I want to address the particular problem that was mentioned about the protection of data, because that is a critical problem. What's often lost is the distinction between different kinds of models.
With the generative AI model, there seems to be a lot of push-back related to anybody owning that output. In fact, there are cases around the world that have said that outputs from generative AI models are not protected by copyright. Inventions that arise from generative AI models are likely also not protected. What that misses in that discussion is the difference between different kinds of models and what they do.
When you're talking about the data that's being collected in this manner, there is a much different public policy for protecting it. There was a case a number of years ago involving geophysical data and the protection of information that was harvested through seismic exploration. That was found to be protectable. There's no conceptual difference between protecting that seismic data that wasn't created using AI and protecting some data that's protected from deep learning models, which is what we're talking about, in order to ensure that it can be proprietary. Right now, the law is unclear in Canada, and it's very likely that if that got tested to the extent that the overwhelming thing that shaped the data was the AI, it would not be protected.
There are things that can be done to create a sui generis right, similar to what the European Union did with respect to databases. There are ways to do it, and that issue is really critical when you have investments like this.