Proof of Work: AI Value Creation
00:32:06 · Yann Magnan, Stuart Willson
[00:32:06] S1: So uh I think we used to do that when we were uh back uh back a couple of years ago. Uh most of that uh issue was basically handled uh at uh the uh implementation point of the platform when we are implementing the platform. Well, basically, again, you you have to develop anyway that uh consistent data model across the board. Uh so one of the work that you do is working with the with the client, identifying all these types of issues, and so identifying the uh correct uh legal entities were uh one of this uh topic. What we're seeing happening is that actually that's typically the area where LLMs can help because they can make this type of connections. So you can accelerate that work now with with LLMs. Again, as long as you have put the data in the relevant place. So that's what we're seeing happening. [00:32:50] S0: Great. I'm gonna take two questions from the audience because they are relevant to this conversation. One is from Johnny. Uh he asks, given solid data infrastructure and a rag-based retrieval lever, how how sophisticated does the underlying LLM actually need to be? Is there a case for running lighter, more efficient models like smaller open source options, Gemma 4, or do the complexity of financial signals and edge cases demand frontier models?
00:32:50 · Stuart Willson, Yann Magnan
[00:32:50] S0: Great. I'm gonna take two questions from the audience because they are relevant to this conversation. One is from Johnny. Uh he asks, given solid data infrastructure and a rag-based retrieval lever, how how sophisticated does the underlying LLM actually need to be? Is there a case for running lighter, more efficient models like smaller open source options, Gemma 4, or do the complexity of financial signals and edge cases demand frontier models? [00:33:18] S1: I can give a give it a start, and then Joe, I'm sure you have uh you're you're you'll be more in the wheels on this one. But my view is that um as long again, getting back to I've said that maybe three, four times now, but uh as long as you get the data consistently structured, consistently labeled, standardized across the board, then that's a very, very strong uh starting point. And then there are lighter models, smaller models out there uh that can help you extract the first layer of intelligence at a lower cost, 100% certain. Um that being said, when you want to do something more sophisticated, you know, that's where you know you have uh you have to you have potentially have to uh to to get into the um into the LLMs.
00:34:04 · Joe Zein, Yann Magnan
[00:34:04] S2: Yeah, I completely agree with Yann here. I think there is easier you make it on the models, the more you can go, I don't want to say the on-market, but you can go on uh towards lighter models. That being said, if you're trying to run some crazy task that is that needs to scour the internet, even if you have amazing data, right, you won't have all of the world's information into that into your data model. And so this is where I think you know you would still need to, it really depends on the task at the end of the day, but there's definitely we've definitely been seeing people and interest about like, I know exactly what task is going to be running on this model. I don't need to have a sophisticated model that allows me to catch all sorts of edge cases. But the the the premise of going that way and using and optimizing how you you choose your models needs and requires uh uh a data foundation. [00:34:54] S1: Just uh an example real quick on that font on our side. So our ingestion technology actually is a combination of uh machine learning, NLP, and LLM type of algos. And if we would be using LLMs across the board, one, I'm not sure that we would get uh an accuracy as good as what we have, actually. I'm quite sure of the opposite. And second, the cost would be just crazy. So there are areas where PML and NLP algos that we have developed do a much better job at a much lower cost than bringing the LLMC.
00:34:54 · Yann Magnan, Joe Zein, Stuart Willson
[00:34:54] S1: Just uh an example real quick on that font on our side. So our ingestion technology actually is a combination of uh machine learning, NLP, and LLM type of algos. And if we would be using LLMs across the board, one, I'm not sure that we would get uh an accuracy as good as what we have, actually. I'm quite sure of the opposite. And second, the cost would be just crazy. So there are areas where PML and NLP algos that we have developed do a much better job at a much lower cost than bringing the LLMC. [00:35:25] S2: Yep, there's techniques that have been there to parse receipts and to do stuff like that, like OCR. That this is one of the nice things of like someone who's like 73 strings who started before that whole AI craze is that they had to go the hard way about doing the extraction. And then, you know, they they layered in LLMs, so but they have a foundation that is pretty deterministic compared to an LLM that allows them to do the extraction at a lower cost on and but and and have it more accurate too. [00:35:55] S0: Great. Another LLM question from the audience, a very common question I get from organizations of all sorts from Sean. What about the sensitivity around confidentiality? If we put the information in LLM, won't the LLM be able to use that information for queries from other companies?
00:36:13 · Joe Zein
[00:36:13] S2: Depends on on the plan you're on, right? It's also, you know, uh the enterprise plans cover for that, right? You you ChatGPT Enterprise and Claude, I think it's Team Plan Plus, well, you know, in the agreement is not using your data for training, for future training, and and obviously you're operating in an insulated environment. That being said, you're trusting those companies, sure, I think. And by the time they retrain and that data, if there's a leak, I mean, again, it's a it's it's a trust game, right? And it's very mission critical. Like like when you see someone or when you see the government going and using those models, you I think I I personally better believe that that that you know there's no cross-contamination across orics. But I'm a I know I'm I'm an optimist about that, about that stuff. I've seen people wanting to go full open source, um, because on on open source models, you're hosting the model on a machine that you own, so you know no one is going to train it. That data's not going anywhere and it's seeing within your network, within your infrastructure. Uh, so that's the answer for people who are extremely cautious about their data and don't trust the frontier model provider companies. Uh they decide to go with something you know open source that they host, but then there's also cost associated with that. There's uh maintenance associated with that. Uh, so it's you know, pros and cons.
00:37:31 · Yann Magnan
[00:37:31] S1: Yeah, I would uh answer something uh quite similar, and I think it depends a lot on uh what type of data you we're talking about, what strategy uh you uh are addressing as a private capital fund, maybe a credit fund doesn't have the same uh uh sensitivity on confidentiality data as a private equity fund that uh invests in uh defense uh assets, just to give an example. Uh so depending on what we do you do, you'll have a different sensitivity to that question. I I'm I'm an optimist as well, same as Joe, so I would uh I tend to believe that as long as you activate these enterprise uh licenses, uh you should be safe. That being said, in our platform, we can turn off and off the LLMs depending on whether the clients are agreeing to that or not. The last comment I would make here is that all of us have been uh using uh LLMs. We've seen um you know the uh improvements uh that they that that that have that that they have made in the past few quarters. I guess we can all guess that the what's coming ahead is going to be like pretty, pretty amazing. And the question as well that I that I asked to my clients before they make this decision, LLMs or not, is are you willing to potentially pass on that intelligence that these LLMs are going to provide you with because your data is too confidential? If you're and if the answer is yes, that's fine, fully understandable. If you think that your data is much is so important that you you'd prefer pass passing on the uh extra layer of intelligence and and keeping your data in a place where you're certain that it's safe, that's fine. That's a good answer. But you have to be you have to be conscious that you'll be passing on massive intelligence, and your competitors might not, by the way. And and you have to think about that.