by David Kordahl
When I was a physics graduate student at Arizona State, one of my fellow grad students had a roommate who, after obtaining an undergraduate business degree, got a job as an office assistant for an economics professor. The roommate (remember, not a grad student) was earning like a young professional, not a wizard’s apprentice. “You know what he’s doing this week?” the grad student told us during a typical lunchtime rant. “He’s downloading files one by one from a dot-gov website and copying them into spreadsheets. A week of work, for what I could write as a five-minute script! And he’s getting $60,000 a year for this!”
Whether or not $60,000 sounds like a lot of money depends on one’s position. My whole life, I have seen claims the median physics B.S. graduate makes that much, but I personally didn’t make $60,000 until my third year as an assistant professor—and, even then, only after getting another job and negotiating a raise.
Science Nonfiction: Behind the Scenes in University Research, the new memoir from Dr. Darren Lipomi, chair of the Department of Chemical and Sustainability Engineering at the University of Rochester, addresses such issues bluntly—until it doesn’t. “There is a fair case to be made that the financial burden of research is borne not by the taxpayer,” Lipomi writes, “but by the ‘forever trainee’—the twenty-two-year-old PhD student who becomes a postdoctoral scholar at twenty-nine, and an untenured research scientist at thirty-four.” But now in his mid-forties, having more or less figured the system out, Lipomi is doing better than fine, and his memoir charts an uneasy path between celebration and critique. Read more »
There has long been a temptation in science to imagine one system that can explain everything. For a while, that dream belonged to physics, whose practitioners, armed with a handful of equations, could describe the orbits of planets and the spin of electrons. In recent years, the torch has been seized by artificial intelligence. With enough data, we are told, the machine will learn the world. If this sounds like a passing of the crown, it has also become, in a curious way, a rivalry. Like the cinematic conflict between vampires and werewolves in the Underworld franchise, AI and physics have been cast as two immortal powers fighting for dominion over knowledge. AI enthusiasts claim that the laws of nature will simply fall out of sufficiently large data sets. Physicists counter that data without principle is merely glorified curve-fitting.
The photograph beside this text shows two men standing side by side, both scientific celebrities, both Nobel prizewinners, both of them well-known and well-loved by the American public in 1932, when 
Physicists writing books for the public have faced a longstanding challenge. Either they can write purely popular accounts that explain physics through metaphors and pop culture analogies but then risk oversimplifying key concepts, or they can get into a great deal of technical detail and risk making the book opaque to most readers without specialized training. All scientists face this challenge, but for physicists it’s particularly acute because of the mathematical nature of their field. Especially if you want to explain the two towering achievements of physics, quantum mechanics and general relativity, you can’t really get away from the math. It seems that physicists are stuck between a rock and a hard place: include math and, as the popular belief goes, every equation risks cutting their readership by half or, exclude math and deprive readers of a deeper understanding. The big question for a physicist who wants to communicate the great ideas of physics to a lay audience without entirely skipping the technical detail thus is, is there a middle ground?
