Volts published seven conversations in September, which is a lot for anyone to listen to. We’ve rounded up the highlights into less than an hour. Enjoy!
I am in a hotel room in Aarhus, Denmark, just about to go to bed, but I wanted to get this out to you, so pardon the sound quality. Once a month, I pull one chunk out of every episode, string it together, and create a clip show to give you an overview of the month’s episodes.
This was a big month for Volts, a couple of technical deep dives, a couple of broad overviews, and lots more. Some of my favorite episodes of the year, I think, this month.
Paid subscribers get these early. Each one opens up to everyone when the next one comes out. So the Best of August is now open to everyone. If you know somebody who’s been meaning to check out the show, that’s the one to send to them. All right. Seven conversations this month, one piece of each.
As always, I deeply appreciate all of you who pay for subscriptions. This podcast is entirely listener-supported, and I couldn’t do it without you. So let’s get into it.
Chapters
00:00 – Introduction
01:32 – Leah Qusba on which clean energy messages work
07:17 – Carl Hoiland & Joel Edwards on hunting for geothermal
13:09 – Jane Flegal on philanthropy and the environmental NGO world
19:36 – Nelson Abramson on data center noise and water use
25:37 – Frank Maisano on the pace of climate action
31:36 – Cole Ashman on who is buying plug-in home batteries
36:36 – Luke Winston-Almanzar on a heat pump water heater with a computer
Resources
The Full Episodes
How to respond to disinformation about clean energy, at scale
How to build a more flexible, quieter, less obnoxious data center
Related Volts Episodes
Text Transcript
David Roberts: First up, we begin with Leah Qusba, CEO of GoodPower, which pushes back on clean energy disinformation.
I’m curious what else you have found just in your message testing. I mean, is it just all the intuitive stuff like cost and convenience or what matter, you know, that kind of thing? Like, what have you found about messages themselves?
Leah Qusba: Yeah, definitely. I think messages, right, we have to get away from, and I think our biggest failure is sort of our own egos and wanting people to care about climate or wanting them to care about… And really essentially what it boils down to, we got almost 70% of Americans that live paycheck to paycheck right now. So we’re thinking, “Am I gonna keep my job? Everything’s more expensive. What about my health care? What about filling my car up with gas?”
We do this great series, it’s on our GoodPower YouTube channel, called What We Pay. And we’re telling these stories. There’s a story on there from Pennsylvania 10, this is Scott Perry’s district. He’s a House Republican, and we just talked to this family. They have an $859 utility bill. They pull it up on their screen.
We did, like, a mini documentary. They’re like, “Yeah, we have other bills due tomorrow, so we’re gonna have to choose which ones to pay.” So essentially, yeah, economic security, Republicans and Democrats all have to fill their gas up, their car up with gas. They all have to pay an energy bill. So I would say election cycle after election cycle, having economic security and feeling like one is getting ahead, I would say really helps. How do we tie that to the energy transition? Oh, well, how about we stop the tyranny of far off wars-
David Roberts: Mm.
Leah Qusba: and just break our dependence on this stuff, and just, you know what, charge your car in your garage for a couple bucks? That sounds pretty great, and if you look at Australia and if you look at Europe, EV sales are through the roof right now. I mean, the trends are everywhere globally, so I’m hoping to see that in the US. But I think the values argument around some of these technologies and how ideological they are and how polarizing they are across political lines is really holding us back. So I think what is working, definitely economic security. I think second, when we look at audiences in rural conservative places where most of the utility-scale renewables are being built-
David Roberts: Mm-hmm.
Leah Qusba: energy security, grid reliability, like keeping the power on around extreme weather, American competitiveness with the rest of the world, and keeping us ahead and making sure that, you know, we can build these technologies here. I would say, you know, the jobs message, while I feel like we’ve, some practitioners have kind of turned away from that, we still see a lot of potential and influence when you show really cool people in clean energy jobs that love their jobs, and they’re showing beautiful vistas from the top of turbines or women in solar or people on battery energy storage sites. There’s a ton of stuff on our YouTube channel about that. So I would say it depends on the audience. Sometimes a climate message really works for, like, a young audience under 30.
