The biggest challenge that I see potentially facing smaller Canadian competitors and innovators that want to rely upon or develop AI in this economy and in this context is the concern about what they can lawfully do and lawfully use, and then how they can implement and put to work the AI tools available to them.
To be honest, the incentives to use AI and the availability of good public systems mean that there is a growing capacity for everybody to take advantage of generative AI tools in particular and to find efficiencies that can benefit them. In this regard, I think education and access can be key.
To the extent that people want to be able to develop their own tools that maximize their capacities in their own sectors and for their own purposes, that's when you see both the need for technical supports and the accessibility of the data and tools becoming key. We could spend more time thinking about how we prop up and support the development of open-access and open-source models of data commons that are accessible to small movers and innovators that want to take advantage of that, rather than thinking about how large rights holders can block and prevent training on their data.
Rather than thinking about how we can exclude, we can think about how we include people in the data and how to ensure access to data and the technology it allows.