Erik Engstrom: Back to GPT. I mean maybe I was not clear enough there. We see the GPT fundamentally as an opportunity to continue to enhance things that this is an underlying €“ the underlying technology, the large language model and the generative AI behind it. That’s something that we see as one step in technology development, something we’ve been on for a very, very long time. I mean some of you may recall, we’ve talked about AI and machine learning in some of our seminars many years ago and how we include this. We believe, as I said, that our ability to identify and leverage those ahead of others is a competitive advantage. And I believe that this is not a defensive tool for us. This is the main driver. Using technologies like this has been the main driver of the improvement in our growth rate and the increasing value-add in our analytics and our decision tools.
And I do not believe that this step is an exception to that or the future step should be an exception. We seek these partners out. We seek the individual developers out. There were smaller companies you talk to, even the ones that come from big companies. We often look to develop partnerships with them and do licenses and collaborations early here, and we’ve been on this for quite a while, and we’ve been experimenting with building things for quite a while. So, we believe that this is a tool that we’re particularly well-positioned to leverage in a positive, proactive way. When you ask, what can other people do, there will always be technologies that are useful. And if they’re useful to us, they will be useful to others. But as we’ve said many times before, the value that we bring in our decision tools is because the €“ it’s because we have the combination of three different components.
Number one, and the most important one, is we have extremely deep customer understanding of our key market segments and how exactly they behave today because their usage of our tools today were inside their processes. We know how they make decisions. We understand the value of those decisions, and we can build tools to enhance the value. That’s the underlying number one, customer understanding. Then on top of that, we have built these content sets, the data sets that we’ve accumulated over a very long period of time. And while some of those have been accumulated from public sources, many of them have been built through contributory databases or by internal editorial work over a long, long period of time. And those data sets, those content sets, they are not publicly available.
They will not be publicly available for these tools either. They are proprietary when we have built them, and they’re proprietary the way we put them together, even though many of the original data sets were publicly available at the time. And third, we build these tools with the layers of machine learning tools, technologies and analytics on top, of which, the GPT tools or similar types of artificial intelligence, that’s just one component. And when you buy something expensive from a professional business information provider, what’s important is that it’s truly something you can trust and rely on. And I think in order to do that, the value comes from the combination of those three components: the deep customer understanding, the broad and complete data sets, and the combination of many different analytical tools and algorithm on top of it.
