Kristin Myers: Now, every market that you can think of has a number at the center of it. Oil has Brent, for example. Interest rates have SOFR. And without an agreed price, nobody can write a contract. Grid batteries, the ones being built to hold power and sell it back when the grid needs it, are a fast-growing asset class, with billions of dollars going into them. And until recently, they did not have a number.
Well, my guest’s company is trying to be that number. But first, let’s take a look at what they are building. The road from an idea to the opening bell starts right now.
AI is hungry for power. Data centers are driving electricity demand higher, and that’s putting a bigger spotlight on batteries that can store energy and send it back to the grid when it’s needed most. But here’s the problem: if you’re putting millions into a battery project, how do you know what it should earn, or whether it’s actually a good investment? That’s the opportunity Modo Energy is going after.
Co-founded by Quentin “Q” Scrimshire, Modo provides data, forecasts, and benchmarks designed to help investors, developers, and operators understand battery markets and price the risk. And now it’s pushing further into AI. Its AI analyst, Ko, lets energy professionals question the data, compare markets, and analyze battery performance, with answers grounded in Modo’s data and research.
But the bigger opportunity may be turning battery performance into something Wall Street can actually trade. Modo’s benchmarks are already being used to price risk and settle financial contracts. So now comes the real test: can Modo become the benchmark that global energy markets build a new generation of financial products around? From Startup to Stock Exchange, Modo Energy co-founder Quentin Scrimshire is next.
And joining us now is Quentin Scrimshire, CEO and co-founder of Modo Energy. Quentin, do you want me to call you Quentin or Q?
Quentin Scrimshire: If I’m in trouble, call me Quentin. But otherwise, call me Q.
Kristin Myers: Q. I like it. It’s very like [unclear: “doctor X”]. Is that from the...
Quentin Scrimshire: Yeah, that’s a person. I didn’t make that up.
Kristin Myers: Yeah. All right. So let’s talk a little bit about your background. You spent a decade or so in batteries and in power markets before this: development, financing, and trading. What were you running into? What were you seeing? What were you hearing that made you say, I have to start Modo?
Quentin Scrimshire: Well, the best thing to do is step back for a second. We’re entering the new energy economy. Everything’s about electrification. The world is building trillions of dollars of new energy assets, and all of these assets are going to sit on balance sheets, mostly private balance sheets. And so this is big wind assets, solar assets, batteries, data centers, and big industrials. All of these are going to sit on balance sheets, and they need to be valued on the balance sheet. So we help folks do that.
And just a bit about the background: before we came along to do this, it was a Wild West, right? So you had companies building billions of dollars of assets and signing agreements with trading partners to trade the power through those assets. And nobody knew what was going on. And everybody said that they were the best, or they were, you know, outperforming everyone else. And we came along and standardized that.
Kristin Myers: Mhm. Okay. So what do they still not know? Because you called it the Wild West. If right now, today, you know, October 2026, myself or someone else wants to decide whether they’re going to put a couple hundred million dollars into a battery project, what do they not know now that they also might not have known several years ago?
Quentin Scrimshire: So this is actually a very mature asset class now. So I’m only in my late 30s, and I’m a dinosaur in this. We’ve got a technology S-curve.
Kristin Myers: I’m in my late 30s. Please don’t call us dinosaurs.
Quentin Scrimshire: Right. So I started getting into the battery world in the 2010s, early 2010s. And back then, you know, a million, $2 million in a project was a big project. Now you see single projects in the 500 million to 1 billion range. And if you look into the Middle East, it’s even larger. And the asset class has matured, in that we now have much more mature technology. Silicon and lithium-ion batteries are now a mature asset class, and you have institutional capital coming in.
