You’re out of free articles.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Sign In or Create an Account.
By continuing, you agree to the Terms of Service and acknowledge our Privacy Policy
Welcome to Heatmap
Thank you for registering with Heatmap. Climate change is one of the greatest challenges of our lives, a force reshaping our economy, our politics, and our culture. We hope to be your trusted, friendly, and insightful guide to that transformation. Please enjoy your free articles. You can check your profile here .
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Subscribe to get unlimited Access
Hey, you are out of free articles but you are only a few clicks away from full access. Subscribe below and take advantage of our introductory offer.
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Create Your Account
Please Enter Your Password
Forgot your password?
Please enter the email address you use for your account so we can send you a link to reset your password:
Patrick Brown claims to have “left out the full truth” in order to get published. But his full story is much more perplexing.

Patrick Brown is a climate scientist at the Breakthrough Institute, a heterodox think tank based in California that advocates for using technology and economic growth to manage climate change. He holds a Ph.D. from Duke University in Earth and ocean science.
Last week, Brown and a team of co-authors published a paper in Nature that found climate change has made it more likely that California wildfires will experience a particularly dangerous kind of event: a moment of rapid, explosive growth. Thanks to climate change, these dangerous events are now 25% more likely to occur, the paper found.
On Tuesday, Brown published a lengthy Twitter thread about his wildfire paper, as well as an article in The Free Press, an online publication founded by the former New York Times columnist Bari Weiss. Now Brown told a different story about his research — a far more negative one. His new paper revealed fundamental problems with climate science, he said, because it looked at climate change alone and not at the role that other factors, such as vegetation change, arson, or forest management tactics, might play in driving wildfire growth.
“I knew not to try to quantify key aspects other than climate change in my research because it would dilute the story that prestigious journals like Nature and its rival, Science, want to tell,” he wrote. “I sacrificed contributing the most valuable knowledge for society in order for the research to be compatible with the confirmation bias of the editors and reviewers of the journals I was targeting.”
These incentives revealed that climate science is now more interested in serving as a “Cassandra” than revealing new information about the world — a tendency, he charged, that can “actually mislead the public.”
Brown’s argument attracted my attention, because I have written about how nuanced and complicated climate science can sometimes be. When President Joe Biden linked the Quebec wildfires to climate change, I expressed doubts about the connection. When Hurricane Ian made landfall in Florida last year, I wrote that hurricanes have a far more complicated relationship with global warming than many believe. And in 2019, I broke the news that a respected team of climate scientists had ruled out some of the worst-case scenarios for rapid sea-level rise this century. I am not, in other words, someone who sees climate change in every shadow.
On Wednesday, I called Brown to talk about his claims, the Nature publication process, and the state of climate science as a field. Our conversation follows below. But I left the interview unsure of why Brown had made such a fuss.
Brown argues that climate science suffers from a serious misallocation of incentives. He says that his paper should have looked at the influence of many factors — such as arson or forest management — in driving rapid wildfire growth. Yet it didn’t. Even though he says these factors can be “just as or more important” than climate change, he declined to study them because the professional incentives pointed against it. Doing so would have detracted from his paper’s “clean narrative” focused on climate catastrophism and made his paper less likely to “pass muster with Nature’s editors and reviewers.”
But after talking to him and reading his paper, a different story emerged. When Brown began his research, he did not actually know that, say, arson or forest management were as important as climate change in driving wildfire growth. What he did know is that it would be complicated — and labor-intensive — to pull out every factor that might influence a wildfire’s growth. So he chose to focus his first paper on what was likely to be the biggest signal: climate change.
During the peer-review process, Nature’s reviewers asked him why he made this choice. It would have been “very difficult” to study those other variables, such as forest management, he replied. “This is precisely why we chose to use a methodology that addresses the much cleaner but more narrow question of what the influence of warming alone is on the risk of extreme daily wildfire growth,” he wrote, adding that he hoped to look at other factors in future research.
Nature’s editors and reviewers accepted this argument in good faith. And Brown did, in fact, begin studying the role of those other factors on wildfires. He claims that newer, unpublished work shows that active forest management can negate some impacts of climate change on wildfire in California, although this finding has yet to be peer reviewed.
When the Nature paper came out last week, Brown was admirably upfront about its limitations. The paper itself cautions that its findings should be interpreted only “narrowly”; Brown stressed on Twitter and in interviews that even though climate change is making wildfires worse, near-term greenhouse-gas reductions will do little to cut that risk. And journalists listened to him. NPR and the Los Angeles Times devoted multiple paragraphs of their stories to that insight and to the importance of forest management.
So I’m left asking: What’s the problem here? Brown had a knotty research problem, and he chose to divvy it up into smaller parts and focus on the easiest part first. He triaged, in other words. When peer reviewers — whom he now claims accepted his paper due only to their “confirmation bias” — pushed back on his decision, he argued against them and said that he would look at other variables later. He kept that promise; he is studying those variables now. When his paper was published, he publicized its findings fairly and accurately. The media covered them with nuance. Where’s the scandal, again? What are we supposed to be mad about here?
Brown seems to have talked himself into the view that he did something wrong, but it’s not clear to me that he actually did. Shorn of his personal example, his Free Press article amounts to a series of gripes about other high-impact climate papers. He criticizes an article that calculated how carbon emissions could hit GDP, but his concerns, while reasonable enough, are hardly an indictment of the field. He complains that journal editors look for “eye-popping” statistics when reviewing papers, but this is hardly a vice unique to climate science. None of what he describes — least of all his own behavior — amounts to an effort to “distort research” or “mislead the public” that he has seemingly alleged.
His critique has found its audience anyway. Since we talked, Brown’s argument has been cited by Fox News, The New York Post, and The Telegraph. “Climate scientist admits editing paper to fit ‘preapproved narratives,’” reads a typical headline. (Brown denies distorting or lying about his results.) The editor-in-chief of Nature, meanwhile, has rebuked Brown and said that the journal is “carefully considering the implications of his stated actions.” During our conversation, Brown lamented that only articles warning of climate change’s dangers ever appear in the media. Now he is receiving a wholly different type of coverage.
Our conversation has been edited for concision and clarity.
I wonder if you could catch me up on what’s happened in the past few days and on the criticism, or the meta-criticism, of the paper that you just published.
If you look at my tweet thread or the press release on the paper, I went out of my way to emphasize the points that I end up critiquing the paper for. I emphasize that in our current phase of research, we’re finding that hazardous fuel reduction treatments might be able to completely overwhelm the impact of climate change on fire.
My argument is that I would very much defend the research overall, but I’m making this commentary on framing it for the journal. What I did when writing with paper, when my goal was to get it in Science or Nature, is very, very common. It’s pervasive. It’s just turning the dials in all these specific ways that end up skewing the public view of the overall situation that we’re in. As a climate researcher, if you want this high-end paper, if you want this paper that’s going to make a splash and help you in your career, your goal is to cut through everything else and use a bunch of sophisticated statistics to find the climate change signal in there.
There’s nothing explicitly wrong with this paper; it’s just what ends up getting communicated at the end of the day. So in this case, you hold everything constant, you only look at this temperature impact, so you’re controlling for other factors — like changes in human ignition patterns or changes in characteristics from fire suppression. Those caveats are mentioned in the paper, so I’m not saying that they’re hidden. But you focus exclusively on climate change and you ignore these other factors that might be important. There’s a firehose of papers like this, but they end up giving a totally overemphasized impression of the climate change impact.
