Field notes on how AI reads names.

826 of 1,614 applications, under 55 legal names. Smaller than 2012 and more concentrated; 181 of 263 contested strings stay contested after every switch to a backup string, and private deals are banned.

DomainNameWire found 61 junk-looking domains that sold for up to $34,556. We ran ten of them blind against the Dynadot appraisal. The sale price landed inside our band eight times out of ten.

Every TLD’s size and every gTLD registrar’s share, month by month back to December 1983, with the month it covers and the name of whoever reported it attached to every figure.

The first application window for new top-level domains since 2012: $227,000 per application, who applied and why, how the round works, and when you'll actually be able to register names on these things. Updated after Reveal Day.

Answer Engine Optimization assumes AI can already place you. But for about a third of names, the models can’t even agree what the company is for — and that’s set by the name, before you buy. You can optimize what AI says about a name; you can’t optimize your way out of the name.

The good names left aren’t the ones you’ll brainstorm — they’re the ones you have to invent. Here’s how the desk finds them.

We invented 50 names and asked four models what industry each was in. The modal answer was “tech” — even for pure nonsense, even for words with a real non-tech root.

599 names probed. Most get a confident, unanimous read; about 8% split the models across industries — the highest-leverage band you can pick from.