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.
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.
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 analyses themselves, obviating the need to pay Bloomberg’s approximately $30,000-per-user annual subscription price, Edwards said a few have tried and found it’s harder than it looks. “You have to buy all those sources, do all the validation work, build benchmarks—and tokens aren’t cheap. Most customers are saying, ‘Awesome, Bloomberg, you do that. I’m going to focus on my [own trading strategies].’”
That’s not to say that AI can’t help. Edwards told me AI agents have dramatically accelerated how Bloomberg builds data sets. Data ingestion that used to take four-and-a-half months now takes two days, he says. That’s freed up the large teams once dedicated to data entry and cleaning, many of whom have been redeployed onto building internal evaluations.
Which brings us to the second big lesson: Building good internal evaluations is critical to deriving ROI from AI agents. “Evaluations, I cannot stress enough, are the make-or-break of building a useful, trustworthy system,” Edwards says, calling the emphasis on creating these evaluations one of the biggest “cultural shifts” Bloomberg has experienced in the past two years.
Building the evaluations isn’t easy—and it isn’t cheap. It requires close collaboration with domain specialists—in this case, bond covenant experts, equity analysts, market structure wonks, and even Bloomberg’s journalists—and engineering and product teams. Bloomberg was willing to pull these experts off their day jobs both to write benchmarks for sub-agents and to help evaluate entire workflows. Using AI models themselves as evaluators can work for easy cases, Edwards says. But for everything else, human assessors are required. Through building these evaluations, he says, Bloomberg is encoding its experts’ “tacit knowledge” in how its AI agents work.
Next, cost discipline is fundamental. And that means workflows need to be multi-model. AskB uses a mix of commercial frontier models and open-weight ones, as well as its own internal models, routing queries to the cheapest model that can handle a given task with the kind of reliability and performance that workflow demands, Edwards says.
Finally, the next frontier is proactive. When I asked what’s coming, Edwards’s answer was agent-to-agent workflows and always-on data monitoring. He wants Bloomberg to be “the eyes and ears” for its financial customers—watching the world against each client’s positions, mandate, and strategy, and surfacing not just the obvious things but second- and third-order effects. A flood takes out a factory making parts for a supplier to a company whose stock you’re long on; AskB, in Edwards’s vision, would flag the problem to you before you’d thought to ask.
Achieving that vision will be difficult. But this kind of proactive, always-on agent is where a lot of businesses want to go. Bloomberg is showing some key steps along the path.
Ok, with that, here’s this week’s AI news.