Climate change is this nuanced thing, and it shows up in different ways in the world, and what we know about it is quite nuanced. I think that can absolutely get lost in the loudest parts of the discourse.
I struggle with it. People are coming from such different baselines, where I think some people are of the mindset that we don’t even know if the climate is changing, or we have no idea if humans are contributing to it or something like that. And, obviously, that is completely against what all of the empirical evidence and science shows. For those people, I’d love to convince them of the importance of climate change and the dominant role that humans play in it.
But then there’s this other group of people that I think is misled by social media or certain media outlets, that are under a very misinformed impression about how large changes in weather and impacts on people are at least historically or up to this point. That starts with the journal articles themselves, and I perceived there to be strong incentives to really just focus exclusively on the climate change impact and to play it up more than it deserves to be played out.
I want to talk about that by focusing on this paper. It seems there are these other factors that shape wildfires. You mentioned just now changing ignition patterns, changes in fire suppression, changes in vegetation. I think the way that you just described them is that they “might” be important. The way you described them yesterday is that they’re “just as or more important.”
My first question is: Do we know that? I can imagine that they might be important. But have you done the research to know they’re just as important?
So the paper in Nature was submitted in July 2022, and since I submitted this, I moved on to that question. And we don’t have a manuscript yet, but what we're seeing using the same methodology is that fuel loads just have an enormous impact on fire danger. It’s a struggle to figure out how to model mechanical thinning and prescribed burning, but the results indicate that doing that at least locally can totally overwhelm or negate the climate change impact.
That will be a new paper, but that’s not some new result. If you look at disciplinary journals — it’s not in PNAS or Nature for the most part — that is kind of a consensus, that the fuel component of this is very large. It’s not unreasonable at all to think that a hazardous fuel reduction could overwhelm the impact of climate change.
So, do we know that for sure? No, but that’s one of the points I’m making — researchers aren’t incentivized to write that paper as much as I think that they should be. That’s a paper that's like, We’re going to do the very best we can varying different scenarios of ignition, or how we think ignition patterns have changed historically, or varying different scenarios of fuel buildup based on suppression policies and climate change. And we’re going to do this in a super rigorous, fair way, and we could rank these things or just see the relative influence of those factors. That has a much lower return on investment from the perspective of a researcher. It’s way more work, it’s way harder, and whatever the results, it will be much more equivocal. It’s going to be this super long paper, and it’s going to get bogged down in review.
I want that to be the gold standard. But what I see becoming the gold standard are these papers that are mimicked off of Science and Nature. You have a limit of about 2,000 words and three figures, and it incentivizes you to make this case. You have all of this data that’s messy, and your goal is to find the story and to tell the story with beautiful figures. Inevitably, doing it that way, you have to relegate things that go against your story to the supplements and explain them away. That’s the way that scientific publishing works, at least for these letter-type papers in Science or Nature.
There are two threads here, but I want to stay focused on the smaller one first. I think the way you put it in your thread was that to focus on other factors would “muddy the waters of a clean story” or would decrease the odds that this gets approved by Nature’s editors or reviewers.
Yeah.
But at the time you submitted the study, did we have methods to pull out the vegetation signal? Or the ignition signal?
What I’m doing now is the same idea as trying to pull out the temperature signal. You’re using the variation in space and time to get at the incremental influence of fuels on fire danger. But historically you would’ve had to come up with an estimate of what suppression policies had done to those fuels.
Got it. But at the time you submitted the paper, the work hadn’t been done.
Right. And I would love to have it be the case where as a researcher you could afford essentially to submit a paper and then be like, actually this other [method] is better or something, and then take your paper back. But that is insane, but you would never do that as a researcher. You need publications, and you want to build off the previous one.
I’m asking because there’s a throughline in some of what you’ve written that basically alleges that this kind of science is simply not something that Nature and these high-level scientific institutions are looking for. You wrote, “I sacrificed contributing the most valuable knowledge for society in order for the research to be compatible with the confirmation bias of the editors and reviewers of the journals I was targeting.”
I’m struggling to square that with the fact that the Nature reviewers, who are the people you accused of confirmation bias, directly ask you for this analysis in the peer review. One of them flags that there’s “numerous factors that play a confounding role in wildfire growth that are not directly accounted for, including vegetation, fire management, and ignition.” And another cautions against publishing the study because of methodological problems.
And during peer review, you responded to them, “We agree that climatic variables other than temperature are important for projecting change” — then you name all the factors we’ve been talking about — but “accounting for changes in all of these variables and their potential interactions simultaneously is very difficult. This is precisely why we chose to use a methodology that addresses the much cleaner but more narrow question of what the influence of warming alone is on the risk of extreme daily wildfire growth.”
It seems there’s this motte and bailey here. I understand that a researcher has limited time and they’re going to invest in the most methodologically clear stories. But you’re saying that you “molded” your work to fit the confirmation bias of Nature reviewers. Yet the Nature reviewers actually asked you to do the thing that you’ve identified as the biggest question mark in the article — and when they asked for that, you said it was too difficult. So which is it?
So I think that, that’s very good that the reviewers brought that up. But like I said before, doing that is, then, it’s not a Nature paper. It’s too diluted in my opinion to be a Nature paper.
This is what I’m trying to highlight, I guess, from the inside as a researcher doing this type of research. Reviewers absolutely will ask for good sensitivity tests, and bringing in caveats, and all that stuff, but it is absolutely your goal as the researcher to navigate the reviews as best you can. The file even gets automatically labeled Rebuttal when you respond to the reviewers. It’s your goal to essentially get the paper over the finish line.
And you don’t just acquiesce to reviewers, because you’d never get anything published. You don’t just say, Oh you’re right, okay, we will go back and do that work for five years and submit elsewhere. The reality of the situation is you have to go forward with your publication and get it published. They can ask for legitimate things, and you can kind of hand wave it away, and I don’t think that would work if you were not focused on the climate-change signal alone. If you were only focused on the fuel effect, I don’t think it would even go to review. They would be like, They didn’t consider climate change. That’s the thing we care most about.
I think it’s good that they’re now publishing reviewer comments and retorts, but that is common, absolutely conventional practice. You do what you think you have to do, and you don’t do what you think would take too long or bog it down or end up with a paper not being published.
There’s two different stories there, though. I mean, you write: “To put it bluntly, climate science has become less about understanding the complexities of the world and more about serving as a kind of Cassandra” and that this “distorts a great deal of climate science research. It misinforms the public and it makes practical solutions more difficult to achieve.”
The argument you make in your article is that this is due to confirmation bias and the desire for splash. And the argument you’re making now, which is different, is that the methods on the climate signal are much better developed. That it’s a much clearer thing. It’s what everyone cares the most about, for understandable reasons, and if you’re a researcher, it’s the lowest-hanging fruit, so it’s the easiest and most pressing thing to focus on. But that’s very different from this being about confirmation bias or a tendency toward catastrophism — it’s about researchers trying to make the most of the limited time that they have.
Yeah, I don’t think that they’re that different. I think that the methods are less developed for these other causal factors because of this climate obsession. We know the most about the climate signal, again, because the data sets and the infrastructure are all designed around the climate signal.
