5 "Generative AI" Posts

You know the story: drop a cookie on the kitchen floor, swoop in before five seconds are up, and declare it safe. It is comforting. It is also wrong.

“Germs don’t wait five seconds. They start the party the instant your food hits the floor.”

The truth is much more interesting than the myth. Germs do transfer gradually, but they are especially fast at the beginning. That means if you want to know whether your floor-cookie is still edible, you need to think in curves, not in timers. And curves are something we can model.

Every week your calendar fills with more meeting invites than you can reasonably handle. Which ones are worth the time and energy, and which should you politely decline? What if there was a way to quantify that choice?

“Your calendar is a knapsack. Every meeting takes space, but only some add enough value to justify carrying them.”

The good news: math can help. By modeling your schedule as a 0/1 knapsack problem with two constraints , you can treat meetings like items with value, time cost, and energy cost. Classic optimization techniques then help decide which meetings to attend. In this post, we’ll walk through framing the problem, prompting AI to scaffold the code, and running a simulation to visualize your optimal “meeting diet.”

My first full-time programming job was for Holiday on Ice, an international ice show. While I focused mainly on back office systems such as accounting, itinerary, and box office reporting, I knew that one of the biggest technical challenges faced by the show’s crew was efficiently loading trucks for the next city.

“Given the dimensions of a truck and a list of containers (with their dimensions and weight), in what order, position, and orientation should you pack the truck?”

One day, the controller asked me if I could code a system that took, as input, the trucks’ 3D dimensions and the 3D dimensions (and weight) of every object to be packed. Back in the Turbo Pascal era, exploring 3D packing was painful. Today, with Python and AI-assisted scaffolding, it’s surprisingly approachable.

If ChatGPT can write code, summarize legal briefs, and help draft business strategies in seconds, why doesn’t that show up in our productivity statistics?

Economists have long relied on a metric called Total Factor Productivity (TFP) to measure technological progress. But in an era of free digital tools and generative AI, TFP looks more like a rearview mirror than a windshield. It tells us a lot about the past, but almost nothing about where the economy is headed.

You can see the computer age generative AI everywhere but in the productivity statistics.

(Adapted from Robert Solow, 1987)