GoKawiilFor its famous intractability, the Bloomberg Terminal has long inspired devotion, bordering on obsession. Among traders, the ability to chart a path through the software’s dizzying scrolls of numbers and text to isolate far-flung information is the mark of a seasoned professional.
But as a greater mass of data is fed into the Terminal—not only earnings and asset prices, but weather forecasts, shipping logs, factory locations, consumer spending patterns, private loans, and so on—valuable information is being lost. “It has become more and more untenable,” says Shawn Edwards, chief technology officer at Bloomberg. “You miss things, or it takes too long.”
To try to remedy the problem, Bloomberg is testing a chatbot-style interface for the Terminal, ASKB (pronounced ask-bee), built atop a basket of different language models. The broad idea is to help finance professionals to condense labor-intensive tasks, and make it possible to test abstract investment theses against the data through natural language prompts.
As of publication, the ASKB beta is open to roughly a third of the software’s 375,000 users; Bloomberg has not specified a date for a full release.
WIRED spoke with Edwards at Bloomberg’s palatial London headquarters in early April. We discussed the impetus for revamping the Terminal, whether traditionalists might balk at the change, and Bloomberg’s attempts to iron out hallucinations.
The following conversation has been edited for length and clarity.
WIRED: Shawn, tell me about the rationale for this overhaul of the Terminal.
Shawn Edwards: For years, Bloomberg has kept adding to this comprehensive dataset that we have. Often, finding the right piece of data in the sea of information is the deciding factor in whether you’re successful or not. It has become more and more untenable: You miss things, or it takes too long.
The primary problem we’re solving with generative AI is helping users to find key insights and synthesize a view of the world around a particular idea.
The concept is that untapped alpha lurks somewhere in the data, and ASKB will help to surface it?
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