So it’s very easy for me to get estimates of the temperature change since the pre-industrial era, and we have all these models, and it’s all at my fingertips. It’s easier, it is the low hanging fruit. But you can imagine a world where in the fire science community, there are resources and databases estimating historical ignitions or other data, like, “this is our year-by-year estimate of fuel loads from 1850 to the present.” Then, that would be the low-hanging fruit. You would have potentially a totally different feeling for what comes out the other end, or what is reported in the media, if those data sets existed.
But the other side of that story is right here, which is that the peer reviewers ask you for that other stuff, and you say it’s too hard. You said it’s “very difficult.” And the other thing you said is that, “Our study shows that large-growth days are predictable using our predictors despite having no other information.” My interpretation is that this leaps out of the data even if everything else is moot.
The predictors include fuel characteristics. There’s nothing in the paper that does long-term manipulation of those, like the temperature or other variables. The models do know certain fuel characteristics, but those have gotten much more sophisticated in the current version of the models.
But back in 2019 or 2020, when you started the wildfire paper, you actually didn’t know about the role of ignitions or vegetation or forest management in driving wildfire growth. You would assume, as I would assume, that these are important factors, but you didn’t know about them. You chose to focus on climate first.
Yeah.
And then you were told to go back and look at the other ones, which you eventually did — that’s what you’re doing now. But at the time, you were like, that’s very hard, so we’re doing this one first. So my question here is: Isn’t this just science working, then? Is that really so scandalous? You picked the highest salience trendline first and then, having found that, went to go study other things.
You could say that that’s science working, but I think that what would happen is that when we start to dilute the climate change story, it’s not a Nature paper anymore. It's not a high-profile paper.
I’m not throwing all of science or all of climate science under the bus. I’m saying, the incentives are aligned to get this exclusive billing in these highest-profile papers and that skews the overall public impression of how large the climate signal is. So yeah, we could go publish another paper, but that wouldn’t have nearly the splash or penetration into the public as this one would have.
I am not sure that's true. I’m also not sure that it seems like nobody has done this vegetation work that you're doing. And when you publish that, it seems like it will be a very important methods paper — and methods papers get cited in some ways more than the high-impact stuff.
I don’t see a story, really, or a narrative getting into The New York Times about how — or especially the Guardian, or someplace like that — about how something other than climate change could be the dominant driver. Maybe I’m wrong, but that doesn’t seem like it would be nearly as likely as focusing on the climate change thing exclusively.
Again, I’m not sure that’s true, but I think that gives away the game a little, because if you’re a researcher trying to publish work that will be highly salient to the public, of course you’ll focus on climate change — the public already cares a lot about climate change. And the public is fundamentally right to care. I even think this whole process has sold your own paper short: If climate change is contributing to these rapid wildfire growth events, that’s a very important finding! Even if it’s not fixable in the near term with emissions reductions. Of course the public cares about it.
There’s a lot in your criticism that suggests certain kinds of analysis are “discouraged” or that certain kinds of questions would not have made this a Nature paper. And I understand you’re just trying to get past the review stage, but the process that was set up to edit Nature did tell you this, and you argued against it. So it’s a little duplicitous to turn around to the public and say, Well, I was only arguing against them because of the incentives.
I realize you have to publish. But when the peer reviewers told you to look at these other factors, you were like, “Oh, it’s very difficult, and climate change is so important that it’s worth pulling out this signal anyway.” And now to the public, your meta-interpretation of your own paper is like, “I wish I had been able to focus on this other stuff.”
But we got it through. Reviewers and editors could say, “No, this is ridiculous, you can’t focus exclusively on the climate change signal.” And they could do that with everything — with yields, with deaths, with fires, with floods, with GDP.
But what I’m saying is that from reading Nature, from reading Nature Climate Change, from reading Nature Communications, from reading Science, from reading Science Advances, I know as a researcher that this is not going to stop my paper. This is what everyone does. So when someone says, look at other factors — which is always what you get in reviews — you just learn to say, that’s not in the scope of this paper, but we can do it potentially in future work. You come up with a reason why you’re not going to do it in this paper, because you need that paper published so you can go on to the next one.
I hear that, but it does feel dishonest to turn around and interpret the paper to the public in this way. You’re not saying, in either the Twitter thread or Free Press article, that “Frankly it was very hard to pull out all these other factors, so we didn’t do it in this paper, but you should know that other factors matter.” What you’re saying is that the entire publication set-up is geared toward producing articles finding a catastrophic climate impact.
I understand you felt you had to just get past the reviews, but you can’t tell a high-impact journal that what they want is too hard, then turn around and tell the public, “Simply put, the incentives didn’t let me do this!”
Yeah, I understand what you’re saying, but what I would say is that there was no pathway for the reviewer to say, “Consider these other factors,” and then for us to do that, and for it to become a Nature paper. There’s no off-ramp there where you say, “Okay, good idea, we’ll go do that.” Your response has to convince the reviewer that you don’t need to do that. That would be a potential way to reform things — if you were able to hold papers in limbo. As a researcher needing publications, and wanting as many high-profile publications as you can, you have to argue with the reviewers and do whatever’s necessary to then get it over the finish line. My larger point is that it’s still the biases of the editors and reviewers that allowed the hand-waving response to get through.
But if you thought that they had made such a valid point that it torpedoed the paper, you could have pulled the paper. You did have options.
Yeah. I could have pulled the paper.
But that’s my point. I’m trying to improve science. And I’m saying, from the inside, that you don’t pull your paper, that’s crazy. You would never do that. Your incentives as a researcher are not aligned with the best knowledge generation for the public. You can say, okay, a more pure scientist would have done that, and shame on me for not pulling the paper, but I don’t think that’s fair, because I think 99.99% of people in my position … this is our job. To argue with reviewers and get papers over the finish line. Especially when nothing is actually wrong. Nothing is explicitly wrong here.
You’ve said that this “wouldn’t make it a Nature paper.” But there was a commentary in Nature Climate Change just yesterday that argued against the assumption that the future will be worse than the present and said that we shouldn’t paint an increasingly dire version of the future.
These articles don’t always get covered in the press, but is this a problem with science, or is it just a problem with the press?
Well, I think that there’s definitely a press issue, and that's a whole separate issue. But yeah, I’m highlighting what I think is a basic foundational science portion of the issue. And there are feedbacks between the two. I think 10 years ago, or 15 years ago, I don’t think there was this alliance between certain celebrity scientists and certain journalists, like — “You journalists will be the PR for my study that just came out, and I’ll shape my study to be the most salient for writing about.” I think that’s a natural tendency for highly motivated and ambitious people.
This question of an “alliance” is an interesting one. Because there’s a very understandable story about the incentives you’re describing. I think it’s good to point out that climate change is where we have the most developed methods, it’s what the public cares about the most, so it’s what you write about first when you start to take a crack on this wildfire problem. And if there are negative consequences that follow from those incentives, that’s quite understandable. But when you talk about an “alliance,” it suggests that there’s this malevolent or highly self-interested conspiracy —
I don’t think that. Just as an example, though, you are being an excellent journalist right now. You are really questioning me, and shaking me down, and questioning all sorts of things that I said. But I did a bunch of interviews on the Nature paper and I got nothing like that. No one ever asked challenging questions. They do this kind of, You are the scientist, you are the arbiter of truth-type thing.
So I notice a difference here — when I am in this role of warning about climate change, I am treated very differently than when I’m in this role of challenging that. And maybe you always do this with all the climate stuff that you cover, but I think that the people on the climate beat should not just be these megaphones for researchers, they should challenge them.
When those reporters were asking you questions, were you going out of your way to say, “Oh, fire management is really important. Actually, how we manage this is really important.”
Yes, I was, actually.
And did it make it into the stories?
Well, the vast majority of the stories I was not interviewed for. There was an NPR story and an LA Times story, and I think my quotes got in there. So that’s good.
That’s good to hear. I’ll be honest that when I saw your Free Press article yesterday, I was pretty taken aback. And it’s because I was looking forward to calling you as an expert during a big wildfire event. But when I call a researcher about a paper, I need them to be honest with me and tell me their full views about it. But if someone says A to me and to my readers, and then they turn around a few days later and say, In fact, I really believe B — I was only saying A because of the incentives, it gives me pause. Because why should I trust you?
I totally understand what you’re saying. Part of this is certainly a confession of personal fault, but in my tweet thread and in the press release, I made sure to emphasize everything I thought was important. If we had wanted it to go viral, I could have emphasized a [high emissions scenario] and made the title that climate change radically alters wildfire growth. But the title was about using artificial intelligence. And in all the interviews I did, I emphasized what I think is the most scientifically interesting part of this, which is that if you put climate change on different wildfires, you get different responses because forests are nearer or closer to these aridity thresholds. I feel no regrets whatsoever about the publicity I did for the paper.
I want to ask you about a few of the ways that Twitter and Free Press readers have interpreted the criticisms you’re making. One reader says you “inflated numbers” in order to get the article published. Did you do that?
No. That sounds like something totally different from what I’m talking about. That sounds like you go into a spreadsheet and change the numbers or something. I’m talking about a much more subtle thing, about emphasis.
Got it. There was a Free Press commenter who walked away from your story and said that you “distorted the facts and lied in order to succeed.” Do you think that’s a fair read?
No — “lied in order to succeed”? No. There’s nothing out of the ordinary or unconventional about this paper. I’m saying the conventions lead to an incomplete picture overall.
Do you understand why people walked away from the criticism you were making with those ideas?
I suppose if you just understand that people are reading things pretty quickly and lightly, but I don't think if you go word by word through my piece, you would see that.
Your piece does allege that the public’s being misled by the incentives here.
I think the overall picture being painted emphasizes climate more than it deserves.
Were your co-authors aware that you were going to use the paper in the way that you did?
I gave some of them a verbal heads up, but they did not see the piece, so this is, 100%, I own it. They are not involved and should not be accused of anything. I wrote the paper, I did the entire analysis, and this is my thing in terms of the opinion piece as well.
Is this really about climate change, or is this really about something that we know happens in every scientific discipline, which is that the most novel, eyebrow-raising papers get into high-impact journals and get the most press coverage? We don’t read about the bulk of the experiments that happen at the Large Hadron Collider, either.
It’s hard for me to judge completely about other fields since I’m not in them, but I would say that the splashiest papers in a non-politicized field are the ones that would go against conventional wisdom or the state of perceived knowledge in the field. I would say it’s the opposite in climate science. It’s all about emphasizing the climate-change signal or the climate-change impact. A lot of climate researchers feel like it’s essentially their job to raise alarm about climate change and emphasize the emissions reduction component.
But climate change is inherently politicized, right?
Yeah.
And the history is that climate scientists are not who politicized it. The reason it’s an especially touchy field is because there was a 15-year effort to emphasize every single error bar, every quibble, every well-founded scientific statement of doubt, to convince the public to doubt the climate-change hypothesis.
I take your point. I don’t know if it’s fair to say — I mean, are you saying that they could be politicized? Because I think it’s still politicized in general. There’s all these values that are taken onboard [by scientists]. There are traditional environmentalist values that impacting Earth is inherently bad, and so we should look for and highlight these bad things. That’s not explicitly, necessarily a partisan, political thing, but it’s an ideology that’s running in the background. It’s different from an ideology that says warming is this much, but it could be overwhelmed or offset by this technology.
I’m not someone who sees the fossil-fuel industry in every failed climate policy. But just as a matter of historical fact, from 1990 to at least 2005, there was a well-funded, highly organized effort to publicize every point of scientific caution to sow doubt about climate change. And yes, climate science was associated with environmentalism through the 1970s and ’80s, but had there not been an organized effort to play up every morsel of doubt in the literature, climate science wouldn’t have been politicized in the same way.
I’m not a historian of that. I take your point.
You’re describing a set of incentives that push researchers to look at climate change first. But if you go to a climate science conference, it’s really different, and you do in fact see ideas in climate science get rolled back over time. Like, in the 2000s, we thought climate change played a much larger role in hurricanes than we do now.
I think that’s a really important point, but I don’t think that that is at all what the public thinks or that’s communicated to the public. I guess if that’s an empirical question, you could do the polling on it, but it seems like, now, to me, it seems like every extreme weather event is covered through the lens of climate change.
Look at Canada’s explosive wildfire year. On the one hand, it’s so out of line with historical norms, it’s hard to see how there isn’t a climate change signal there — on the other hand, it’s so out of line that it’s hard to say what’s going on. So what do we want the public to do here? Because I don’t think every member of the public will be an expert on exactly how climate change drives extreme weather. So is the American public, writ large, sufficiently concerned about climate risks as I understand them? Probably not. Is 5% of the public too concerned? Maybe, yeah. But I also don’t know what’s going to happen in the future.
I’d have to look at the polls. I remember seeing that 42% of young people have some form of climate anxiety every day, which I think is filtered through social media reframing all extreme weather through the lens of climate change, bringing in a very apocalyptic view that I think is incongruent with the data. You think 5% is too concerned? I don’t know. You’d also have to bring in policies, and costs of policies, and the net overall costs and benefits of energy systems and agricultural systems and everything, and that’s just a much more difficult thing to get right.
But the leading candidate for one of the two parties also says climate change isn’t real. So my question would be, are there malicious actors and institutions here? Or is this just an extremely hard, very difficult thing to get right?
I do not think that there are malicious actors and institutions. I think it’s much more just the cultural milieu of institutions. The problems that I’m highlighting, I think there’s just a groupthink that develops, and people not wanting to rock the boat too much, and everyone kind of being on board knowing that, Well, the good side is to raise alarm about climate change and to reduce emissions, and the bad side is to do anything that would be in the other direction of that. And I think that you can make an argument for that, but I think that that ends up distorting actual scientific output.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Rob sits down with the Josh Parker, head of sustainability at America’s world-leading chip designer.
America’s tech companies are transforming the electricity system — building entirely new fleets of new solar panels, batteries, and gas turbines — in order to power what are essentially warehouses filled with cutting-edge chips.
Almost all of those chips are made by Nvidia. On this week’s episode of Shift Key, Rob is joined by Josh Parker, Nvidia’s head of sustainability. They discuss the climate and electricity impacts of artificial intelligence, why Josh is incredibly bullish on AI’s ability to cut carbon emissions and whether it has done so so far, and the company's work with clean energy and fossil fuel companies.
Shift Key is hosted by Robinson Meyer, the founding executive editor of Heatmap News.
Subscribe to “Shift Key” and find this episode on Apple Podcasts, Spotify, Amazon, or wherever you get your podcasts.
You can also add the show’s RSS feed to your podcast app to follow us directly.
