(Credit: Getty Images)

These fake start-ups highlight tech firms’ silly name trends

Tumblr, Flickr, Grindr – tech firms sure like to follow rigid naming conventions, and this comedy company’s predictive tools can come up with names for them all on their own.

If you're thinking of launching a start-up nowadays, it seems you have only a few options for a name: tacking on a go-to suffix like -ly or -ify (Contently, Spotify), dropping a vowel (Tumblr, Flickr) or picking something that sounds like a magician's incantation (Shazam, Hulu, Venmo).

Silicon Valley's regurgitated naming trends have become so predictable, it made us wonder: do you even need a human to come up with these names anymore?

So as part of our Faking It series this month, we thought we’d find out, by using an algorithm to create a series of made-up companies that sound plausible in the real world.

Botnik used a neural network to create the names of these fake start-ups, and a predictive, idiom-specific keyboard to create their bizarre – but familiar-sounding and occasionally gramatically suspect – descriptions.

To do it, we enlisted the help of Botnik, a Seattle-based community of artists, developers and writers which blends AI and comedy to create viral hits such as this fake Coachella lineup and this new Harry Potter chapter. By looking at a list of hundreds of mostly US-based start-ups and on angel investing directories like AngelList, Botnik used a neural network to create names and descriptions of 10 imaginary start-ups. It's a kind of deep-learning tool that recognises patterns to make forecasts.

They looked at common placements of letters and certain nomenclature constructions, for example, to develop a program that instantly conjures company names that sound right at home among portmanteaus like Pinterest and calculated misspellings like Digg.

The team then used a predictive text tool to come up with realistic descriptions of the made-up companies based on commonly used phrases by real start-ups. Despite the odd grammatical and stylistic blips, most of what the text tool produced sounds eerily familiar – which shows how derivative a lot of this language can be.

So for example, “after the words ‘we build’, there’s a certain distribution of words that’s likely to occur in the literature of the source material,” says Botnik CEO Jamie Brew. “That’s how we wrote the tagline and descriptions – the most likely verbiage for a startup to use in describing themselves.”

As long as companies keep using predictable ways to describe themselves, the easier it will be for us to predict that these companies have to say – whether it’s their name, their mission, or anything else they want to deliver to consumers.

“There’s a truth to algorithms if you’re in a place where there are so many rules to the kinds of things you have to say,” Brew says. “Your writing is further beyond your control than it would be if you were writing a journal.”

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Bryan Lufkin is BBC Capital’s features writer. Follow him on Twitter @bryan_lufkin

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