Last month, the Wealth Inequality in America video by Politizane got an update after thirteen years. The gap between the richest and the normals has only grown. This interactive version uses the very tall chart from the video to show the scale of wealth for the top 0.1% of people.

Set your expectations for how long it will take to scroll from the bottom to the top. Then reset expectations as it becomes clear that it’s annoyingly tall. Keep going. Reset again. Keep going, change the scale, reset. There is an “I quit!” button for a reason.

Every month, I gather tools and resources to make better charts that communicate and reveal. Here is what happened in October.

For the New York Times, Arfa Momin and Ron Lieber charted median income of graduates, by college and major.

Your school can make a big difference — or matter very little — depending on the major. A graduate who studied mechanical engineering at Carnegie Mellon University earned roughly $10,000 more than a graduate from the University of Alabama. That difference is over $150,000 if those same graduates studied computer science.

You can also search for your college and major to compare.

There are correlation and causation considerations to make here, but the data from the Department of Education exists to hold schools accountable. If a school charges a high tuition, but graduates earn lower incomes, then the program is worth a closer look.

The charts in the NYT article are based on an analysis by the HEA Group, who have made their data available for download. [Thanks, Charlotte]

For the Pudding, Owen Lacey analyzed the network of Hollywood actors connected by movies they appeared together in. The goal is to find the “real” Kevin Bacon.

Lacey uses remoteness, or the average number of movies it takes for an actor to connect to all other actors in the network. Lower remoteness means a more connected actor. So to find the most connected, you find the actor with the lowest remoteness.

Kevin Bacon has a lot of acting credits, but there are many actors who are more connected.

The visual essay has a nice a structure that walks through past, present, and future with a well-made line chart.

I also appreciate the classic analysis on degrees of separation. It’s been a while. See also the six degrees of Francis Bacon, four degrees through Facebook, and the six degrees of Kevin Garnett (might be defunct).

For the New York Times, Raj Saha and Blacki Migliozzi used mazes to demonstrate how AI models are trained and how agents complete tasks in unexpected ways. Some argue the double-edged sword:

At the same time, he said, agents can’t be safely contained if they are given the tools and access they need to be useful. “An inconvenient truth about developing frontier A.I. systems is that minimizing safety risks and maximizing legitimate utility are incompatible objectives,” Dr. Casper said. “The same agent that is good at helping biologists debug lab experiments could also be misused for constructing biohazards. In this sense, building ever more capable A.I. systems is somewhat of an inherently dangerous activity.”

Call me crazy, but if the large companies are held financially and socially liable for the models they distribute, I suspect the companies might find a way to block “rogue” behavior.

Ethics aside, the mazes in this piece are a good illustration of how the AI gets made. I paused at each one to watch the simulations play through.

Local Wikipedia, by Ed Jefferson, subsets regular Wikipedia to people, places, and things that are within a half hour walk from you. Just enter a location and get the localness.

On a per token basis, the cost of using LLMs from Anthropic and OpenAI decreased a lot over just the past few months. Max Woolf lays out the fast timeline.

From the timeline above, there is a clear tit-for-tat between Anthropic and OpenAI, and it is reasonable to assume that these particular price cuts from OpenAI and Anthropic are in reaction to each other. However, no one cuts prices by 80% unless there is some externality in play. So, what gives?

You know the about pages on every site that provide a summary of topics covered. Maybe there is a little bit about the writer. Neal Agarwal took the about page in a more literal direction. There is an interactive 3-D scan (that you can download) and live metrics from Agarwal’s fitness watch, so you know that he took 3.9 hours of naps this week and has type B+ blood.

For Development Seed, Kiri Carini and Martha Morrissey explain what embeddings are and what they are used for.

An embedding represents this information as numbers grouped into dimensions of a “latent space.” We are used to describing our world with three spatial dimensions, but dimensions in latent space are mathematical rather than physical. Together, they encode patterns and relationships the model has learned from the data.

As humans living in a three-dimensional world, it’s hard to imagine a space with dozens, hundreds, or even thousands of dimensions. This makes embeddings difficult for humans to interpret directly, but computers have no problem working with these mathematical representations. In practice, an embedding is usually stored as a list of numbers.