Here is an excerpt from their conversation:
Robinson Meyer: So Heatmap has been tracking what, to us, has been a very sudden and shocking rise of local pushback against AI data centers. And of course, this has become a larger meme over the past few months, as it’s gotten more attention. For instance, we think about 50 AI data centers or data centers broadly were canceled last year after facing local pushback. And we think more than 50 have already been canceled this year.
Are you seeing that at all at Nvidia? I mean, it doesn’t look — your quarterly results came out yesterday and they were, they absolutely blew out expectations. And so evidently it’s not affecting demand yet. But do you hear it from customers? Is this affecting Nvidia’s business at all? And how do you think about it as a risk going forward?
Josh Parker: So I’m aware of the sentiment, the paranoia around AI, mostly on a personal level because I see it on social media like other people do, as well. I’m not aware of any direct impact on our sales, so I can’t comment on that. But what I will say is, I do think it’s particularly tragic, because this technology has the potential to be the most beneficial, both for environmental goals and for social goals — so things like education and health care, and kind of across-the-board social issues benefit from AI, as well. And the concerns about AI, a lot of them are based on either erroneous data or old data. And I worry that some people don’t fully understand the net impacts, the positive as well as the negative of AI.
Plus, we have the uphill battle of, it’s really hard if the data center is being built a few miles down the road, to tie that data center — which, they don’t always look beautiful and things like that — to the benefits that the whole world is going to get from AI. So if — obviously not promising this — but AI could unlock cancer cures or cures to other diseases, and we’re seeing trends in the direction of cures and treatments and drug discovery and so forth. But it’s really hard for us as humans to draw a line between the infrastructure that we see down the street, and especially the speculative, the moonshot benefits. But even the more fundamental ones, like the benefits and productivity that we’re seeing in potential for wage growth and education and so forth, even though it’s hard for us to draw the line between the infrastructure.
So it’s understandable, but I do think it’s tragic. And I think it’s our responsibility in the tech industry to help people see the bigger picture and to address people’s concerns head on about environmental impacts and social impacts. Because the data really does demonstrate that, by and large, these data centers are pro-sustainability. They don’t have the impacts that most people are concerned about, and they’re manageable. And most data center operators are trying to operate them in a sustainable way.
You can find a full transcript of the episode here.
Mentioned:
Previously on Shift Key: Data Centers Are Creating a New Kind of Battery Monster
Previously on Shift Key: A Skeptic’s Take on AI and Energy Growth
From Heatmap: Exclusive: Local Opposition to Data Centers Explodes in 2026
This episode of Shift Key is sponsored by ...
Heatmap Pro brings all of our research, reporting, and insights down to the local level. The software platform tracks all local opposition to clean energy and data centers, forecasts community sentiment, and guides data-driven engagement campaigns. Book a demo today to see the premier intelligence platform for project permitting and community engagement.
Music for Shift Key is by Adam Kromelow.
This transcript has been automatically generated.
Subscribe to “Shift Key” and find this episode on Apple Podcasts, Spotify, Amazon, or wherever you get your podcasts.
You can also add the show’s RSS feed to your podcast app to follow us directly.
Robinson Meyer:
Hello, it’s Tuesday, March 26, and the second unofficial day of summer here in the United States. yesterday was the first. And at least as of when markets closed last week, the chip maker NVIDIA was the world’s most valuable company. It currently has a market cap of around $5.3 trillion. The next biggest company, Alphabet or Google, is worth $4.6 trillion. Just last week, NVIDIA released its financial results for the first quarter, and it was another blowout. It was expected to generate just under $79 billion in revenue. Instead, it delivered $82 billion. That’s up 20% from the previous quarter and up 85% year over year. NVIDIA has now beaten Wall Street expectations for 14 quarters in a row.
Robinson Meyer:
I go into all of this, not because Shift Key is a technology business podcast, we are not, but to illustrate the centrality of NVIDIA to artificial intelligence and I think to the broader American economy right now.
Robinson Meyer:
NVIDIA produces the physical infrastructure behind the AI and data center boom. And since that boom is the biggest story in electricity, climate, and even energy, NVIDIA is probably the most important company to energy, electricity, and climate too. After all, America’s tech companies are building solar panels and batteries and gas turbines specifically to power NVIDIA chips. When we talk about data centers being built across American communities, we’re talking about warehouses holding NVIDIA chips. Utilities are tripping over themselves to power and have access to warehouses powering NVIDIA chips. NVIDIA chips are where America’s dominance of the global software and AI industries meets America’s physical economy. That is the actual electrons, copper wires, gas molecules, and infrastructure that runs through America’s towns and cities. So I’m excited to welcome to the Shift Key today, Josh Parker. He is NVIDIA’s Head of Sustainability, a role he’s held since 2023. Before that, he was Head of Sustainability and Assistant General Counsel at Western Digital. Josh and I had a good conversation last week. We talked about why he thinks AI is a net good for climate change, about whether AI and NVIDIA are already cutting emissions on the power grid, and about NVIDIA’s work with clean energy companies, as well as fossil fuel companies. It’s a very interesting conversation. I learned a lot from it. I’m Robinson Meyer, the founding executive editor of Heatmap News, and it’s all coming up on Shift Key.
Robinson Meyer:
Josh, welcome to Shift Key.
Josh Parker:
Thanks, I’m thrilled to be here.
Robinson Meyer:
So you joined NVIDIA in August 2023, which was right a few months after ChatGPT came out and completely changed the AI conversation. What did you walk into at the time? And where was the internal conversation around sustainability and climate at that moment in NVIDIA?
Josh Parker:
It was a really unique and wonderful time to join NVIDIA. You know, the company was just doing amazing things. the whole world was starting to wrap its head around the fact that AI was useful and was finally here in ways that would transform the world, transform the economy, and really our existence. And so the timing was fantastic for me, really thrilling just based on where the company was, what it was doing, and the whole conversation around it. The sustainability conversation was one of growing interest at NVIDIA. Jensen, our CEO, really has this vision of technology helping to solve the world’s biggest challenges. And sustainability is, of course, one aspect of that. Things like climate change and materials resources and water conservation. And he believed that AI had a very critical role to play in sustainability in the near future. And the company was looking to expand its sustainability program and efforts. And so I was very fortunate to come in at a time when the company was really trying to accelerate that program and find new ways to use tech for good and also to be a responsible organization ourselves.
Robinson Meyer:
How do you think about NVIDIA and sustainability today? What are the goals that you have? Because obviously at this point depends slightly on the day, but recently it’s the world’s most valuable company. It’s driving this enormous infrastructure boom. NVIDIA provides the physical infrastructure of the AI boom. And so to some degree, it’s an every sector of the economy story. And I wonder, given the company’s enormous importance right now, how do you think about its sustainability goals and what you focus on?
Josh Parker:
NVIDIA is a pretty unique company just across all the metrics. The culture here is very unique, very dynamic, and we could get into that and have a whole podcast on it. But the sustainability space follows that same pattern. We have a very unique approach to sustainability, I think, based on NVIDIA’s role in the ecosystem.
Josh Parker:
One of the first things that I did when I joined NVIDIA was to start some analyses.
