Claude for Financial Services

The more and more AI projects I see both at work and online, the more convinced I'm that I should treat AI as an application interface, that's all.

It's a slightly different modality for the application. Nothing AI does wasn't possible before. You could always "create a price performance chart showing a stock's movement with key events annotated since May". You could also always buy dozens of software that will not just give you all the charts you could possible think of, but any one that you could even dream of. Check tradingview.com or koyfin.com for a taste of what a "free" offering can give you. Then imagine what the 100k software gives you.

The difference is the interface. You'll 100% need someone onboarding on their 100k custom trading platform. It might take you months to master it if you never saw one of these things before. Once you have learned it though, your productivity and velocity is expected to significantly increase.

Now with the AI interface, you don't need someone onboarding you or months to learn. You can ask the AI to "build a benchmarking analysis against Velocity's athletic footwear comps" instead of learning how to learning how to use the software to create such a thing. Maybe you never saw financial analysis software before, but you spent the last 20 years analysing financials by hand (in 2025 for some reason) and now you wanna onboard to a financial software. You don't need to "learn" anything. Just describe your thoughts to the AI and it figures the interface for you.

How transformative was that for you? I don't know. Maybe your financial analysis tool is as big of a piece of shit as Reactjs is and it's mind-numbingly tedious to generate such report. "It's just a 75 clicks that you have to do" and the AI interface saves you from doing that like it saves me from using React's shitty interface (text editor) to write garbage react components that are all just a copy of each other.

I've been thinking that for some time. Its a "looser way" to describe what you want as a different modality; a dynamic interface if you will. Even with code editors I've found its good to generate a lot of volume, but the detail still needs iteration or going back to direct instruction (i.e. code/clicking/etc). That applies to any artifact where iteration and validation is required to get it right. Instead of deterministic clicking and having to instruct every detail you can describe in "vague english" and the 80%/20% rule applies. Definitely an acceleration/leverage and a smaller learning curve.

In the BERT era of language models, it was normalized that to get the best performance for a task, you probably needed targeted post-training

As models got bigger and instruction following got better, everyone jumped on the general capabilities of the model + prompting

We're approaching wall that needs to be overcome with a completely new and unheard of breakthrough, otherwise we're going to have to go back to specialized post-training (which lends itself to vertical solutions)

I think people are seeing that now with stuff like Devstral being posttrained specifically for OpenHands and massively over-performing for its size at agentic coding

Why are both AI giants choosing to pay attention specifically to this space out of all other spaces they could choose to focus on?

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