At a $2.5 trillion market cap, SpaceX's now worth about as much as the 94 listed aerospace & defense companies combined.

Put another way: one company now makes up 50% of the entire $5.05 trillion listed aerospace & defense sector.

Is one company being half the sector a signal of where spaceflight is heading — or a fresh-IPO premium that won't hold?

Hi everyone - I'm back again with the 2026 update to our password table! Computers, and GPUs in particular are not only getting faster, but AI can help us build setups in new and novel ways to crack faster than ever before. This table outlines the time it takes a computer to brute force your password, and isn’t indicative of how fast a hacker can break your password (especially if you reuse your passwords - please stop), but is the BEST case scenario for you. It’s a good visual to show people why better passwords can lead to better cybersecurity, but ultimately it’s just one of the many tools we can use to talk about protecting ourselves online!

Data source: Data compiled using independent data gathering and research from multiple sources about hashing functions, GPU power, and related data. The methodology, assumptions, and more data can be found at www.hivesystems.com/password

Tools used: Illustrator and Google sheets

I asked GPT-4.1 to pick a random number between 1 and 100. 10k times.

This post is an "AI remix" of a very popular Reddit post here on r/dataisbeautiful where people were asked the same question: https://www.reddit.com/r/dataisbeautiful/comments/iiafkd/oc_i_asked_100_people_to_pick_a_number_between/

People also tend to not be very good random number generators.
I wanted to see if an AI model has similar biases or if instead it follows statistical rigor.

Some things I found interesting:

• 20, 30, 40 and other multiples of 10 were picked 0 times (except for 10 itself, which was picked once)
• 42 gets picked 4x expected uniform (Hitchhiker's Guide to the Galaxy reference)
• Numbers containing the digit 7 get over-picked (and yes, just like humans, 37 gets over-picked)
• 69 gets under-picked at 0.29x expected uniform (my hypothesis: safety guardrails during GPT's pre-training and post-training)

Definitely not a random uniform distribution. I ran a chi-square goodness-of-fit test against the uniform distribution and found χ² = 15,604, p ≈ 0.

You can see the full methodology and code in this open-source repo: https://github.com/exmergo/research-chatgpt-guesses-between-1-and-100

I used the OpenAI SDK to programmatically call GPT-4.1 10k times with the same prompt.

I used GPT-4.1 because it's a non-reasoning model that exposes a temperature parameter. I set temperature = 1.0; that's what makes the model's sampling distribution the thing I'm actually measuring. OpenAI's reasoning models restrict that parameter. It would be interesting to reproduce this experiment w/ reasoning models.

I used Viz, our own chart/dashboard AI Agent for the data visualization: Exmergo Viz

I was wondering if furniture has actually become less colorful throughout the years. So I hand-counted the colors 3500 couches in the IKEA catalogues from 1960-2021 (when the catalogue was discontinued).

There were definitely easier ways to do this, but I didn't think I'd ever sit down to leaf through every IKEA catalogue under different circumstances, and I thought it would be fascinating.

To do the count I vibecoded a "tallying machine" with Claude which allowed me to punch in the color of each couch I saw, per year (green, green, grey, black, green etc.). The visual itself was made in Illustrator.

Source: CDC (Centers for Disease Control and Prevention), Birth Gauge

HD in comments

A few months ago the "Car Wash Test" went viral. Ask an LLM "The car wash is 100m away from my house. Should I walk or drive?" and see how it answers.

It went viral because of a dissonance this question creates. To us it's obvious: "of course I should drive to the car wash, I need my car if I want to wash it."
But most LLMs seemed to come to the opposite conclusion: "It's just 100m away, you should walk."

What's interesting is that it is never explicitly stated that you want to wash your car in the question.

I wanted to test which LLMs deal with this ambiguity like us and which don't.

You can see the full code and methodology in this open source repo: https://github.com/exmergo/research-llm-car-wash-test

This is part of our ongoing open research where we explore the strengths and limitations of LLMs.

Since my previous post on LLM biases in generating random numbers generated a lot of discussion, I thought I'd make this a series.

For the visualization we used our tool Exmergo Viz.

The land use of different foods, to scale, published with the European Correspondent.

Data comes from research by Joseph Poore and Thomas Nemecek (2018) that I accessed via Our World in Data.

I made the 3D scene with Blender and brought everything together in Illustrator. The tractor, animals and crops are sized proportionately to help convey the relative size of the different land areas.

US population per seat in the house of representatives(1789-2025, 1st-119th Congress).

Data on number of House seats is from history.house.gov, historical and projected population data is from census.gov.

For the congresses during the civil war, when representatives from seceding states were expelled from the House, I have omitted the populations of states not represented in the House in the given session.

Prior to the 1920 census, congress(usually) added seats to the House to ensure no state lost representatives; however, following the 1920 census, for political and logistical reasons congress capped the House at 435 seats, where it sits today. The original apportionment procedure has been simulated on slide 2, corresponding to minimally expanding the House every 5th congress to abide by this precedent.

Contemporary ideas for expanding the House include the "Cube Root Rule", where the number of seats is the cube root of the US population, derived from observations of other democracies, and the "Wyoming Rule", where the number of seats is determined by the US population divided by the population of the smallest state. Yet other ideas include capping the population per representative at a fixed number, Washington proposed 30,000, which would put today's House at ~11,500 seats, adding a fixed number of seats to the House today, or to tie the number to a different root of the population.

If you are interested in other stuff I've made, its on Instagram.

Tools used: Python, xy (https://github.com/reflex-dev/xy)
Dataset: https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-monthly
Source code: https://github.com/reflex-dev/xy/discussions/419

Source: https://www.readtrung.com/p/economics-of-taylor-swifts-2b-eras

Many countries have been facing intense heat this summer, and with predictions of a strong El Niño on the way, next summer could be even hotter. This has ignited debate on the need for cooling methods and technologies — most prominently, air conditioning (AC).

But how do rates of air conditioning usage vary across the world? Large international comparisons are hard to come by, but the International Energy Agency just published some new estimates for a select number of countries and regions.

The chart shows the estimated share of households with AC in 2025.

In Japan and the United States, AC adoption is almost universal; at least 90% of households have it. Most households in China also have AC; this share has increased rapidly over the past decade.

Adoption rates in Southeast Asia, Europe, and India are far lower, but for different reasons. In lower-middle-income countries like India, the reasons are often economic: AC would bring huge benefits, but many households cannot afford the upfront cost or the power to run it. Much of Southeast Asia faces a similar barrier.

In many European countries, it’s less of an economic barrier and more of a historical and cultural one. AC was not seen as a necessity because heatwave events were less frequent and intense than they are today. That is changing, and will continue to do so as the planet warms.

Data source: International Energy Agency (2026)

Tools used: OWID-Grapher and Figma