Elon Musk became the world’s first trillionaire with the SpaceX initial public offering today. For Bloomberg, Ben Steverman, with illustrations by Tim Enthoven, provides a quick ridiculous overview of what $1 trillion gets you.

See also the Chalabi classic on Jeff Bezos wealth.

The Apocalypse Early Warning System by Kyle McDonald was built on the premise that rich folks will use their private jets to get out quick in case of a global emergency.

This site watches a fixed cohort of business jets and asks a simple question: is the number currently airborne unusual for this time? It is not tracking all aircraft. The original version used an FAA-only business-jet list. The current tracker builds a broader global aircraft metadata table by merging ADS-B Exchange aircraft records, Mictronics/tar1090 records, and FAA registry data by ICAO hex. The importer classifies metadata into business jets, military aircraft, large airliners, regional airliners, non-jet aircraft, and other known types, then applies a practical business-jet filter. Each tracked aircraft is matched in live data by its ICAO hex identifier.

I think this is only partially tongue-in-cheek? Data is updated every half hour and you can sign up for updates through Telegram or text.

Another recent flip: Based on estimates from Ember, monthly solar-generated electricity reached an all-time high in May and coal reached an all-time low in April. It looks like it won’t be long until both solar and wind consistently beat coal.

Speaking of when data flips, last night in game four of the 2026 NBA Finals, the New York Knicks were down by as much as 29 points to the San Antonio Spurs. According to Mike Beuoy of Inpredictable, there was a less than 5% chance of the Knicks winning by the middle of the second quarter. With nine minutes left, the Knicks had a less than 1% chance.

Then began the biggest collapse in NBA Finals history by the San Antonio Spurs.

This week, we focus on when there is a sudden change or flip in the data that you want to highlight.

The New York Times counted Elon Musk’s promises on X/Twitter and Tesla earnings calls since 2011. Over the years, there have been more declarations, but the number of achievements per year changed little in the past five years. Instead, the rate of non-achievements appears to be rising.

It’s going to be hot in North America as the World Cup 2026 starts this week. But the level of heat will vary over time and city, which means teams will experience varying levels of heat depending on when and where they play. Bloomberg crossed the forecast with playing schedule to estimate the heat stress for each team.

Tunisia, France, and Ghana will play in the hottest temperatures. Uzbekistan, the Democratic Republic of the Congo, and Spain will play in the coolest temperatures, including the sweet air-conditioned stadiums in Atlanta and Houston.

I’m saving this for later. I wonder if this affects performance in the end.

Companies use seismic airguns in the Gulf of Mexico to find oil and gas deposits. The airguns deliver air blasts in the water, and the time it takes for an echo to return can be used to estimate metrics on the seabed.

For the New York Times, Katherine Chui and Catrin Einhorn demonstrate, with visualization and audio, how the waves from the airguns interfere with whale communication.

The Gulf of Mexico, which the Trump administration calls the Gulf of America, is one of the noisiest bodies of water in the United States. Air gun blasts are the loudest element there, according to research by scientists who monitor underwater acoustics. Shipping traffic is another major contributor.

The noise could affect the ability of Rice’s whales to find food and mates, scientists say. The chronic stress of living in a loud environment could be detrimental to their health.

The maps, graphics, and sounds combine well to emphasize the problem.

For Bloomberg, Laura Millan, Kyle Kim, and Armand Emamdjomeh mapped the projected extent of melting glaciers in the Alps. The risk of avalanches and rock slides has risen, which could lead to buried towns that reside below.

In the above, the outer border represents the 2010 extent of selected glaciers. The solid blue is the extent that researchers have projected by 2100.

I think the color scheme confused me at first, because I thought I was looking at land mass with ice on top. I wonder if a second shade to indicate the melted portions would be helpful, or maybe a flipped focus to only show the melt would highlight how much is disappearing.

Traffic has been rising extra quickly these past couple of years. Unfortunately (or fortunately, depending on your point of view) it’s mostly from automated AI bots scraping all they can get. From Matthew Prince, the CEO of Cloudflare:

Welp, that happened faster than I predicted. Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet’s history.

It was only nine months ago when bots accounted for 30% of web traffic. Ninety percent next year?

LLM speed is commonly expressed as tokens per second, which is kind of meaningless for the uninitiated. Mike Veerman made a more intuitive view for what various speeds look like.

More tokens per second means faster generation, which you can clearly see through the output appearing in the box. Select between code, text, thinking, and agent models.

