Interesting to see the discourse around the GPT 5.2 Excel demo last night and this morning. It started with promotional hype, and ended with people having a laugh at the egregious calculation mistakes in the (now deleted) Uber DCF example that was posted. It’s a funny set of errors for sure, but the reality is that basic DCF mechanics, terminal value calculations, etc. have already been solved by other AI Excel agents - Shortcut for example already does these DCF calculations with correct formulas - and I expect this will be corrected by OpenAI as well.

Based on my reviews of both Shortcut and Ramp Sheets over the past week, I think the more nuanced and difficult hurdles to institutional adoption that Excel AI providers face relate to 1.) Historical data retrieval accuracy, 2.) Incorporating appropriate levels of granularity, and 3.) The ability to take existing models/templates and accurately replicate the formatting and general model structure/logic in a new model.

After some great discussions with several new subscribers, I wanted to share some constructive thoughts on the path forward on each of these issues and what I will be watching for in early 2026.

1.) The historical data retrieval problem and potential near-term solutions:

I’ve already pointed out data pulling accuracy issues in the reviews over the past week, so I won’t harp on them here. Let’s discuss potential solutions. Pulling bulk amounts of historical financials and reported KPIs with a high degree of accuracy is not a trivial issue to solve - so it is to be expected that newly launched Excel AI providers experience some difficulties there. Daloopa, for example, has spent 5+ years building algorithms/systems focused narrowly on this exact issue and, at least for public companies where reporting is more standardized, seem to have figured this out with 99% accuracy since 2021 (link). Daloopa has announced MCP integrations with Claude (link) and OpenAI (link) which provide enterprise customers with data connectivity to Daloopa if they are subscribers. So, assuming data pull accuracy using the MCP continues to improve (latest report on accuracy of Daloopa + Opus via MCP in Figure 1 below shows 94% accuracy), there may already be a near-term solution for the data retrieval accuracy problem for public equities at least. I hope we see providers like Shortcut offer these types of integrations to enterprise and retail customers as well. For the analysis of private companies, who have less standardized reporting and are not covered by providers like Daloopa, solving this issue is a much heavier lift and will have to wait for improvements in the tech stack.

2.) The “granularity requirement” in institutional use cases and potential near-term solutions: