What's driving the energy industry? Itron CEO Tom Dietrich talks AI, smart grids at DTECH

Well, Paul, first, thank you for having me. I really appreciate the time and you make me blush. So, you're very charitable with your comments, but what I tried to lay out in the keynote was really frame up in very tangible terms what's going on across the industry. Grid performance has never been more critical. Whether you're talking about economics or whether you're talking about safety or whether you're talking about just community involvement, it's never been more important that we need to do something to improve the performance of the grid from a resiliency, reliability, and affordability point of view. And let's not forget about safety, which is paramount in the utility industry. So grid transformation as a topic is not a slogan that we can adopt anymore. It's something that we have to take on and something that we have to do. I think from an implementation point of view, how do you do that? The foundation of it is data. So how can we put together and use all of the data that's in our hands today to enable that transformation of the grid. We have our hands on so many different types of data that we can put to use. And I think the key in all of this is making sure that the assets that we put in the ground are reusable in multiple different ways. We have to be able to move at the speed of software, if you will, to be able to get the best out of the performance of the grid itself. So, to the extent you can transform assets you put in the field from the old-fashioned flip phone to the smartphone and download applications into it, you're already further ahead because you didn't have to roll the truck to be able to solve the problem. So, if we're looking for proactive maintenance issues, hey, this transformer is starting to go bad, now we have a way to do that because you can see it in the data. That's really the vision that we have as we started on this vision several years ago by bringing forward this idea of grid edge intelligence. So distributed intelligence, this notion of downloadable applications. Thus far we have 16 million of those in the ground, 10 million more in backlog. You know, crunch all you want. We'll keep making more to make sure that we can scale out this vision for our customers. That was the essence of the speech and I was fortunate to have three more customers come on board and give sort of, I'll call them loose testimonials, but ways that they're using data to solve real world problems. Everything from wildfire prevention to making sure that we're leveraging the new tools that we have on the grid in the most effective ways.

Right. So I very much meant what I said. It's not the future because it is here today. So, the winter storm that just sort of roared across the US and created ice and snow through much of the southern half of the US. You can find some great clips on CNBC with utility CEOs talking about how they used AI models to understand the track of the storm, understand the base of their assets, and preposition crews to put the system back together again right after the ice and the snow had passed. That's not a futuristic thing. That's something that happened this past week. But using the models, it was an Itron AI model where you can say, okay, here's the state of my assets. Here's where the storm track is supposed to be. I can recruit crews from all across the US and get them in position so that we can put the system back together again after a huge event. That's not just one case. That was 10 utilities across the US. There's one example. There was a press release that came out from DTEK this morning which was San Diego Gas and Electric, a company called Tmetus and Itron using it to look for anomalies in the system using AI technology to really pinpoint where there could be things that are going on that eventually could lead to a wildfire. So, it's not wildfire detection, which of course is a noble endeavor, but this is let's get ahead of it and detect something before it happens. Another release just from this week was a collaboration between Microsoft and Itron to bring the power of large language models to bring the capability of frontier models to bear on the use of utility data. So you can speak to a computer in natural language and get it to generate really insightful reports back out of your meter data management data. So there's three examples that are happening right here and right now. Above all of that though, the point that I hope came through in the keynote yesterday is the thing that we need to do as an industry and not violate is trust. The trust in what we do because it is at the base of the pyramid for economics and for safety. We've got to make sure that we have the use of AI which is done in a way where everything we do is traceable. Everything we do is explainable and when we do that we can build trust and trust is what will be speedy in building the speed that we need to accelerate the use of more and more technology in the grid.

Yeah. Well, I would say it's not an easy job, right? You've got to, you said it well there. You've got to balance the needs of society and the mandate of utilities. Nobody wants to go to the switch on the wall and not have the lights come on when you flip the switch. So the need to be, you know, risk adverse, if you will, in terms of how you approach things, it's the mandate. It's what we as consumers would want. So we've got to respect that and make sure we work with it. How do we take new technologies and bring it into the grid? I think we've got to start with things which are more on the probabilistic side and use AI as a decision partner, not as a decision maker. So AI is not the authority. The smartest thing in the grid is the human and it will be that way for quite some time. But let's augment what we do here and find ways we can do that to use that power to synthesize data much, much faster. And that's what I think you see utilities doing across the industry today. And I think it's really, really fascinating and just a great place to be.