David Roberts: This is what I wanted to ask about. One of the big fights right now is I think, like, everybody, almost everybody has come around basically to thinking yes, obviously, like, cheaper energy, more reliable, better for your personal immediate life is the best thing, is the top thing, is the best thing to lead with. But now there’s this argument about, like, some people wanna say, but, like, yes, bolstering that with climate-will boost you a few points. And then other people will say, “No, if you throw climate in fact you polarize a bunch of people away. Even if you’ve got them on your side from that, then they’ll polarize them away with climate.” Like, I wonder specifically about climate, what you’ve found.
Leah Qusba: Can I just start by saying something maybe very polarizing? But I think this is just the most ridiculous false debate that is so dangerously distracting from the bigger issues. I’ll just say that. I mean, we-
David Roberts: They love it on social media though, Leah. They’ve-
Leah Qusba: They love it. They love it, and they, I’ve been reading all the blogs, and news stories, and Substacks, and everything about it. I mean, how we think about it is maybe a little different, but I think we can have our cake and eat it too. And to continue with the dessert analogy right, we think about it like a pie. So there’s some people right now, like 8% of young people under 35 rated climate as their top voting issue in 2024, right?
That’s a lot of people. So we wanna use a climate first message for them. And, you know, it’d be great if people who are kind of could be predisposed to being really driven around a climate message to get more of them, to increase the size of that slice of the pie. But then there’s other people who will never be driven by a climate message. I will reference my dad who is a staunch conservative Trump supporter, but he now loves solar because his HVAC guy has a side hustle doing rooftop solar, not because his daughter works at GoodPower, but because his HVAC guy convinced him, you know, he could save money.
So I mean, it really depends on the audience, and this sort of crude segmentation between should we say climate, shouldn’t we? When you look under the hood and really look at the nuances of how we communicate the audience segmentation, how we market, the answer is just not black and white like that.
David Roberts: Right. Yeah.
Leah Qusba: It’s, you know what? Use it if it’s advantageous. In most cases, it’s not incredibly polarizing.
It’s also, what I said, a logistical issue. When you say climate, how do the algorithms respond?
David Roberts: Right.
Leah Qusba: On TikTok, there’s evidence that your content is suppressed.
David Roberts: Yeah.
Leah Qusba: So it could be a logistical reason. It’s not even a polarizing thing. I’m less worried about the polarization of saying climate. I actually don’t think it’s that big of a deal for most people right now. We’re seeing it. We’re living it. It’s really a logistical thing with certain platforms deciding to suppress or serve your content to different audiences, and how do we get by the goalie?
David Roberts: Next up, Carl Hoiland and Joel Edwards of Zanskar, who use machine learning models to hunt for conventional geothermal.
And so then let’s talk about this third discovery. This is called Big Blind in Nevada and this is sort of I think the news that made everybody really sit up and pay attention because this was unlike the previous two we’ve discussed. This was genuinely new. There were no prior wells on this site to give you any data. There were no surface manifestations of activity on the surface. This was a genuine blind site. So how did you find it? In a nutshell how did you find it?
Carl Hoiland: Yeah, this is really our bread and butter. So Joel, I’ll let you kinda talk through how greenfield perspective exploration works.
Joel Edwards: Yeah, it’s that concept we talked about at the beginning David, that you build models. Everybody can build models, Zanskar builds models. But you have to pair your model predictions with tests in the field of those predictions. And so this is one of those cases where we had made a set of predictions in… across this part of the state, and we were out testing those predictions and ran into this system at this location. And this is a system that’s west of Tonopah, Nevada, so pretty remote. And like you said, there was no prior data at the site and if you or me or anybody were to walk across the site, there would be nothing at the surface that would clue you into that there’s a massive system below. That initial discovery was made at the end of 2023, and then we went through the permitting processes and we came back with a larger rig and drilled into that resource in...
