Rene Haas: “The areas that I personally find far more interesting are drug research and medical. A very simple example: You’re ill, you go to the pharmacy, they prescribe some medicine to you, and you look at the medicine and the side effects are as generic as it can be. That seems like something that, if the doctor knew my DNA genome sequence and would be able to map out exactly which drugs will give me what kind of reaction, knowing exactly my background and profile, that would be compelling. I was meeting this morning with somebody who’s in this industry and was asking that question. With AI, that’s probably three to four years away. Another interesting example is drug research. How long does it take to develop a new drug? Ten years. That can be cut in half, it can be cut by two-thirds by using AI. That to me is incredibly exciting.”
Ursula Burns on the mistakeshe has made that other head honchos keep repeating? “Not firing people who need to be fired. Not acting on someone when you knew that this person was not the right person for the role. The more senior I got, the more dangerous this inability for me to say, I’m going to give up on Joe.”
James Pethokoukis: “I’m sure there will be plenty of surprises in this emerging Age of Artificial Intelligence. The broad patterns will likely seem familiar to historians of technology. When a major new technology emerges, for instance, companies don’t just rush out and buy new equipment, ASAP. They need to completely rethink how they do business. At first, this typically means spending a lot of time and money on things like training workers and creating new ways of actually working. During this initial phase, as explained in the paper “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies,” it might look like the shiny new technology isn’t helping much because all this preparation work isn’t immediately visible in worker productivity. But once everything is in place and workers learn how to use the technology effectively, companies start seeing real improvements in how much they can get done. Productivity growth accelerates. The classic example here is industrial electrification, when it took a generation for factories to reorient themselves around this new technology.”
Arnold Kling: “With the advent of large language models, I expect to see menus die. As you know I think of LLMs as the next evolution of the human/computer interface. In the near future, I will not have to figure out the WSJ’s menu. Instead, I will simply type in or say “I want to submit a letter to the editor,” or “I am going out of town next week, so don’t deliver the paper then.” The LLM interface will have two advantages. A relatively minor advantage is that it saves users the trouble of learning menus. The major advantage is that the user will not be limited by the “features” as specified by menus. Users will be able to make up features as they go.”
TIME: “Overall, cancer is still overwhelmingly an older person’s disease. As of 2025, 88% of people in the U.S. diagnosed with cancer were 50 or older, and 59% were 65 or older, according to data from the American Cancer Society. But there is no question that the demographics are shifting. Under 50s are not only at increasing risk of suffering from cancer; theirs is the only age group for which the risk is rising. All told, 17 types of cancer are on the rise among U.S. adults in this age group.”
