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.

U.S. voters have historically stuck with two political parties, but that’s changing in some states. USAFacts shows the shifts through voter registrations.

Colorado shows the first pattern most clearly. In 2016, the two major parties were nearly tied at around 30% of registered voters each, with unaffiliated voters already slightly ahead at 38%. By early 2026, more than half of Colorado’s registered voters belonged to neither major party.

This is a nice combination of ternary plots and connected scatterplots, with a bit of animation for good measure. [Thanks, Amber]

Stuart A. Thompson, a New York Times technology journalist, used Google’s Gemini chatbot to avoid a realtor fee and sell his house almost completely from start to finish.

I had started this experiment thinking that the chatbot would create a superpowered version of myself — combining my own judgment with its vast knowledge. But once I started relying on A.I., witnessing its know-it-all competency with basically everything, my shortcomings started to feel enormous and even risky. I had thought I was elevating my own skills. In reality, I was replacing them.

Thompson prompted for listing price, negotiations, and dealing with the mental hurdles from selling an asset that made most of his family’s net worth.

The house sold above asking price and there were fewer fees, from using Gemini, a general chatbot paid for by an employer. What happens to Realtors in ten years? It’s getting a lot easier to see homeowners and buyers sidestepping high fees and instead using specialized chatbots to negotiate with others using chatbots.

For the Washington Post, Federica Cocco and Taylor Telford report on the increasing difficulty for recent college graduates to find jobs.

The squeeze is hardest on those just starting out. At one point last summer, new workforce entrants made up a larger share of the unemployed than at any point since the late 1980s — higher even than during the Great Recession.

When hiring slows, the door closes first on those without an existing foothold. For the class of 2026, the timing could hardly be worse.

Fewer entry level jobs, more applications per job, and older people working longer have nudged unemployment for recent graduate above the national average. I think the yellow line for all workers should’ve been a dashed neutral color to draw attention to the comparison.

Every month I collect tools, data, and resources to make better charts. Here’s what happened in May.

For the Washington Post, Jeremy B. Merrill, Jonathan O’Connell, and Luke Connors built an AI tool to estimate the ubiquity of sports gambling ads in broadcasts and in arenas.

For its analysis, The Post chose 50 games across a variety of sports leagues, venues and networks, and randomly selected a one-hour segment during each of those games. The selected segments were then divided into one-minute intervals, and the analysis counted how many contained at least one reference to betting. By that measure, 27 percent — or 1 in 4 minutes — included at least one gambling reference.

Betting references were most frequent in hockey, appearing in 60 percent of the one-minute segments across eight hours of broadcasts. NCAA football had the least, with references in 6 percent of the segments across five hours of footage.

Gambling and sports broadcasts used to remain separate for at least an air of legitimacy in competition. It seems the networks just needed more money thrown in their direction.

Trump visited China, and then Putin visited a week later. Reuters compared the two visits from various angles. There are more important differences in this piece, such as the airport welcome and the talks, but my eyes perked up on the banquet menus.

Pope Leo XIV presented his first encyclical Magnifica Humanitas, which translates to Magnificent Humanity in English. It is focused on preserving human life as AI technologies change the way we interact with information and data.

On the misconception of AI as something human:

It is not possible to provide a single, comprehensive definition of AI. What can be stated, however, is that we must avoid the misconception of equating this type of “intelligence” with that of human beings. These systems merely imitate certain functions of human intelligence. In doing so, they often surpass human intelligence in speed and computational capacity, offering tangible benefits across many fields. Yet this power remains entirely tied to data processing. So-called artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships and do not know from within what love, work, friendship or responsibility mean. Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences. They may imitate language, behavior and analytical skills, or even simulate empathy and understanding, but they do not understand what they produce, for they lack the affective, relational and spiritual perspective through which human beings grow in wisdom. Even when these tools are described as capable of “learning,” their way of doing so is different from that of a human person. It is not the experience of those who allow themselves to be shaped by life and grow over time through choices, mistakes, forgiveness and fidelity. Rather, it is a form of statistical adaptation based on data and feedback, which can be very effective, but does not imply inner growth.

Bookmarking this for further detailed reading. There is a lot to digest.

Birth rates are falling nearly everywhere. For Financial Times, John Burn-Murdoch explores why that is, from shifts in education, work, how we interact with others, choices in relationships, and of course, technology that was supposed to make connections easier.

An aging population means more seniors 65 years and older are taking care of other seniors. This grows more challenging with disabilities, financial concerns, and mobility. The Straits Times looked at the situation through the lens of seven seniors caring for other seniors and their daily routines.

The comic illustrations pull you in to these people’s stories. I like the running clock on each as you move through the day and stay tied to the schedules.

U.S. inflation is high, and the prices of almost everything are rising fast enough to notice when we go to the store. You know this. I know this. Your bank account knows this. But the U.S. Department of Labor posted this monstrosity of a chart that shows the handful of things that decreased in price, as if it were something to brag about.

Here is the full dataset from the Bureau of Labor Statistics. Out of 340 line items, only 15% of them showed a negative change. The remaining 85% increased in price, including fuel oil, which increased by 54% year-over-year.

This is hardcore cherrypicking.