So these projects are not just financed with equity. They’re also financed with debt. The offtakers for these projects are big-balance-sheet energy companies and big global power, oil, and commodities trading houses. So we’ve come a long, long way. Before Modo existed, really, no one knew what the value of these contracts was and how much money these batteries could make in the markets, and we help folks figure that out. Whether you’re a big bank or an institutional investor or an asset manager or an insurer or a power trader, we help the whole market figure that out. A bit like what S&P does in equities, we do for grid assets.
Kristin Myers: Right. So talk to me about arriving at that number, right? How are you picking it, and how are you standardizing it? Because in my mind, power essentially pulls in a lot of different factors, right? It’s a battery that is sitting ready for someone to plug into it. It’s grid services. There’s a lot that sort of goes into it. So how are you kind of arriving at that number?
Quentin Scrimshire: Yeah, it’s an interesting problem set, because in the commodities world, and I don’t want to underplay this, you have some fantastic price reporting agencies. And in the olden days, they used to call round the traders and say, what price did you settle at today, and aggregate that. Terrific business. And you can do that in commodities that settle daily, for example. With batteries, though, these are like spaceships, this asset class, right? They can participate in so many different markets within the day that it’s a very complicated problem to figure out where they are.
Okay, let’s step back. How does a battery make money? It buys power when it’s cheap and sells it back to the grid when it’s more expensive. And there are lots of times a day it can do that, and it can access additional services on top of that. So you can’t just call the trader and say, what did you trade at today? You have to build software.
So that’s what we do: software which is calculating, for an asset in this location with these market dynamics, what money should I have made, or could I have made, or what money did I actually make, across a whole range of services, from energy arbitrage, that’s playing in the power markets, to grid stability services and other things. Very locationally dependent. Some of these markets, like Australia, clear every five minutes. In Europe, it’s mostly half an hour or 15 minutes. In Texas, it’s 15 minutes, right? And we’re all around the world.
So this is a software problem, not a telephone problem. And that’s where we build benchmarks that are real time and are calculating, for all the assets out there, what could they have made, and then aggregate that all up and give those prices to the market.
Kristin Myers: So okay, we’re sitting on the floor of the New York Stock Exchange, and you mentioned the S&P. Let’s just do a similar sort of comparison as you’re thinking of the benchmarks. How are you thinking about them globally, right? Are you thinking, okay, we’ve got one benchmark inside of the UK, or is it Europe? Or are we thinking of another benchmark inside of the United States, or is it even regionally dependent within the US?
Quentin Scrimshire: Yes, it’s very regionally dependent. We have over 500 benchmarks. Firstly, I mean, this is just so awesome to be here. It’s like a boyhood dream. So thank you to you for having us on. So it’s very dependent. We have markets, as in Great Britain, where it’s split between the national price, but you also have Scottish assets. Assets behave a little bit differently. We have European regions...
Kristin Myers: Would love to know that they’ve been separated out.
Quentin Scrimshire: Yeah. Scottish assets do pretty well, actually, right? Because you get a ton of wind, and you can’t get the power down to the south. But anyway, so it’s very location-dependent. And then there’s an apples-and-oranges problem, because some battery assets can only provide power for one hour, some of them can do two hours, four hours, or longer. So you then have to divide up the assets by what type they are. Generally, there are one- or two-hour systems. We have different benchmarks for that.
But yeah, there’s a ton of ways you can chop up this market. Some of our customers, they’ve spent hundreds of millions on Tesla, for example, and they only want to see how their Tesla assets are performing against other Tesla assets or similar assets in Europe. Our customers that only use BYD, a big Chinese manufacturer, want to build their own custom benchmarks with us to see how they perform against other BYD assets. So yeah, a very, very interesting problem.
Kristin Myers: It’s almost dizzying how much data you have to really pull in to essentially create this benchmark. So to that point, where is the data coming from? You described it earlier as a software problem, right? For other commodities, you could actually call around, find out what price they settled at, and sort of create an aggregate. Obviously, the software is coming in and helping do that throughout the day, multiple price points around different regions. But is the data coming from the same sort of sources? Is it getting that aggregate data the same sort of way, from all of these different operators? Where is it coming from?