Illustrations by Gus Becker provide a crochet metaphor to help you mentally link the steps. While in the context of geospatial data, the guide should also be helpful in understanding embeddings in general.

Terence Tao has become the unofficial spokesperson for the future of mathematics. Elisabeth Bumiller, for the New York Times, reports on the soured relationship between Tao and OpenAI:

OpenAI’s releases have now left him feeling used. “As recently as a year ago, I had a pretty good relationship with many employees at OpenAI,” he said in a recent phone conversation.

“A lot of people I interacted with shared a very common vision of how A.I. should be used — you know, to empower scientists.” But the rush to publish new findings, he said, shows their true purpose. They “used math for what they needed, which was to evaluate their models,” he said. “At this point, I don’t see a way to constructively engage.”

I feel for the PhD students who are four years into research and OpenAI comes along. I think the only successful path forward involves figuring out how these new tools intertwine with existing work.

Mathematician and Cornell professor Steven Strogatz was a guest on NYT’s Daily podcast to talk about the recent developments in Mathematics. Well, it starts out that way, and then Strogatz gets into purpose and touches on deeper feelings that many of us are getting to know.

This week, we switch the chart defaults to non-defaults.

If your electricity provider is an investor-owned company, it’s likely you’re paying more than someone with a public provider. For Bloomberg Green, Michelle Ma and Nadja Popovich highlight the difference:

Estimates are based on data from the Annual Electric Power Industry Report published by the U.S. Energy Information Administration.

Stephen Follows counted the number of shots in movies, from 1920 through 2025. The overall number of shots increased, and so has the range.

Between the 1930s and 1950s, films averaged between 550 and 580 shots. The median started to climb in the 1960s and didn’t slow until the current century. In recent years, the increase has been minimal, sitting at 1,479 for the 2010s and currently 1,559 for the first half of the 2020s.

Follows used TransNet V2 for transition detection and shot count.

More big numbers coming in. For NYT’s the Upshot, Aatish Bhatia demonstrates the millions of trillions (octillions!) of calculations required for training AI.

During its training, the A.I. is fed a portion of a sentence, and the knobs are adjusted using a series of math calculations to improve its guesses for the next word. This process is then repeated over trillions of words of training data.

It takes only a few additions and multiplications to adjust a single knob. But when trillions of knobs are adjusted trillions of times, the math quickly adds up.

And this is just the first step in training a language model, known as pre-training. It’s followed by additional steps of calculations that hone the A.I.’s behavior and improve its ability to code and solve math problems.

All those calculations require energy, data, and time. The big companies are taking what they can get with the first two things in efforts to reduce the third.

This is fun, or I guess very serious depending on your point of view. Georgios Karamanis adapted the Show Your Stripes project, which visualizes average temperature each year as a stripe, into a screensaver that runs in macOS.

It is fascinating how a simple view of data can fit into different spaces.

There is a proposition on the California ballot this year to impose a one-time tax for billionaires. The result would be a 5% tax on their net worth, which includes investments. For New York Times Magazine, Nicholas Konrad illustrates the scale of this tax on 250 individuals in the context of their wealth, the taxes they typically pay, and their spending to oppose.

For us regular people, one billion dollars is difficult to imagine, so the transitions between tiny and giant circles provide a sense of how big these numbers are. By absolute counts, the tax dollars are a lot, but by percentage, the tax rate doesn’t seem like that much all things considered.

This week, I walk through my own thinking as I poke at fresh data.

This is really going to surprise you, but the wealthy people in the United States became wealthier over the past three years. For the Wall Street Journal, Rachel Louise Ensign and Justin Lahart report on these changes with some charts.

The roughly 137,000 households who make up the nation’s top 0.1% had already become richer in comparison to everyone else in recent decades, thanks to higher incomes and lower tax rates. Now the stock-market boom, driven by strong corporate earnings and highflying tech stocks, is powering their wealth even further.

By comparison, the rest of the top 10% of richest Americans gained $38.2 trillion in wealth over the same period, but it was split among nearly a hundred times as many households.

This is based on fresh Federal Reserve data from the Survey of Consumer Finances.