Data Centers: Site Selection to Approval

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Hello, and welcome to another episode of The Market Edge. This podcast is produced
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by the Market Intelligence Committee at the American Council of Engineering
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Companies, where we bring you conversations with industry experts on the issues
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shaping our firms and the markets we serve.
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This series is presented in partnership with the Engineering Influence Podcast at ECEC National.
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I'm Diana O'Leary, Vice President of Market Intelligence, and I will be your host.
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Today, we're joined by Navdeep Martin, co-founder and CEO of FlyPower,
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an AI-powered platform that helps developers protect millions in project investment
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by answering two critical questions.
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Should I build here? And what is my path to approval?
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FlyPower analyzes social, political, and permitting risks to develop,
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give development teams the intelligence they need to chart a strategic path to approval.
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So, Navdeep, thank you so much for joining us on the Market Edge today.
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Thank you. Thanks for having me. Absolutely. So let's jump in.
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I'm just going to give a little bit of background. So communities across the
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country are grappling with the growth of data centers because of the AI expansion
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and other large scale infrastructure projects, frankly.
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Engineering firms are finding themselves at the intersection of not only technical
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design, but now public perception and regulatory approval.
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And in many cases, project success depends, you know, not just on,
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you know, understanding on social and political dynamics.
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It's also on engineering solutions as well. So today we're going to discuss
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how firms can better anticipate those challenges and position projects for success.
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So I'm going to go ahead and jump right in with my first question.
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I kind of wanted to get your origin story.
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So what specific challenges in the development or infrastructure space led you
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to focus on permitting and approval risk? And when did you realize this was
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a broader industry gap rather than a project-specific issue?
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Yeah, no. So I actually have an AI background, was doing it for over 13 years.
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And in, I would say, 2024, I was out to start my own company.
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I was ready to start FlyPower.
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And at that time, ChatGPT was suddenly everywhere. And, you know,
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there was an excitement in the air.
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And having, you know, background in that technology, I felt like I went from
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that kind of nerdy, you know, person with glasses and braces and unfashionable clothes,
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you know, person in the 80s movies, right, to suddenly I was the popular kid
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and I could, you know, do whatever I wanted with this skill set.
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And it was a really exciting place to be.
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And so at that point, I picked renewable energy to start with.
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I soon moved to data centers. And,
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the pattern I was seeing was really that projects were getting shut down.
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Maybe they had sound engineering and the economics worked out,
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but they were still dying. And it was due to community resistance.
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And so we set out to fill that gap. I realized there wasn't anything in the
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market technology-wise. to be able to deal with that.
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And so that is, that's how we got started and where we are today.
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So I don't know if you've seen the latest movies, even though I love 80s music
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and movies, but smart kids are now the cool kids.
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Yeah. Yeah. So yeah, it was time anyway. So you've made it.
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So you often frame, you know, the challenge as answering, should I build here?
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And what's my path to approval?
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How should engineers and project teams think about structuring those decisions
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more effectively today.
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Yeah, I mean, I think in the old world, you found a location and you did your,
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environmental study. You know, are there endangered species?
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You know, am I on wetlands?
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And then you did your interconnection study. Like, how am I going to connect to the grid?
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And in this new world, this new paradigm, I would argue that community resistance
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is actually that third study you should be doing, right?
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The stats are staggering here. You know, Berkeley Lab analyzed one in three
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utility skill and solar and wind projects end up getting canceled.
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Over half experienced significant delays. Data center side, there's roughly
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$18 billion in data center projects blocked and over $46 billion delayed,
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right, over the past two years, all due to local opposition, right?
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So social risk now needs to be at the top, right?
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When you're analyzing your permitting, your environmental and technical due
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diligence, you also need to understand, am I going to be allowed to build here?
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And that's what we set out to build. So we have our two reports,
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the should I build here a fast, easy, go, no-go report, and then a full path
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due diligence report where we are analyzing your path to approval.
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So everything from the commissioners to past projects, what are the community
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expectations, who are the silent, say, gatekeepers that can quietly stop you.
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So that is how we approach that problem. But overall, the general kind of structural
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risk for engineering teams is now, will they let us? in parallel with can we build it.
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So in a world filled with everything you just listed, the social,
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political, and regulatory complexities, if it wasn't difficult enough to get a,
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project planned, permitted, and built, what does it take to translate those
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signals into actionable insights, and where do traditional approaches tend to fall short?
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Yeah, so we approach it with a framework. So we're classifying governing bodies,
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how they make decisions.
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Do they defer to staff or do they mirror, you know, the loudest voices in the room?
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And then we look at opposition as well. You know, it's not enough to just know
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that, hey, there's people kind of chattering online.
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You know, yes, there's probably opposition everywhere where people are complaining
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about things online. But more importantly is, have they managed to successfully
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shut down projects in the past?
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And so we're looking at the governance, the opposition, and then we interpret that.
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We're not just aggregating like a whole, you know, data set and kind of good luck with that.
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But we're actually giving you strategic insights to then take these thousands
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of data points and help you
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with this hyper-local grounding of like, okay, it's not enough to know that,
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hey, this county survey or the state average says X, but, you know, tell me about my
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specific site location, like down to the coordinates, right?
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Because the county is huge.
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You're going to have, you can think of all these different places I've lived,
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right? It's very different from the north versus the south or just one end to the next.
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So really understanding at a local level. And that's where I think developers
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can, And if they can begin to put forward this framework, they can then pivot
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where traditional approaches fail.
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Great answer. So I know your background was initially in AI,
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and it's transforming so many industries today.
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So where do you see it adding the most value in infrastructure planning and
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permitting, particularly in identifying risks teams might otherwise miss?
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Yeah, I mean, I think that it's now transformed how you look at projects, right?
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So the old way might be, hey, I'm building a data center and,
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oh, this location has a bunch of data centers that they must,
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you know, this looks like a good spot, right? You know, they've approved these data centers.
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And what you're missing out of that is, first, there might be possible development
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fatigue, right? A sense of, we've done our part, you know, we don't want any more of these.
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But next, you're missing like what happened there before.
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So we've seen situations where maybe the last developer left the roads and,
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you know, terrible conditions.
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And now you're showing up, you know, trying to alter those same roads or even
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just other technology projects.
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So you're building a data center, but everyone was also complaining about a
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local Aldi grocery store, or maybe there's a recent fire.
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So the point is being able to understand a location, again, back to this kind
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of hyperlocal location, AI gives you that real breath.
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It also gives you speed, right, on this unstructured data. So no longer do we
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need to listen to these government meetings.
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We don't have to go study the ordinances, scan local news, social media,
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right? There's this, I can click on a button and get a full report of this information, right?
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This is uncovering information that no human team could uncover.
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And I have two stories on this, actually, that I think was super interesting.
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There was one time where there was a lack of government meeting records,
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right? There was nothing available. And we were studying to do this location.
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And somehow someone took their iPhone, right, in the meeting and they videotaped
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the whole meeting. And they were like, it was a terrible video,
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right? It was underneath the desk. They were trying to hide their phone.
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But that single, like the AI found this, right? And that like completely changed
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the game in terms of how we understood those commissioners. They were actually
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very against projects in that area. And we wouldn't have known that, right?
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Because the lack of meetings were so, you know, they were just not there.
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But another situation was the government had actually redone their website,
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and the AI somehow uncovered remnants of the old website.
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The URL was not obvious. Like, you would never have known if you were trying
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to just search for this yourself.
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But we uncovered all of these old meetings that were still there, you know, accessible.
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So AI is just the breadth, the deepness that you can go into this data.
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It's finding things you wouldn't otherwise find.
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But the real moat is just the framework on top. So AI is, you know,
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AI that only summarizes is kind of hollow, right?
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You're going to miss, you know, the non-elected gatekeeper, right?
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The actual person who's in charge, who's pseudo in charge of accepting your project.
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You're missing the denied project from different technology types.
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You're also missing like this deceptively quiet looking jurisdictions,
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right? The ones that have by right permitting is that's not necessarily a win,
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right? Then your technical gatekeepers are now more important.
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And so I would just say AI is giving you that breath and giving you the ability
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to provide that human analysis on top.
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All right. So since we're going down the path of storytelling,
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can you share a high-level example of how better visibility into approval or
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stakeholder risk has changed a project's trajectory?
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And I know you can't share maybe too much, but also include what tends to surprise
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teams when they start using that kind of insight earlier on.
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Yeah, I would say the thing that surprised us and how our data was getting used.
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We've had more than one client tell us that they killed a project because of our report, right?
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So we do this risk rating one to five, five is bad, one is good.
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And so, and in one case, the client actually told us, like, she knew the location
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was not a good one, right? And she was trying to tell everyone,
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like, hey, we shouldn't build here.
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But it wasn't until she had this objective report, right, validating her concerns
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that she was then able to be like, hey, I told you guys, this is not a good location.
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So that's one thing because our clients want to be able to kill bad projects
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sooner. So that's a really important thing for them.
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But another example that surprised me was this particular client had ordered
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reports all in the same geographic area. Like, you know, one,
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you know, there was, I don't know, four, I think four counties all in the same kind of area.
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And they were all rated a four out of five, right? Five being the worst.
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So they were all rated and not a good location to build.
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And instead of backing away, the client actually decided to pursue those projects,
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but they put a full-time resource who lived there and who was going to live
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there for the next several years there.
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So they had someone who's there who could answer questions, be there whenever needed.
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So that was just another way that people are using our data.
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But I would say that the other things, too, is I would say affluence actually
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makes approval harder, not easier, because if you think about it, if you don't need.