Josh Parker:
Some incredible third-party validated products carbon footprints for some of our high-volume projects to figure out what does the data show us about where our lifecycle impacts are. So if you look in gaming or in AI or 3D modeling, pro-visualization, what are the kind of soup to nuts, cradle to grave hotspots for emissions in particular and then other impacts as well? And when you look at that, you very quickly realize that NVIDIA’s direct footprint, and this is something most people would understand just conceptually, NVIDIA’s direct footprint is a tiny, tiny fraction of the total lifecycle impacts of our products. So while traditional sustainability programs, especially tech companies that involve manufacturing and perhaps downstream use as well, really focus on their own footprint, if we focus myopically on our own footprint, we’re missing the forest for the trees. So very quickly realized that Jensen’s vision about sustainability and about AI’s potential to impact sustainability issues was much, much more significant than NVIDIA’s direct impacts through our operations. And so as a result of that, we’ve been focused from day one, really, on trying to unlock applications of AI for sustainability and to work with our value chain partners, both upstream and downstream.
Josh Parker:
To decarbonize, to manage impacts, et cetera, across the value chain. So it’s a lot more outward-focused sustainability program than most, which I think makes a lot of sense based on where NVIDIA sits in the ecosystem.
Robinson Meyer:
And can you talk a little bit maybe about why that is because i think what many listeners i expect will understand but just to be clear here nvidia designs its chips and it operates them as well but it doesn’t actually produce the chips the chips are usually produced by tsmc or another outside chip fab and so I guess from your standpoint, then, that makes these external projects especially important. But like, where did the emissions, I guess, in NVIDIA’s world come from? Do you focus on emissions as, you know, a key metric here?
Josh Parker:
Yes, our top issue for sustainability since I arrived at NVIDIA has been emissions and climate change. So that has been the top focus for us. And yeah, if you look at the value chain emissions and those product carbon footprints that I mentioned, we’ve published summaries of those that are cradle to gate. So they start from the very beginning of the value chain and end kind of when we ship our products to our customers, because we don’t have as good a visibility to how our customers are using our platform. But we are, as a company, historically, it’s accurate to say we were a chip design company. Nowadays, we’re more of kind of a platform infrastructure solutions company, but we are focused very much on the design. So on the AI side, we do very advanced networking. We have CPUs, GPUs, data center architecture. We co-design things like cooling solutions for data centers, and we publish reference designs for those. And then we work with manufacturing partners, contract manufacturers to actually build the systems and then to sell them. And then we do operate some data centers, but most of our business is really selling the tools, the infrastructure to the companies that go out and build great things with that infrastructure.
Robinson Meyer:
What’s the most important metric to focus? I mean, we were talking about emissions, but in terms of understanding kind of NVIDIA sustainability goals, what’s the most important metric to focus on?
Josh Parker:
I think at a moment when AI is growing rapidly, transforming the world, the most useful metric is one that takes into account both the footprint and the handprint. So it takes into account the impacts as well as the potential offsets, the benefits, the transformational impacts down the road. Now, consolidating that into a single metric is really difficult, but there are some studies that have tried to look at least the net impact on greenhouse gas emissions of AI broadly. So that’s, I think, the best indication of, is AI a hero or a villain or somewhere in between in terms of climate change and greenhouse gas emissions in particular? And the very rapidly growing consensus is that AI is most likely to lead to net emissions reductions, especially if it’s deployed broadly. So organizations like the International Energy Agency, World Economic Forum, Boston Consulting Group, Grantham Institute, have all come to that conclusion that AI, because of its transformational impacts on other sectors in particular around energy efficiency and so forth, is poised to drive net emissions reductions. So if I were to pick a metric, I would say, what’s the net impact on emissions that AI is creating? And it’s really a positive one if you look at those studies.
Robinson Meyer:
Can you... So... I think that this is like get set to some degree, the question that I want to talk about while we have your time, which is that there’s enormous focus on the on the energy use from AI, right? And of course, the energy use from chips. And we can talk about chip efficiency and what NVIDIA is doing there. And I think it’d be good to talk about it. But it does seem like to kind of step back that we are in this moment of massive infrastructure investment in AI. And that infrastructure investment is going to happen. And regardless, at this point, I think it’s just AI is too valuable. It’s too obviously useful for that infrastructure investment not to happen. And what we track at Heatmap and we look at data centers get built across the country and we become aware, for instance, that there’s a lot of off-site, you know, behind the meter gas being built to service these data centers. Obviously, there’s going to be a surge in electricity demand and there’s ways
Robinson Meyer:
in which electricity demand increases can be good. But just as we think through the next five years, given that at this point, the AI investment boom is happening and to some degree, you know, the AI story is a foregone conclusion. What needs to be true for AI to have been good for the climate or for NVIDIA’s efforts here to have been good for the climate?
Josh Parker:
The biggest variable in that analysis of what’s the net impact of AI is really, again, if you look at those studies that I mentioned, including the International Energy Agency, is how broadly we apply it in the near term. So yes, the infrastructure is getting built, it’s getting used, And contrary to what most of us consumers conceive of as AI, the vast majority of the really useful cases of AI is not the chatbots that you’re engaging with. It’s not the dogs surfing in Hawaii videos and photos that people create in their spare time. It’s the commercial applications where AI is saving energy. It’s saving material resources and so forth. And that infrastructure is being deployed for that purpose, in addition to the chatbots. And the real opportunity for us is to say, okay, we’ve got these amazing models. You’ve got Claude, you’ve got Gemini, ChatGPT, X.
Josh Parker:
They’re really, really powerful and obviously just growing in capabilities month over month. There’s so much potential there for those to transform manufacturing, for example, digital twins. And we see proof points of AI reducing energy in manufacturing by around 30% across the board if AI is deployed to optimize manufacturing for energy. That happened at one of our manufacturing partners in Guadalajara, Mexico, for example, a 30% reduction in energy. And so the opportunity is, and the risk is that if we build out all this infrastructure and we don’t use it effectively, if we don’t apply the AI to these big problems, then we may miss out on those significant emissions reductions. So what needs to be true, the biggest variable here is, are we taking advantage of what we’ve built? Because the infrastructure, like you said, is being built and it’s being used, but can we deploy it more broadly And can we bring in some of the sustainability-focused organizations to deploy it for good? How do we intentionally use AI for good in addition to the kind of regular efficiency, revenue, and cost-driven allocations that are happening very naturally and have very, very significant gains across sustainability? There are also very purpose-driven applications of AI that can have big impacts as well.
Robinson Meyer:
Do you think that AI by itself increases efficiency where it’s applied in that, you know, if you apply it to manufacturing, for instance, or another one of these industrial uses that it’s going to just increase the efficiency of that process by dint of its application and being very intelligent and finding, you know, ways to streamline processes or skip processes or augment processes that maybe wouldn’t have been considered otherwise? Or does it need to be applied in an intentional way where people say we need to look at this for efficiency or for emissions and that should be our main focus here.
Josh Parker:
So that’s the beauty of the concept of efficiency in free market is that the incentives to reduce costs are really well aligned with sustainability goals of reducing impacts, reducing consumption, and so forth. And so what we are seeing, and I think this will even grow more over time once we get out of this kind of Cambrian explosion of tech innovation that we’re in right now, which is a little chaotic, is that you’ll see optimization of, okay.
Josh Parker:
Using a huge LLM for this problem might be good, but it might not be the best tool for that particular task. Can we use a lighter weight model? And you see tons of innovation in this space. Mixture of experts has been around for a long time. We’re seeing a lot more innovation around how to use more efficient models and target them to specific applications. But the market and kind of customer demands and everything is really driving us. Plus supply constraints, compute constraints are really driving us towards efficiency and to optimize allocation of those resources. And if AI doesn’t end up being the right tool for every task, then it won’t be used there. And we can continue to use traditional techniques. But efficiency does happen to be one of AI’s kind of low-hanging fruits, one of its superpowers that is really easy to unlock and unlocks value immediately across the board. So it is very fundamentally true in general that AI does drive efficiency very, very rapidly in most areas.
