The Exchanges

Q This is like walk us through, like how it all came about. You started the first company as a student or right after your PhD?

A That's correct. Um, actually during our PhD. So my co-founder Donna and I, we met at MIT as PhD students, uh, over 20 years ago. I, I now cap it because we're just old. Um, but it was, it was a fun sort of research project that we worked on, which was, um, this was around the time that Wi-Fi was emerging as a new technology. We built a, uh, research project called RoofNet. So we covered essentially the city of Cambridge, the area between MIT and Harvard with free Wi-Fi in the early 2000. So that was really exciting. It's like a hands-on kind of very practical research project. We did a bunch of academic research on routing protocols and, you know, how to build the network. Um, but the first company Meraki came out of that project, which was We thought it was tremendously cool, this idea that Wi-Fi could connect so many people, just incredibly useful. We wanted to help other people build big networks. And so we essentially took that research, um, and, and now I would use the word distilled, like we condensed it down to, uh, you know, run in a box that other people could build networks out of. And then we started essentially making that product available. So that was Meraki. Um, to be honest, we kind of thought of it as a project. Like we weren't even thinking of it as a company. Uh, we kind of bootstrapped the business in Boston. We ended up moving to California. Um, and it was …

AI assessment note: “That's correct. Um, actually during our PhD. So my co-founder Donna and I”

Q And then what, what has, um, uh, generative AI fundamentally changed for, for you guys? Um, is that video reasoning? What, what, what do you use for, for what?

A We use generative in a few different ways. I would say if we think about the overall class of models, yes, you can now reason about video. So, uh, what would have required a human in the loop reviewer, um, or, you know, and that's the kind of work that might've been done overseas in lower cost geos or something like that. You can now do at much more volume, um, in the cloud using these models. So, uh, for example, if someone slams on the brakes, right, while they're driving their truck, um, the, the naive thing to assume is like, hey, the driver was distracted and they, they, they kind of woke up. The more nuanced thing is that driver might have been avoiding a deer or a dog or, you know, some kind of, uh, defensive event. If you can watch that as a video clip, you can now say, hey, we're actually going to give the driver some positive feedback because they did a really good thing. The VLMs are able to effectively do what I just said, right? Um, similarly, like, if you want to understand, did someone run a red light, right? These things happen. You need to have a pretty sophisticated model that understands the geometry of the road and all the conditions and so on. So that would be like a JEPA-style model, for example. So we're able to use a few different model families. On the generative side of, like, actually being able to create Video. That's also very interesting because fr…

AI assessment note: “We use generative in a few different ways... you can now reason about video.”

Q And, um, maybe walk us through how AI Changes that whole discussion. So, uh, you know, there was industrial automation, obviously that's been going on for decades and perhaps centuries. Then there was a whole wave of IOT and, uh, famously your, your ticker as a public company is, is IOT. And now there's AI, um, how, how different is the current moment?

A Yeah. Well, it's interesting. You mentioned centuries because that is like the right timescale to think about physical infrastructure. All the way back to, like, the Roman era, like, there's pretty significant infrastructure out there. So many of these processes of, like, how do you maintain a roadway have been in place for a very, very long time. A lot of that process was manual, right? Like, let's go inspect the condition of the road. Let's understand when it was last worked on. You know, let, let's kind of dig it up and see what we find. If you think about the ability to digitize that and then use sensor data, it's a huge unlock. So the question becomes, how do you get the data? Then how do you process it? And then how do you come up with a meaningful insight or really an action? Like what should we do about it? Uh, and that's, that's I think now possible. The last call it two decades was around reporting. Like how do we ingest the data and give you a really cool like table so you can look at it and reason about it, figure it out. Now what's awesome really in the last two, three years is the AIs are able to reason about this kind of information. They can look for other context clues and then give you the insight. And now we're actually seeing agentic AI, of course, which is It can take an action for you can maybe schedule the work to be done or, or start performing some of t…

AI assessment note: “Now what's awesome really in the last two, three years is the AIs are able to reason”

Q As a thought, uh, so selling to a bunch of different personas, especially in like more traditional industries, uh, especially as you've added this AI layer recently, how do you go about it? Like, how do you convince people in You know, typically non technology industry to choose to buy.

A Well, you know, I think the, the great part about this technology is very tangible. And, um, it's the kind of thing that when you see it, you get it very quickly. So what we do is we tend to go on site, we will demo the technology and do trials. So you can easily, these are plug and play. So you can easily try it out in your environment, in your industry. And, um, like I said, there's so many challenges in physical operations. It's tends to never just be one thing. So Yes, we want to reduce the number of accidents we get into, but I think we're also, like, leaving our trucks idling a lot because it's just a bad habit, or we're leaving tools behind at the job site, and we'd like to get those back because we spend millions of dollars replacing them. So we will often find multiple challenges like that, and then we demonstrate to them at small scale, like maybe a team or, you know, a region or something like that, that this works, and when they see it, they get it immediately. These are people who are experts in their industry, so they would say, I immediately see the value or the ROI, but they have to see it in that kind of tangible way. They're not just buying it because it's AI or big data or something like that. They're like, no, if this solves problems for us in our construction business, great, let's do it.

AI assessment note: “we tend to go on site, we will demo the technology and do trials.”

Q Do you think, uh, automated, uh, you know, self-driving trucking is, Just around the corner. It seems to be around the corner for cars. I mean, obviously, it's already happening with Waymo's, uh, and, and Tesla's now an automated pilot. In three years, are we at 10%, uh, self-driving trucks? Are we at 75%? What's your gut?

A I think on the robo-taxi side, it's going to happen a bit faster because the operations are much more regional. Um, they're much more, um, similar. Right? Like the way that you and I ride in a taxi across town is going to be quite similar. And so that's, I think, where you're going to see the biggest sort of, like, visible impact of autonomous vehicles. Um, on the trucking side, it's also important to realize, like, there's long haul trucking and kind of, like, moving stuff from point A to point B. That tends to be a minority fraction of what the commercial vehicles on the road are doing. Most of the commercial vehicles are, um, in, like, industries like field service, right? So they're HVAC technicians or plumbers, electricians. So people performing some work. Um, and they're also, uh, they're either doing something like that or they're in industries like construction where they're building the road. That tends to be where current day sort of like autonomy doesn't work so well. It's like the really messy long tail. So for that reason, we think the adoption might be a bit slower, but it's not like a no, it's just, it might take 1020 years. And these are industries where, again, the equipment's highly specialized. Like if you look at cement mixers or You know, garbage trucks, like these are custom built. So for, um, for the autonomy systems to make their way out to that edge, it…

AI assessment note: “we think the adoption might be a bit slower... it might take 1020 years.”

Q What do you think agents are not able to do just yet?

A Oh boy, that, that ceiling question, it changes like, you know, I feel like every week, um, and there's, there's some nuance to this. So for example, these new models, like the kind of Fable five class and, and GPT soul, it's hard to figure out where the practical ceiling is, but sometimes you do see them go And get, um, distracted or, like, lost in a loop somewhere, right? So I think there is some aspect of, like, they may find the answer eventually, but can they find the answer in, um, 10 seconds or one minute or even one hour, right? So that's one area where I think there is still a practical ceiling, and my guess is as these models become more and more powerful, more sophisticated, that will shrink, and then these algorithms are getting more efficient. So maybe the compute combined with the algorithm combined with just, like, Smarter model architectures will make what would have been like a one day task, a one hour task, or, you know, even faster than that.

AI assessment note: “sometimes you do see them go And get, um, distracted or, like, lost in a loop”