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The tax revenue, then the developer has less to work with.
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And then the other kind of surprises is that the most predictive precedent for,
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understanding how past projects went and how yours might go is not always projects of the same category.
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And I mentioned that earlier. So, I mean, the most useful comparable is actually
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what activated the same type of community conflict, whether it's heavy industrial
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use, the roads. You know, what does this do to our town?
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That is sometimes provoked from a different technology type.
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So whether it's a denied warehouse or a gravel pit, that can actually predict
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a data center's fight better than the approved solar farm next door.
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Wow, really interesting. I want to go down the permitting path because that
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seems to be a big issue right now. So those challenges often come down to community
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sentiment, political dynamics.
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How should engineers and developers better incorporate those factors again into
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the early stages, which really sounds like the key here?
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And are they more predictable than people assume?
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It is actually more predictable than people assume.
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We talked about this earlier. You have your decision-making bodies,
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the governance archetypes.
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So we did a study last year through all of our reports looking at just the governance
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archetypes. Like basically, how does this governing body make decisions?
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How do each individual, how do the individuals within that make decisions?
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And we actually found it was really interesting.
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The governance archetype is actually the leading predictor of project outcomes.
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So how our decision-making body is making those decisions is actually going
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to affect your outcome more than opposition, more than anything else.
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And so if you know that, and you know that you can actually pretty easily classify
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your governance body into these
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archetypes, you can actually predict how your project is going to go.
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And of course, there's lots of nuances, And, you know, who are your allies?
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Who do you talk to? And what are your talking points with them?
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Like, we absolutely want someone on the ground. You know, our report is not
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a substitute for people actually making friends and connecting and understanding
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what the community means.
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But I think that having a starting point of who to contact and what to say to
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them and who not to contact and what are
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the talking points is actually the best way to then bring this,
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the political and community risk into,
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your decision-making process and throughout the whole development pipeline.
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All right. So I've probably got time to get one more rapid fire question in
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because, but it's forward looking.
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So I want to make sure we talk about it. So looking ahead five to 10 years,
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which I don't even know if I could guess where we'd But how do you see project
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planning evolving with better risk intelligence that you all are working on?
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And what is one particular step that firms could take today to move from a reactive
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place to proactive decision making?
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Yeah, I think approval, social political risk, that'll become the standard.
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I think that it'll become quantified and tied into the early stage input along
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with, you know, engineering and environmental diligence.
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I think portfolios will be triaged by the approval risk before capital commitments.
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And so site selection becomes risk intelligent, not just resource driven.
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And then second, I see the more AI first your firm is, the better positioned it'll be.
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And I'm talking about not just siting and permitting.
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I'm talking about everything. Like, think about your marketing,
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your sales, your operations, your finance, your attorneys even.
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Like, I'm not going to I'm not suggesting to go to get rid of all of your attorneys.
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But for us, like, I want to hire attorneys that are AI first,
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right? I want them to use AI.
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And then they, of course, I want them to double check their work.
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But I want them to be more efficient and catch more things than that human eye might catch.
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And so whether it's your core competency or your, you know, buy versus build
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is always like a question.
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But I think the winning combinations are firms that marry that AI within their
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operations from site selection all the way up to operations,
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tied with the right messengers to get their projects built.
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And you asked one lesson. So the practical step today is I would run an approval
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risk screen before committing to a site.
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So, you know, actually internally ask yourselves that question.
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Will they let us, right, upstream, like before, like right when you're thinking
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about the location itself so that it is, you know, answered on a spreadsheet, you know, early on.
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This is the risk level versus being discovered at the wrong time,
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right, when you're already down the path and at the hearing.
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Well, that really seems like the theme of this conversation and the strongest
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advice is to get in early and get ahead before it becomes a really big problem.
00:17:52.944 --> 00:17:57.679
So before we wrap, I want to let our listeners know about an upcoming educational
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opportunity that expands on many of the themes that we just discussed today.
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So we hope you join ACEC on August 20th and 21st. It's a strategic communications.
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Two-part series for mission-critical and public-facing projects.
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It's focused on practical strategies for managing what we talked about today,
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managing stakeholder engagement, public perception, communication challenges,
00:18:21.069 --> 00:18:23.829
including those surrounding data center development, of course.
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So you'll hear from industry leaders at member firms, GHD, DLR Group,
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and HDR, as well as a public relations expert, Adam Week Kunis of Mill Dam Public Relations, excuse me.
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You can find registration details on the ACEC website and in this episode's show notes as well.
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And I also have to give a plug that ACEC's Energy Committee also released a
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position statement on data center growth related to U.S. energy infrastructure,
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which we'll also link for you as well.
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That's about all the time we have for today. Navdeep, thank you so much for joining.
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Thank you. I enjoyed this. Awesome. And Thank you to our listeners for tuning in to another.