Robinson Meyer:
I think what I hear you saying is that a lot of the good that will ultimately come from this build out there will be done from intentionally applying AI to intentional sustainability problems. Is that wrong? Or is it also just the diffusion? I mean, we were just talking about efficiency. So I guess that’s on the other side. But in your kind of first answer, I did hear a sense that a lot of the most important work on sustainability will come from NVIDIA intentionally applying its technology to sustainability problems.
Josh Parker:
I would say that’s important, mostly because it does require us to think about it and to do something. It’s not being driven necessarily automatically by existing incentives and market dynamics. So the market dynamics and the efficiencies that are being driven by that, like a 30% reduction in manufacturing efficiency, it’s really mind-boggling. When you think about we’re concerned about the energy that is being consumed by AI, AI still represents less than 1% of total electricity consumption worldwide. Now, it’s obviously higher in some regions, higher in the United States.
Robinson Meyer:
And it’s about to go up a lot too, is the other side.
Josh Parker:
No, it’s expected to double by 2030. So it’s growing very rapidly. But if you think about AI’s existing footprint, again, less than 1% of global electricity right now, even if it doubles, doubles again, doubles again, it’s still going to be a small share of global electricity. If, as we’re seeing the proof points for, it can reduce energy in much, much larger energy consuming sectors like transportation, like buildings, like industry, which are each in the 20 to 40% range of global electricity, then those savings dwarf AI’s footprint unambiguously. And that incentive is there because companies want to reduce costs. They want to reduce their energy consumption, especially when we’re in this environment of energy constraint, particularly in the United States, the incentives are there. So that is going to happen. I think that’s kind of inevitable because it’s an opportunity. There’s value and there’s sustainability. It’s good for everybody and the stars have aligned. The...
Josh Parker:
Additional piece is applications of AI intentionally for sustainability. And that’s where maybe it won’t happen unless we think about it, unless we try to apply it there. And the potential is just phenomenal. When you think about the way AI is already transforming drug discovery and healthcare and material science, there’s potential in nuclear fusion, advanced fission, geothermal, and carbon capture and storage just across the board. When you add intelligence to these sustainability challenges, you arrive at this wonderful inflection point where we might finally have a technology that can sufficiently complement policy to help us actually prevail on some of these sustainability challenges, help us to kind of reverse things and make progress that we otherwise wouldn’t have the opportunity to do.
Robinson Meyer:
There’s two types of AI that we’re talking about here, and I wonder if we can disambiguate them a little bit, in part just for my understanding. So there’s the large language models, which I feel like are the charismatic megafauna of AI. This is Claude, it’s ChatGPT, it’s Grok. Those are the models that I think people are most likely to have experienced when they think of AI. But there’s also this whole other set of AI applications, which I feel like you’ve alluded to, applying it to manufacturing, applying it to drug discovery, applying it to energy. And my sense is that type of ai it doesn’t look like Claude or it doesn’t look like ChatGPT it might have the same kind of organic structure where it was trained on a large data set and kind of allowed to self train itself on that data but it doesn’t have the same interface it’s much more kind of machine brains than maybe the LLMs of the world and to the extent you could share this data to what extent is ai demand and nvidia’s demand and energy use coming from the LLMs of the world like claude and Grok and ChatGPT versus these other AI applications.
Josh Parker:
It is true. There are very different applications of AI depending on the sector, and the consumer-facing chatbots that you see are one small use case and not where you see the biggest opportunities for advances in sustainability through AI, of course. Things like digital twins, for example, and that’s a really interesting marriage of NVIDIA’s expertise in 3D modeling and AI. And that is a very fundamentally valuable concept and technology for things like the manufacturing optimization that I was talking about.
Robinson Meyer:
You build a digital simulation of a real-life factory or physical space, right? Right.
Josh Parker:
That’s right. Yeah. And they become, it’s a lot more than what it sounds like at first blush, just a 3D rendering of a building. You actually can simulate robots going through this factory, simulate the airflow through the factory and the cooling system and all of the impacts of various factors on it. So it’s very complicated, and the emulations enabled by the AI really make the technology as valuable as it is today. That’s one example of something that is obviously not a chatbot that is fundamentally just extremely valuable when it comes to sustainability applications of AI. But there is actually substantial overlap. So when you see Anthropic training Claude Opus and devoting all of these resources to training that huge LLM, so many parameters, and same thing with ChatGPT and Gemini.
Josh Parker:
Those very large, large language models end up being really useful tools for helping us create more bespoke, lighter weight custom models as well that can do other things. So the multimodality functionality of modern day LLMs is just going through the roof. And the result of that is that these foundational models become even more valuable for lighter weight, more tailored applications of AI. So it’s true that the actual application of them in other areas probably won’t be the exact same model that was the huge foundational model that you started from, but through distillation and other techniques, you may end up using that as the basis for one of those other models.
Robinson Meyer:
There’s been a lot of excitement and i believe nvidia has invested in a number of companies or at least emerald ai companies that are look looking at whether data centers can be flexed up or down to meet the grid needs of the moment so instead of data centers simply being a huge energy suck on the grid they could modulate their usage and their they could modulate their compute and therefore their energy usage to kind of meet the grid’s needs i know nvidia is invested in this Can you give us a sense of where does that project stand right now in between, say, white paper and deployed scale?
Josh Parker:
So we are actively deploying this technology at our data centers. We’re building a data center right now in Virginia that will come online, I believe, later this year, that is, we think, the world’s first entirely flexible data center for AI. And we do see this as the future because it leads to a situation where we’re making better use of existing energy resources. And this is something that’s really, I think, underappreciated. And it might be a little nuanced for most people who don’t follow this to appreciate, but the concept of AI data centers becoming grid assets is really powerful because they’re being deployed rapidly. They’re using a lot of energy. And if they end up being good citizens of the electrical grid, then that can have actually a profound reductive impact on energy prices for retail consumers like you and me. The concept here is you have a grid that is built for peak load. So in the middle of the summer in Texas, when everybody’s running their AC units and you’re consuming the maximum energy that the system can deliver, that is what the system is designed for. So when you’re not at peak load, what does that mean? That means that all of those resources that you’ve built for the peak load are being underutilized.
Josh Parker:
This leads to the conversation about smart grids and virtual power plants, where I think everybody that looks into this closely wants to get where we’re saying, okay, how can we be more flexible, both primarily with our demand, but also on the variable generation side, how can we make better use of wind and solar that aren’t for power sources?
Josh Parker:
Data centers play a huge role in that, especially as they become a higher percentage of electricity consumption in the United States. If a data center can say, okay, I’m in Texas, I’m in the ERCOT region, and it’s a hot day in late July, everybody’s running their AC, I’m going to curtail my electricity draw slightly for a few hours until the system can get back to below peak load, and then I’ll ramp back up. That ends up becoming a net asset because you’re able to soak up the electrons when they’re more available and then reduce your load when they’re less available, which means we’re paying money for electricity that is otherwise being unused with existing grid infrastructure. So it’s fantastic for consumers. It’s fantastic for the energy sector. And it’s good for data centers because it means we can build them sooner and take advantage of existing resources. And one last comment on this, you may know that the concept of Emerald AI and this data center flexibility ties back to a study last year by Tyler Norris at Duke University, who said there’s 100 gigawatts.
