Fusion power 🪫 - Canada’s General Fusion goes public, Ticks aren’t just spreading Lyme disease

Canada’s General Fusion is now the first publicly traded nuclear fusion company, as its Nasdaq debut gives it more cash to chase a breakthrough that could one day help meet soaring demand for clean power — if the still-unproven tech can finally produce more energy than it uses. Plus, doctors are warning about a tick-borne illness that was barely on their radar five years ago, as warming temperatures help ticks spread farther north and bring more diseases with them.

And in The Big Picture: Ottawa and Alberta strike a deal with oil sands producers on a major carbon capture project, an Innu First Nation rejects Quebec’s proposed hydro deal, and Chinese automaker Dongfeng gets ready to sell EVs in Canada.

After today's interview we host an exclusive interview with Sumee Seetharaman, VP of AI/ML Practice in the Center of Excellence at TD:

Brett:

Hey, Peak Pals. Thanks so much for joining us today. We've got a great guest for you, Sumee Seetharaman, who is the VP of AI/ML Practice in the Center of Excellence at TD. 

This is gonna be a great conversation about AI at work, something I think we're all experiencing, so we're really excited to have you. To kick it off, the use of AI in Canadian workspaces is surging. According to KPMG, about 51% of Canadian adults are now using generative AI at work. Has the technology become just so commonplace in the workplace that Canadians need to have AI skills before they enter the workforce? And if so, which skills do they need? 

Sumee:

Yeah, it's a great question. Interestingly, we're at a point where the personal use of AI is far exceeding use at work, a March 2026 Angus Reid survey found that over 70% of Canadians surveyed had used AI in the previous three months for personal use. So odds are that most folks are actively using AI for their day-to-day already and have some of the basic AI skills required at work. Skills like knowing how to use and prompt a chatbot effectively, and I'm not talking about just a single Q&A, but really knowing how to converse with the model. Treating, the response to your initial question as just the starting point. Knowing how to ask follow-up questions and providing clear instructions to refine the response. So the fluency is in really knowing how to iterate and refine, building out the parts that are missing, and pruning out the parts that don't feel right? Most people are learning to do this instinctively as they're using ChatGPT and Gemini apps. 

Next, I want to say folks who are using AI regularly are also starting to develop a good sense of the strengths and the limitations of the technology, right? All of us have encountered that the model sometimes hallucinates, and so we know and we've learned that we cannot treat the output of the model as gospel. And so in a work setting, this is where domain expertise, not AI expertise, is actually more helpful because we need experts to validate and apply human judgment on the output of the model. And also if you're actively using AI and reading on AI, AI can be used in harmful ways, so most folks are starting to develop an appreciation for the guardrails that are needed and what does responsible use of AI look like. And are careful about what you put into public versions of these models. You're starting to build a good awareness of privacy safeguards and such. All that to say if you're familiar with using ChatGPT or Gemini for personal use- You should be able to fluently use most AI tools in the workplace. So at TD, over seventy percent of our colleagues our workforce has access to a GenAI capability, whether it's virtual assistants that we have developed in-house for our frontline colleagues in contact centers or wealth operations or branches, or Copilot capabilities we've enabled across the organization. GitHub Copilot for developers and Power BI Copilot and Office 365 Copilot. So chances are, if you're joining TD, you have access to these AI capabilities to, to really amplify your impact, and you probably are already quite fluent in how to use them. 

Brett:

TD is obviously ahead of the curve on AI, and I'd be interested from a practical standpoint, what are some examples of jobs at TD that AI is already transforming? 

Sumee:

Yeah. So we recently announced our first agentic AI use case to, to speed up the pre-adjudication process of mortgage and HELOC applications. So when an application is submitted, AI agents that we've deployed are actively scanning the documents, calculating client income, performing consent checks, looking for discrepancies, et cetera. After this sort of document review process is done, the agents then produce a memo with their findings and this goes off to the underwriters to review and make a decision on the application. And so if you look through that agentic AI activation, it's transformed the jobs of colleagues like our underwriters who are now working hand-in-hand with AI agents and making decisions on applications much faster. We've gotten the review process down to an average of three minutes from an average of fifteen hours. Effectively, the way they're operating has transformed day-to-day. If I look at the role of our back-office staff that has transformed as well. They're now providing valuable oversight on our AI agents and helping us ensure that it's performing within the necessary parameters and it's delivering the right outputs. It's just the first set of use cases supporting the first pre-adjudication leg, but there's a much broader end-to-end transformation roadmap for our real estate secure lending operations. So we're just getting started. And as we gradually scale AI use within RESL operations, we'll start to see more role transformation across the board. On a similar vein I find the roles of our frontline colleagues are gradually evolving, transforming too. So our colleague