that journey revealing and enlightening, and it can become an emotional arc. Yet another thing Ive learned is to present a surprising result and then ask people how they would explain it. It opens their minds quite a bit: they generate reasons they find persuasive, and thus become active participants in the dialogue. Instead of preaching your view or prosecuting theirs, you engage them in the process of thinking like a scientist and generating hypotheses. I quite enjoy that.
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