Quentin Scrimshire: So we pull data from exchanges. We pay the exchanges for the data. We scrape it from some fairly archaic websites from local authorities and local distribution networks. We have APIs with grid operators. So we pull it in from about 40 different sources. Some of it we pay for. Most of it is public, one way or another. And then we also have analysts that are doing some cleaning work. There is an aggregation and cleaning job to be done before you can even think about transforming that into a price.
Kristin Myers: So what does it take to become a benchmark that folks actually trust and want to use?
Quentin Scrimshire: So for us, we’ve been doing this for a long time. We started in 2019. We sell a trust product, as you say. So we’re FCA-regulated. We follow the IOSCO principles. There’s a whole governance layer you need to provide on top, where you need to convince the market and demonstrate that not only are you going to be accurate with the benchmarks, but you’re going to be consistent. And so for us, we have eight years of track record showing that. But yeah, really, it’s a trust product. Every day, we show up at work thinking we’ve got to earn the trust of these customers, and we take that very, very seriously.
Kristin Myers: So say a little bit more. You mentioned it just a second ago. Who is the regulating body? Who is governing you guys? Because contracts are being determined based upon the benchmark prices that you’re creating.
Quentin Scrimshire: Yeah. So generally, there are physical contracts for power delivery that get settled using our benchmarks, sort of benchmarks as references for performance. Getting a bit technical here. And then there are also nonphysical contracts, like swaps and derivatives. There was a big deal announced a few weeks ago with [unclear: “ZK and StarCraft”] in the UK. There’s lots of German partners and US partners also doing the same. And these are nonphysical hedges against exposure to batteries.
All of those need a benchmark that they can trust. For us, we are the global standard for doing this. And so if you have anything to do with battery assets specifically, and it’s a great privilege to say this, you probably use Modo, or you’ve come across Modo, and you’ve known about us for a long time. And we’ve consistently shown up and earned the trust of those folks.
On the regulatory side, yeah, we have to go through a long process with the regulator, with the FCA, for example. And then now we’re working with the exchanges, including ICE and others, about building products on exchanges. They want to see that you are what you say you are, and there’s a whole process to that.
Kristin Myers: So how does a grid battery actually make money? Well, there are about four ways. First up is going to be arbitrage. That’s buying power when it’s cheap, usually overnight or when the sun is high, and then selling it when it’s expensive. Then there’s the second, frequency services. The grid has to stay balanced second by second. Batteries respond faster than anything else, and grid operators pay for that speed.
The third is going to be capacity, getting paid simply to be available on the days that demand spikes. And then the fourth is going to be the ancillary services. That’s a range of smaller products that keep the system stable. So the revenue mix shifts constantly, market by market, and it changes as more batteries come online, which is exactly why somebody had to start measuring it.
We’re back now with Quentin Scrimshire, CEO and co-founder of Modo Energy. Thank you so much for sticking with us. Q, so let’s talk now about AI, because the infrastructure buildout, the need for power, is absolutely massive. Data centers have been driving up electricity demand. How is that impacting battery economics?
Quentin Scrimshire: And they are directly linked. So before data centers came along, the battery buildout story and trajectory was huge. It’s monstrous, right? And then if you compare that to the amount of CapEx that’s going into data centers, everybody’s seen the graphs. It’s huge. The problem is all of these data centers show up. They are load on the grid, which is obviously a very political topic at the moment. Who pays the cost of servicing them? And therefore, that puts congestion on the grid and stresses the grid. And the best asset in the world to go and sort out congestion and stress in the grid is batteries.
And so you’ll notice, firstly, all data centers have a battery installed on site one way or another, either as backup power to get through brownouts or blackouts, or to manage a smaller grid connection so they can get the power to the data center. You also are now seeing fairly complex products between hyperscalers, who are building big loads, data centers, and standalone battery assets that want to provide services in conjunction with each other to manage the grid and make sure the data centers keep running.
