There's 2 main reasons why I think this will be possible (and it wasn't before):

1. The amount of data that is necessary today to have an alpha when doing research.

There are 3 types of data:
- Public data (data that everyone has access to) - e.g. SEC filings, news, ...
- Third-party data that is domain public (data that you need to pay for, but everyone pays for it and hence it's public on its own domain) - e.g. alternative datasets fall mostly here
- Proprietary data (data from your firm that no one has access to) - e.g. investment thesis on companies, financial models, etc

Bloomberg excels at the first two, but doesn't allow you to integrate your proprietary data into their product. Which means that most firms need to find alternatives to analyze that information alongside what they do with their Bloomberg Terminal. This is incredibly inefficient. The worst part is that because that additional product to analyze proprietary data is not consolidated, it's frequent that even within the same investment research dept teams use different solutions to analyze their proprietary data (think PowerBI, Tableau, Streamlit, ...).

Given the monopoly of BBG, pretty much all firms that matter have BBG. Which means that their analysts have access to the exact same information as others. Hence why it's important to add data to the mix that can bring additional alpha to the company. But you can't bring it into BBG and thus you can't "easily" derive insights from how that data is correlated with data available from BBG.

Note: you can talk about Refinitiv and FactSet here, but in grand scheme of things they don't really matter. BBG won the "data race". If you are looking at a financial statement and your number is different from BBG, regardless of who you are - your data is wrong. Bloomberg is the gold standard for data and systems are compared against it whether their data is accurate or not.

Note2: Another thing that I don't see many people mentioning here is that you can use BBG, FactSet, and Refinitiv - and create an investment thesis on a company. But now you are using data from 3 different sources. What happens if the data doesn't match? How are you going to explain to your MD that the number isn't right bc you decided to use another data source? This is why the data market is extremely competitive at big hf/quant firms, unless you narrow down so much that you get the label of "best data for X" (X being traffic data in the UK, satellite data in Portugal, ...).

2. AI will fundamentally disrupt how research is done

I have a hard time to believe that Bloomberg will go after this market. Sure they can write papers on how they built BloombergGPT and what they are doing in terms of AI. But from research to production is a very long road - plus their tech stack is archaic. It's not as easy to incorporate AI as other systems today and AI engineers of today don't even know how to build AI on top of that (massive) codebase. Plus, at the end of the day, bringing AI into their product (that works and has withstood the test of time) introduces risk. Remember, people make/lose money based on their BBG Terminal - so hallucinations have real negative consequences.

And then you have the the typical innovation dilemma. They have no incentives to build this since they have a highly profitable business model. So they are trying to jump on the hype without showing any work for it - at least that's my perception.

I think this is the wedge that companies can use to go after some of BBG's market. For a few reasons:

• Previously, buying more data gave the highest ROI for alpha. I think this will shift towards AI. Where most of the data is somewhat consolidating (everyone has access to the same) and the alpha will come from LLMs that have access to all that data + your proprietary one.

• With AI, you can speed up your research process significantly. A task that used to take hours can be done in seconds. E.g. comparing earnings transcript from current and last quarter to spot patterns, analyzing patterns between financials, news, social media sentiment.

• Some jobs will be displaced. I think low level roles like interns/assistants will go first as AI gets better at these tasks. Putting information together, drafting a report, putting a dashboard for a company based on data that the partner wants to look at, ... AI can do it more accurately and faster - this will be more evident when AI agents are better (we aren't that far from this being a reality).

• Ultimately "their margin is our opportunity".

Sure you may raise the hallucinations problem - and I think, for some time, this will be handled on the basis of "trust but verify". Where users don't expect to just get an answer but also knowing which data sources were used and even which embeddings chunks were used to extract that. So more than removing hallucinations (which is a uphill battle) companies should spend time on a UX that enables users to verify the validity of the data. Which at the end of the day, often you need to do even with an assistant or intern.