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Latent Space · 2 min · Oct 31, 2026
Finance training improves the shared model, not just one product
Joseph Kim says financial reasoning data goes into OpenAI's main training run and remains available to builders.
In this episode
Full episode- 4:10Bundled market data fixed connector failuresPro pick 3 min
- 7:00Most finance AI product work is buildable by startups2 min
- 22:45Financial AI evals must predict real job performance3 min
- 9:16Financial AI can fit into existing review hierarchies3 min
- 19:01Moving up the analyst stack makes evaluation harder3 min
- 15:45Pairwise human feedback can train models on subjective taste3 min
- 12:32Factual errors are more dangerous than weak visual judgment3 min
- 1:57Finance training improves the shared model, not just one product2 min
“all of the training data that we do for financial reasoning analysis, we do upstream into what we call the big run”
Takeaways
- Finance reasoning training feeds the main model.
- OpenAI aims to make those capabilities available to outside builders.
- Internal finance, legal and sales Codex adoption rose from near zero to 90%.
About this segment
Joseph Kim says financial reasoning data goes into OpenAI's main training run and remains available to builders. He also cites finance, legal and sales usage jumping after Codex's desktop app launched.
Joseph Kim is a leads ChatGPT for Financial Services at OpenAI.
Shawn Wang (swyx), Host · Co-founder, AI Engineer
Wrote “The Rise of the AI Engineer” and founded Smol AI, maker of AI News.
From “Building ChatGPT for Finance — Joseph Kim, OpenAI's Head of Financial Services”, Oct 31, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 2:01
So Codex started to become available to the masses. People started using it for finance, and now you're shipping your first finance product. What is financial reasoning data like? What goes into finance-specific models? Yeah, it's a great question. One thing I will say is that all of the training data that we do for financial reasoning analysis, we do upstream into what we call the big run, which is the main kind of run that becomes something like 6-Astra, 6-1-Astra, etc.
- 2:30
And so even though we're adding a bunch of financial data to the model, it's going into the main model, and so therefore it's available to folks that want to build on top of it. And that is something that's very important to us. At OpenAI, we really do want that intelligence to be accessible, not only to our products that we're building in-house in first-party ways, but also for folks that want to build startups, build businesses on top of that, as well as financial firms that want to integrate that directly into their companies.
- 2:53
And so we will make all that intelligence accessible there. I think you touched a little bit on product and research, and I think that's really core to what we do at OpenAI. Internally, we call ourselves a research and deployment company. And we kind of structure that in order on purpose, like research comes first, and then deployment comes second. And so as we think about, you know, at the end of the day, trying to change the way that people do knowledge work, we kind of work in that order, right? We think about the model capabilities, where they're headed, what kind of new things can they enable? So that's one.
- 3:23
And then two, we think very deeply about the product features and surface areas that we should build to make that model intelligence accessible to more people. And I think you alluded to a slide in my keynote where we had this chart of codex adoption within opening eye. And it's funny because you can see the software engineering adoption curve and it's just like up and to the right, pretty steady.
- 3:44
People are picking it up and incorporating into their daily routines. But if you look at finance, legal and sales, it's pretty much at zero and suddenly it kind of jumps up to 90%. And if you look at that line and try to see what happened around the time where it jumped up, we had released codex desktop app in February. And that was when that adoption jumped up. And so I do think it kind of shows the importance of co-developing those things.
- 4:07
And the purpose of the product is to make that model intelligence accessible to as many users as possible.