Bloomberg Terminal Adds AI Features, Ready or Not

For decades, the Bloomberg Terminal has commanded an almost cult-like loyalty among finance professionals. Navigating its labyrinthine screens of dense numbers and text to unearth obscure data has long been the hallmark of a true expert. But as the Terminal absorbs an ever-expanding ocean of information,from corporate earnings and asset prices to weather patterns, shipping logs, and consumer spending,the sheer volume has become a liability. “It has become more and more untenable,” admits Shawn Edwards, Bloomberg’s chief technology officer. “You miss things, or it takes too long.”

To tackle this overload, Bloomberg is rolling out a chatbot-style interface called ASKB (pronounced ask-bee), powered by a suite of different language models. The goal is straightforward: help finance professionals compress time-consuming tasks and test abstract investment ideas against real-world data using plain English prompts. Currently, the ASKB beta is available to roughly a third of the Terminal’s 375,000 users, though Bloomberg has yet to announce a full launch date.

WIRED sat down with Edwards at Bloomberg’s expansive London headquarters in early April. We explored the reasoning behind this major interface shift, whether die-hard traditionalists might resist the change, and how Bloomberg is working to eliminate hallucinations. This conversation has been condensed and edited.

WIRED: Shawn, what’s driving this overhaul of the Terminal?

Shawn Edwards: For years, we’ve been layering more and more data into our comprehensive dataset. Often, finding the exact piece of information in that sea is what determines success or failure. It’s become increasingly unmanageable,you either miss things or it takes far too long. The core challenge we’re solving with generative AI is helping users surface key insights and synthesize a clear view of the world around a specific idea.

So the idea is that untapped alpha is hidden in the data, and ASKB helps uncover it?

Exactly. Instead of asking for specific data points, the user can pose the high-level question,the thesis already in their head. For example, ‘How will the war in Iran and shifting oil prices affect my portfolio?’ That’s a massive, multi-dimensional question. Can we synthesize an answer in minutes?

If everyone can now navigate the data maze, what separates mediocre traders from the best ones?

These tools aren’t magical. They don’t turn an average employee into a star overnight. The differentiator remains your ideas. In the hands of an expert, ASKB enables deeper analysis and broader research,allowing them to sift through ten great ideas when they previously had time for only one. If you’re a mediocre analyst, you’ll just get ten mediocre ideas.

Bloomberg markets ASKB as a form of agentic AI, but it looks more like a chatbot than an automation tool. What makes it agentic?

Consider earnings season. As an analyst, I need to prepare for every earnings call,comparing a company’s price to peers, scanning documents, reviewing fundamentals. I barely sleep. With ASKB, I can create workflow templates. I write a long query: ‘Here’s all the data I’ll need. Give me a synopsis of the bull and bear cases, what the Street is saying, and the guidance.’ Then I can schedule those workflows or trigger them automatically when specific conditions arise in the market. That’s the agentic layer,automation at scale.