So it’s a very interesting play that’s happening here, where you’re seeing the big load assets now starting to speak and interact directly with battery and flexible assets, to get connected to the grid and manage their position on the grid.
Kristin Myers: Talk to us a little bit about how that demand for power, for electricity, as the AI boom continues to, well, boom and explode, how is that either helping or hurting Modo’s business? Because I imagine, obviously, the demand for power, as I just mentioned, is greater, but so is competition.
Quentin Scrimshire: So very good for our business, actually. Personally, I think the data center buildout is very good for the American economy generally, as a proud Brit, but becoming a proud American. I think it’s a really important story for us in the US to build as much as we can of this, for all sorts of sovereignty issues and defense issues and all the other stuff. I’m not going to go there.
If we just think about Texas for a second, right? Texas, a phenomenal grid. You know, like the poster child of free-market design in the power world. All of us power geeks get excited about Texas, right? The total demand in Texas is roughly 90 gigs, 90 to 95 gigs on a peak day. Really hot summer.
Kristin Myers: Is that a lot?
Quentin Scrimshire: That is. So to put that into perspective, that’s about...
Kristin Myers: Knowing nothing about power, that’s just a lot.
Quentin Scrimshire: Thirty large nuclear power stations, big old nuclear. That’s a lot. In Texas, the thing that you don’t hear about enough is Texas is one of the renewables capitals of the world. They’ve built so much renewables, but you don’t hear about it. You just hear about oil and gas.
Anyway, so roughly 90 gigs, and we’re going to grow that with the data center buildout to somewhere between 110 and 140, depending on who you ask. We’ve never added this much load to a grid this fast ever before, and we’re doing it at a time where the grid itself is already maxed out, right? So we’re going to have to come up with some really interesting solutions for this. We’re very lucky that this is happening at exactly the time where lithium-ion technology has brought battery cell costs down so aggressively that we can go and build these batteries everywhere to go and solve these congestion problems. But this is a Manhattan Project-scale problem that we’re trying to deal with. Nobody has seen anything like this before on power grids. As a power nerd, this is insane and very exciting.
Kristin Myers: So talk to me a little bit about Ko. What is it, and what does an energy professional do with Ko?
Quentin Scrimshire: Yeah. So in our business, we have building blocks of intellectual property. We do a lot of power market modeling, and that’s what feeds into our benchmarks. But on top of that, we have over 200 clients around the world, over 30,000 active users of our terminal. And they use our terminal to figure out what’s going on in power markets, value assets, make sure they don’t cross covenants with the bank, or the financial bits around it.
And then Ko is our AI analyst for our customers who are energy professionals, to automate their workflows. And so in the same way that in the legal profession you have a couple of big ones, you know, Legora and Harvey, that are legal AI for lawyers, we’re doing the same for energy professionals. And we’re seeing explosive growth in usage across our customers and in the terminal. We’re really excited about where we can take this, where energy professionals’ workday...
We as energy professionals actually spend about 80% of our time doing admin and messing around with Excel sheets and producing PowerPoints, and 20% is the judgment and the stuff that we really came to work for. And we want to flip that around. We want to make it so 80% of your time is doing the really high-value stuff, and all the energy-related admin is done by the AI. And that’s what Ko is. Ko takes the busywork away from you.
Kristin Myers: So this is the question that I always ask, and we’re not going to go too deeply into this. But when it comes to AI, there’s always the question around accuracy, right? And so since folks are actually making decisions based on these numbers that you guys are providing, what does that look like with Ko, in terms of making sure that it is accurate, since so many contracts, things like that, are being written based upon some of the analysis that Ko might provide?
