From Observer, July 10:
Shawn Edwards has spent decades building the infrastructure of Wall Street. Now he's trying to teach it to think—without letting it lie.
....MUCH MOREBloomberg’s chief technology officer has, by his own admission, a job that is half about building the future and half about stopping the future’s more embarrassing impulses from getting anywhere near production. “Half my job is keeping out nonsense from the company,” Shawn Edwards tells Observer, and he is not entirely joking. The nonsense he’s referring to is the tidal wave of generative-A.I. hype that has swept through every boardroom with a Bloomberg Terminal. His job is finding the narrow cross-section between what his engineers can dream up and what the people who actually trade bonds, screen credit and prep for earnings calls are desperately trying to get done—not what they’re doing today, but what they’re actually trying to achieve. In Edwards’ worldview, that distinction is the organizing principle behind ASKB, the conversational, agentic A.I. system Bloomberg has built directly into the Terminal.
Ask Edwards for a use case, and he describes the old way of doing things. “Before ASKB, a user would have to go to different places in the Bloomberg Terminal to look at company fundamentals, to look at what the street estimates are, to look at various performance measures, company KPIs, and a different place to look at print, peer analysis and alternative data for that quarter’s performance—reading lots of news about the company and invariably reading lots and lots of company documents and sell-side research.” Edwards pauses. “And then they would have to synthesize all this information.”
As of February, Bloomberg users can ask the system (ASKB) to synthesize information. “It knows where to fetch and knows where to find all this information and gives you a quite detailed analysis for you to be prepared.”
The machine does not replace the analyst, and Edwards is careful, almost insistent, on this point. “ASKB doesn’t do the entire job for the analyst, but it does 80 percent of the work of gathering and synthesizing this information. And it frees them up to do the value-added thinking—really deciding where they really dig in.” The workflows differ by desk. For equity analysts, it’s event preparation and thesis monitoring. For credit analysts, it’s liquidity analysis and bond screening. The common thread is a research problem.
Trust Isn’t a Feature...
