Bloomberg, the OG of financial data firms, has a potent new AI agent. How it built it holds lessons for other companies

Hello and welcome to Eye on AI. In this edition…China blocks Meta’s purchase of Manus…OpenAI falls short of its revenue and growth targets…Anthropic shows AI models can help advance AI safety research…Sen. Bernie Sanders’s decision to invite Chinese AI experts to a Capitol Hill panel provokes China hawks’ ire.

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In their battle for enterprise sales, both OpenAI and Anthropic have been targeting financial services firms. That’s not surprising. As that old joke about why criminals rob banks says: It’s where the money is. OpenAI supposedly has a battalion of ex-investment analysts helping to build a yet-to-be-launched agentic AI financial analysis product. Anthropic has been rolling out financial modeling skills for its Claude Code, Cowork, and Claude for Finance products. Startup Samaya AI is building AI tools for the finance sector too. And there are plenty of new financial advisory tools using AI as well, as my colleague Jeff John Roberts has covered in this informative recent feature.

The OG of specialized financial data and analysis tools, of course, is Bloomberg. Access to the company’s “terminal,” as it calls its core product (even though its data is no longer delivered through a dedicated machine), is still considered the de rigueur tool of every trader, investment banker, and hedge fund quant.

Bloomberg’s tools have seen off lots of rivals since its founding back in 1981. But today, AI is supercharging the competitive pressure on the company, as rivals embrace AI-powered features and use AI models to rapidly ingest and analyze complex data sets, from bond prices to earning transcripts to social media feeds to satellite imagery, that once only Bloomberg consolidated in a single place—and as Bloomberg’s customers can increasingly use AI to perform the kinds of modeling they once needed the terminal to do.

For decades, getting the most out of the terminal required that traders memorized an arcane and bewildering set of three- and four-letter keyboard commands and shortcuts, each of which called up a different feature, function, or dataset. When I worked as a reporter at Bloomberg News, all new hires underwent a full week of training to introduce them to just a fraction of these functions, the bare minimum we would need to access the data and tools required for our jobs.

Even before I left the company to come to Fortune in 2019, Bloomberg had begun to use machine learning and large language models to make accessing these features far more intuitive, as well as to power new kinds of data analysis. And those efforts have only accelerated, especially since the debut of generative AI chatbots in 2022 and recent advances in agentic AI.

I have periodically written about Bloomberg’s progress on AI here at Fortune. But I was still surprised and impressed when I attended a recent “AI in Finance Summit” at the company’s London offices where it was showing off its new “AskB” feature, which the company bills as the biggest rethink of the terminal in Bloomberg’s history. AskB allows users to use natural language to navigate the terminal’s features and functions, but it does far more than this. The system acts as an agent, building investment screens and producing full research reports, including sophisticated financial modeling and bull and bear cases for a particular stocks, on the fly.

AskB, which uses a variety of AI models under the hood, including some built by Bloomberg itself and others from frontier AI model companies such as Anthropic, shows that Bloomberg is taking the potential threat from AI-native startups seriously. I sat down with Shawn Edwards, Bloomberg’s chief technology officer, to ask him more about how Bloomberg built AskB. Much of what he said holds lessons for enterprises in any industry that are trying to get agentic AI to deliver real business value.

Data is the differentiator

The first lesson is that data remains the critical differentiator. AskB pulls from Bloomberg News, sell-side research from over 800 providers, market data, and, increasingly, so-called “alternative datasets” that are hard or expensive to source. This includes things like anonymized credit card transactions, foot traffic in retail locations taken from cellphone pings, satellite imagery of parking lots, and app usage data. A lot of this data is not Bloomberg’s exclusively—it is buying it from other sources. But having it all in one place allows the AskB agent to do some powerful things, Edwards tells me, such as aligning this data with the business segments a public company reports in order to “nowcast” a company’s quarterly KPIs. Edwards relates that before Sweetgreen’s fourth-quarter 2025 earnings call, the alternative data was screaming that the chain would miss analysts’ consensus earnings forecasts—which it ultimately did. It’s an example of the power of pulling all this data together in one place.

When I asked whether customers could just use AI models to ingest this data and run these anal