Quentin Scrimshire: Yeah. So there’s a jeopardy here, in that we sell a trust product. And so we’re putting Ko in between that and the trust product. We’ve got to make sure Ko is correct. So not to get too technical, but this is an evals problem, right? So with all vertical AI companies, essentially you build what you might experience as a chatbot and a workflow automation tool, but it’s a harness that calls different models, and we call a number of different models. And then you set some parameters around it to define what good looks like and what correct looks like.
And so we have power market engineers who come from the industry that are constantly writing a rule set, which we call evals, that define what a good output looks like and make sure that it meets the quality standards that we need. So yeah, I mean, firstly, it’s a lot of work to get right, but this is our bread and butter. This is what we do.
Kristin Myers: So talk to me about some of the biggest misconceptions that folks might have around battery power.
Quentin Scrimshire: Oh, wow. What a question. I think people underestimate how compact these things can be. So you imagine a big battery farm, like a solar farm, or a big battery park. And these assets are enormous. You know, some of these are one-gig to two-gig scale. This is coming back to a nuclear power station size, but footprint-wise, much smaller than a nuclear power station, much shorter, less of a blemish on the countryside. And now we’re seeing so much innovation in lithium-ion cell density, battery cell density. You can pack more and more energy into much smaller containers.
So I think there’s that one. They’re a lot smaller than you think. They’re a lot quieter than you think. I really think that the impact on the environment and the landscape is much smaller than folks think it is, and they add so much value to the grid and to the economy. I personally just don’t understand the NIMBYism around them. We’ve seen issues in California and other states, where there are certain regions where they’ve said no more batteries for a while. And we say, well, guys, that’s fine, but where do you think the power is going to come from, right? You’re going to produce a lot more carbon burning a lot more stuff if you don’t put these batteries in right now. So I don’t know. A bit of a rant there.
Kristin Myers: But I think it’s good, because when I think of these batteries, I think large, and I probably do think about the climate risks. And so it’s good to know that perhaps we’ve gotten it a bit wrong when it comes to thinking about that power.
So I want to ask you now about scaling Modo. Obviously, as you mentioned, you guys are international, but I’m assuming you’re not in every single country all around the world. So as you’re thinking about scaling, even internationally, how does that look, and how does that work? What are the issues that you guys are running into? Because I imagine each power market is regulated a little bit differently. So talk to us about those sorts of plans and the issues, or I would say the hurdles, that you guys face in terms of that scale.
Quentin Scrimshire: So we’re in 15 markets around the world. We’ve got offices in London, New York, Madrid, and Sydney, and we’re expanding through APAC, South America next year, and then the Middle East. All businesses have got complex problems to solve. One of ours is that, as you say, we scale into what we call hyperlocal markets. So when you enter a new market, you need to make sure you’ve got the right expertise. And you need to bring that expertise in and build the right products for that market.
It’s not like we’re selling Dropbox. No shade on Dropbox, by the way. Love Dropbox. But with Dropbox, you’re like, okay, we’re going to expand to France, right? You just stick it in French, right? Well, a bit more than that. But for us, we have to make sure we totally understand that market. And that means that our market expansion plan has to be very deliberate about entering markets and making sure we hit the quality bar before we release products.
So for us, entering a new market takes about nine months. We have a whole thing that we call the model review process, where we review all of our models and how they work in that market, and there are a lot of go/no-go decisions that we have to make. In Texas, for example, it took us two years to pass our internal tests, to build a model that we’re comfortable with. So in our business, we only get one shot, right? As a benchmark business, you’re either a benchmark or you’re not. And we’re very fortunate that we are the benchmark, and we’ve got to keep hold of it. And we’ve got to make sure that we earn the trust every day. And that’s a quality decision.
Kristin Myers: Absolutely fascinating. I cannot wait to speak with you again. As I mentioned, thinking about things like ETFs, as we know, we have an ETF show. So I’m definitely going to want to speak with you even further as we talk about what trading products people are creating based on those benchmarks. Quentin Scrimshire, co-founder and CEO of Modo Energy, thank you so much for joining us.
Quentin Scrimshire: Thank you.