What year is it now? It’s 2026. I think we drilled into it in 2025, and we encountered commercial reservoir conditions at around 2,000 to 3,000 feet. The thing that’s really exciting about this system in particular is how big it is. This system by the surface footprint of the heat flow anomaly is much bigger than Pumpernickel and much bigger than Lightning Dock.
David Roberts: Oh interesting. I guess you’ve just kind of started exploiting it right? I mean is it commercially operational? Where’s the state of play there?
Joel Edwards: No, we’re in the well field stage of the project. So we’re permitting additional exploration wells right now to go back to the site. So we’re still early stages and yes.
David Roberts: Still trying to figure out how big the site itself is, how big the resource itself is?
Joel Edwards: Exactly. We have a sense of sort of the surface area, how big it is. That’s why I can make that claim relative to some of the other projects. But we do not yet know at depth how big and hot the system is. It seems quite spicy. To think that this thing was sitting out there. There’s lots of exploration across the West for minerals and other things, and to just think that this had been missed for so long, you know, gets us really excited about the future.
David Roberts: So you had your models, and they gave you a sort of probabilistic sense, and then you go out and do some exploration in those probabilistic areas, and you just got lucky on this one. Or I guess not lucky, the model… The probabilities paid off in this spot.
Joel Edwards: Yeah, I know. Maybe the models got lucky. Maybe we got lucky. There’s luck in all this, and there’s also bad luck when we miss. And to be clear, we’ve missed way more than we’ve hit. Like, to make this sound like this thing has been working better, you know, than the legacy explorers. In the early days, our models were frankly much worse than expert explorers. And we do benchmark against expert predictions in our modeling space, and it’s really only… Our models really only in the last year or two have started to outperform. And, you know, every model that you build, sometimes your models get worse.
So it’s not like it’s up into the right. It’s like there’s a wobble. It’s sort of like the climate, right? Like some years it’s wobbling hotter and cooler, but you’re generally up into the right, and that’s very true for our modeling space.
David Roberts: So you think from now on over time, the more exploration you do, the more data you feed into the models, the better the models get. Over time, you think you’re gonna get better and better at finding these things?
Carl Hoiland: And that’s actually quite clear already from the data we’ve collected. Like Joel said, they started out much worse than humans. They caught up, and they’re performing better in many locations and areas. And we’ve seen it improve in two ways. As you put more data into the models, the same architecture ends up yielding higher precision, you know, better performance. But in other cases, actual changes to the architecture, the way we’ve designed the model itself with the same data yields an improvement in prediction. And so the teams are constantly driving improvements on both of those fronts.
David Roberts: Interesting. So maybe not up and to the right for every increment, but over time, in the fullness of time, you’re going up and to the right with your models.
Carl Hoiland: Yeah. And really those sort of side steps that Joel’s talking about are what might feel like even a step back is often in a certain type of metric, like precision or accuracy. But it usually reflects that we weren’t realizing that there was something less generalizable about the prior model. And so this step back is getting you to a more accurate representation of the uncertainty. So it’s still a better model, you’re just being more correct in your estimation of uncertainty for it.
David Roberts: Right. Interesting. Interesting.
Joel Edwards: You know, this manifests... It’s funny, you would expect our sort of hit rate, like when we find a system, to be like consistent and linear. But what I’ve found is that our hit rate is like, you know, like in sports, you get into like a streaky zone, like home run after home run or whatever, you’re hitting the threes like back to back to back. That’s the same thing in our models. Like, we go through periods where bang, bang, bang, like survey after survey after survey, we’re hitting, hitting, hitting, and then we go through like a cold slump for a period of time, and then we push out a new one. So it’s been a very like non-linear sort of spikiness to the discovery rate.
Listen to this episode with a 7-day free trial
Subscribe to Volts to listen to this post and get 7 days of free access to the full post archives.