I’m pretty sure the tool was made with an LLM (probably Claude based on the look and feel). The obvious next step is to go full meta to show a site that is generating itself based on the selected speed.

For Bloomberg, Demetrios Pogkas, Jennah Haque, and Kiel Porter show the projected scale of SpaceX’s initial public offering later this month against the current top 100 listings globally.

Rising bubbles are the correct metaphor here. The vertical position represents offer size, the horizontal represents time, and the size of the circles represent market capitalization after the offering. SpaceX floats far above.

This week, we look past the chart and ponder its true purpose.

For NYT Opinion, Emmanuel Saez and Gabriel Zucman make a case for a billionaire wealth tax in California.

The billionaire class in California includes roughly 250 households, a mere 0.001 percent of the state’s families. Yet its wealth now amounts to more than half of California’s entire annual economic output.

This means that if these billionaires spent all of their wealth, they could buy more than half of the goods and services produced in a year in the entire state.

This extraordinary wealth does not translate into extraordinary tax contributions.

They use a tip-of-the-iceberg visual metaphor to show the relatively modest taxes on income against the majority of wealth beneath the surface in assets. California wants to charge a 5% tax on the entire iceberg spread out over a five-year period.

For NBC News, Jane Weaver, Jiachuan Wu, and Javier Zarracina report on the Ebola outbreak in the Democratic Republic of the Congo. When compared to past outbreaks since 2012, the trajectory of the current outbreak looks more intense, as indicated by a much steeper line.

Higher unemployment among young workers has been commonly attributed to generative AI. In their paper from the National Bureau of Economic Research, Natalia Emanuel, Emma Harrington, and Amanda Pallais argue that the rise in remote work during Covid times is a bigger factor.

They compared unemployment for “remotable” and “non-remotable” jobs and then took the difference between younger and older workers:

The aggregate increase in the unemployment rate for young college graduates can be traced to remotable occupations, where young people’s unemployment rate increased by almost 1 percentage point between 2017-19 and 2022-24. By contrast, the unemployment rate of older workers in remotable sectors marginally declined over that period. As a result, the age gap in unemployment between younger and older workers significantly increased in remotable occupations. This relative increase in young people’s unemployment coincided with the pandemic and has remained elevated since then, as have rates of remote work.

The researchers further argue that AI is not yet a main factor for the shift in unemployment. In the chart above, note the sudden rise of the black line for remotable occupations in 2020, a slight taper afterwards, and then a continued rise.

Charts in the paper do need explaining and could use a layer of annotation, but the conclusions seem to make sense.

For the Pudding, Minji Kim and Eunice Lee wrote about the growth of K-Pop through the lens of their friendship.

Minji and I first met when we were nine years old, at a Korean language school that operated out of a high school on Saturday mornings. We were kids in the late ’90s in the suburbs of Detroit, where hanging out meant going to each other’s houses doing nothing. For us, though, we had a familiar routine: drink aloe, eat Korean snacks, and sit cross-legged on the floor of her family’s living room while listening to BoA’s “No. 1” on repeat.

I got my introduction to K-Pop around the same time when my friend gave me a Fin.K.L. compact disc. The fandom confused me then and it confuses me now, but it is a fun trend to observe from the sidelines.

Japan has been aging and having fewer children, which led to a decline of 3.1 million in population over the past five years. For the New York Times, Javier C. Hernández, Pablo Robles, and Kiuko Notoya have the charts and maps to show the drops.

This is a nice step chart. The red-orange hatching emphasizes the negative range, or a net population loss over time. The increase-decrease annotation on each side of the x-axis reinforces the meaning of the values.

Erin Davis calculated the average age of people with a given name to find the oldest name in the United States:

In short, the U.S. government produces estimates of the share of people born in year X who will still be alive in year Y. It also produces data on how many babies with a given name are born in each year.

By combining these two datasets, we can estimate how many babies with a specific name born in year X are still alive in 2025. Then, we can use those numbers to find a weighted average age for that name. (One big flaw this doesn’t account for immigration, but I haven’t found a way around that)

Myrtle wins for oldest average age. Davis provides an interactive version to search for your name.

The U.S. Census Bureau released a names dataset for first names and surnames.

The Census Bureau receives numerous requests to supply information on name frequency. In an effort to comply with those requests, the Census Bureau has embarked on a names list project involving a tabulation of names from the Census of Population and Housing.

These files contain only the frequency of a given name, no specific individual information.

You can currently download data for names that occurred at least 100 times in the 1990, 2000, 2010, and 2020 censuses.

I wonder how well these match with the annual baby names dataset from the Social Security Administration.