by Barry Goldman

My late father-in-law was an old-school family physician. He didn’t believe a computer would ever be as good a diagnostician as a human being. He understood that – in principle at least – a computer could read all the medical literature in every language and remember everything it read. He understood a computer would never over-weight its recent “experience” the way a person might. He knew a computer would not be subject to confirmation bias or groupthink or the rest of the errors to which the human mind is susceptible. But he still believed a good human doctor would always be a better diagnostician than a machine. He believed his decades of experience gave him a special sense that could not be captured in zeroes and ones.
I don’t know what Old Doc Silk would say now that AI can pass the bar exam and write symphonies, solve complex math problems, read x-rays, and write code. I suspect he would change his mind. Nevertheless, I find myself taking his position with regard to the work I do. I don’t think there will ever be a satisfactory AI labor arbitrator or AI judge.
The distinction turns on what we are trying to accomplish. Let’s start with an easy case. We know what an AI chess player is trying to accomplish. It is trying to win chess games. If it can beat its opponents, it’s a good chess player. And if it can beat all its opponents, it’s a champion chess player. Beating all its opponents just is what a chess champion does. The same is true of an AI that reads radiology scans. We know what a tumor is, and we can tell when a tumor has been correctly identified. If we have an AI that correctly identifies more tumors than all the other tumor detectors available (without falsely identifying things that are not tumors), then it is the best tumor detector. There is nothing else to it.
But what would a champion labor arbitrator or a champion judge do better than its competitors? If the answer is it would dispense justice better, that’s fine. But then we need to know what justice is. And that’s the problem we started with. Identifying justice is what we need judges and arbitrators for in the first place. Read more »





Sughra Raza. Blizzard in Fractals. Boston, February, 2026.
Over the past year, there has been significant movement in AI risk management, with leading providers publishing safety frameworks over the past year that function as AI risk management. However, the problem is that these are not actually proper risk management when you compare them to established practice in other high-risk industries.
C. Thi Nguyen’s The Score: How to Stop Playing Somebody Else’s Game (Penguin, 2026;
How can we possibly approach the world today without being in a constant stage of rage? Philosopher and psychoanalyst Josh Cohen’s 




No one sells out anymore. The first pages of
A thought has been nagging at me lately. Are most shitty people not very bright?

Kipling Knox: Thanks, Philip. Yes, that’s true—both books share a world with common characters. But that wasn’t my original intent. Between publishing these two, I started two other novels, with different settings. I put them both aside because I found myself drawn back to Middling. The story “Downriver,” in particular, ended so ambiguously that I was curious to know what would happen to its characters, Morgan and Arthur, and how their mystery would play out. It’s a difficult trade-off—sticking with one fictional world versus exploring others. When you write a book, you are deliberately not writing others, and there can be a sense of loss in that. But it’s very gratifying to explore a world you’ve built more deeply. I think of how a drop of ocean water contains millions of microorganisms, each with their own story, in a sense. So the world of Middling County (and also, in my second book, Chicago) has infinite potential for stories!