Robinson Meyer:
And a Shift Key listener, I believe.
Josh Parker:
Yes, as am I. Yeah, I just want to get that in there as well.
Robinson Meyer:
Thank you.
Josh Parker:
Yeah, no, it’s fantastic work that you do, Shifky and heatmap. So 100 gigawatts, that is a ton of energy that could be accessed if we just ask data centers to be flexible for 1% of the year. And so that’s the concept here. It’s making the energy sector electrical generation more efficient, which leads to lower prices over time and better utilization.
Robinson Meyer:
I think when Tyler’s paper came out last year and when there was the initial wave of discussion about flexible data centers, the thought was that data centers would be flexing their compute, that they would change the operation, the programming, or the level of training that was happening in the data center at that moment to match real-life grid conditions. Since then, the focus has shifted more to data centers flexing how much energy they draw from the grid, but maybe the training itself or whatever compute is happening being more stable. It’s just the question is whether the facility is drawing from the grid or from battery storage that’s on site. When you talk about this data center in Virginia, or when you talk about flexible data centers going forward, are they flexing the compute mostly, or are they mostly flexing their grid use and where they draw electricity from? And sometimes they’re drawing electricity from the grid, and sometimes they’re drawing it from on-site batteries. But most of the flexibility per se is coming from where the electricity is coming from and not how much electricity is being used.
Josh Parker:
It’s really a mix. And where we end up will really depend on what customers the data center is serving, whether it’s a mix, whether they’re being served locally, whether it’s focused primarily on training versus inference. So what we’ll end up seeing is there will be a wide variety, I think, of data centers with different types of flexibility, perhaps, based on the needs of the data center. So if you have a data center that is running critical infrastructure and needs to be available even at peak load, then you may have more incentive to build out a large array of batteries so that you can continue to use that compute even when you’re at peak load on the grid and you can still be a good.
Josh Parker:
Citizen of the electrical grid by reducing your draw from the grid. But there are three different types of flexibility that we’re building into this framework. One of them is what you mentioned with batteries, where you can say, okay, grid’s at peak load. I’m going to use my batteries now temporarily instead. Good citizen. The second is also what we’ve been discussing, which is when you just ramp down your compute, you can say, some of the workloads that I have, I can pause on for a couple hours without deteriorating service or having any significant problems, it’s okay to pause right now. The third type of flexibility that doesn’t get spoken about as much, but that is rapidly developing is geographic flexibility. So if you have workloads that are really vital, but maybe you don’t have the battery storage on site to keep your compute running full steam all the time, you could actually transmit that workload to a different geography. Maybe somewhere in the Pacific Northwest, they’re not experiencing the same heat wave that they are in Texas. And the way a lot of interaction with AI works, that additional latency due to the different geography isn’t a huge factor because there’s already some delay built into the compute.
Robinson Meyer:
So latency is less of a... Is that training or inference that you would move geographically? Like, would you send the inference out to the Pacific Northwest? Or is this, you would actually send a training task out to the Pacific Northwest. And then it doesn’t matter in some ways because training doesn’t happen on a scale that the customer is always aware of.
Josh Parker:
Technically, either is possible. Training, because it’s kind of a large workload, chunking it up into discrete bits and then moving the data to the location where you need to continue the training, does have some additional complexities to it. Inferencing is a little easier to move because it’s smaller chunks, smaller amounts of data. And either one, again, because of the different latency requirements for AI compared to a traditional data center service, are feasible for a lot of workloads. Some inference workloads, the latency doesn’t matter if you’re doing real-time robotics and things like that. You do care about latency, so I don’t want to overstate this. But there’s a lot of inference that can happen where the latency is not a huge issue, and so those types of workloads could be shifted.
Robinson Meyer:
In some ways, the geographic flexing kind of addresses this. But when we talk about flexing compute or flexing grid use and turning data centers into grid assets, I do have to ask, I mean, are data centers getting built in the places where that capacity or that flexibility is useful? Because it often seems like, especially at this point, they’re getting built in places where there’s just energy that’s efficient or profitable to use because compute and energy are so constrained at this moment. And maybe not in the places where, say, that flexibility is useful. Do you see that changing or are we going to go in and maybe make existing data centers flexible in places like, say, the Mid-Atlantic or Texas where that flexibility could be actually useful to customers?
Josh Parker:
Again, I think we’ll end up with a mix. So right now, especially because of the challenges that we see in getting access to energy in the near term, as we’re rushing to build AI, because it’s so valuable and so important to us, you do see data centers being built just where they can get online, where there is electricity available.
Josh Parker:
And you do see increasingly some of these companies bringing their own energy, building new solar farms because they need it, sometimes bringing online new gas. But the good news is this flexibility is available in the future when we need it. And the companies that are bringing their own energy to their data centers, I haven’t heard of any that really want to be off grid. It makes a lot of sense economically and conceptually for data centers to be part of the grid so that they can be assets. They can take advantage of the shared resources, offer benefits to the grid through improved utilization, et cetera, especially with the flex technology. So I think where we end up will be a highly interconnected mesh of data centers that can flex and can transmit data. But we do have some hurdles that we need to cross to get there, especially in the United States. So permitting reform, transmission, of course, the things that we always talk about in the energy sector. This could be the golden moment where there is enough consensus around the importance of AI from an economic development, national security.
Josh Parker:
Scientific discovery, sustainability perspective, that we can find a way to make progress on these important issues and break through some of those backlogs. If we can do that, what we’ll end up with is a smarter grid, more robust economic development, more sustainable outcomes. It really will be good for society generally and help with energy affordability as well.
Robinson Meyer:
So the data center that we were discussing earlier, you said, is set to come on later this year. I think a lot of this conversation about data center flexibility is future focused, is looking at improvements that could happen in the future. Is there a substantive example of using AI on the grid right now to improve the supply side or the overall efficiency of the grid?
Josh Parker:
If you’re asking about kind of the data center flexibility piece, we have run several pilots. In conjunction with Emerald AI in Chicago, Virginia, and the UK to demonstrate that this is viable and it works. I’m not aware of it being implemented fully at a data center yet. I think this Virginia one that we’re building now is going to be the first one that is really built around that concept. But the pilots that we’ve run, the demonstrations have been really impressive. They’ve kind of hit all the metrics that we were hoping to achieve. So we think that it’s been demonstrated conceptually, and we’re excited to see it work in real life with this new Virginia facility.
Robinson Meyer:
So when I think about the AI electricity and AI energy use story, I’m thinking back almost to 2023. I think when AI was first forecast or projected to be a very large user of energy, frankly, from a lot of folks I talked to, including guests we had on very early episodes of this podcast, there was a lot of skepticism. Because if you go back 10 or especially 20, 25 years at the end of the dot-com boom and the beginning of the aughts, there was a lot of fears that electricity, that computers, personal computers in that case, and server farms to a lesser extent, as we called them then, were going to be a major user of electricity across the U.S. And they really weren’t. Those concerns really never panned out. And that’s because the actual chips, the computers themselves, got more efficient. Now, of course, it’s become a big user of electricity. it’s totally transforming the energy system. We’re compute constrained. We’re energy constrained. We’re in a very different moment. And...
Robinson Meyer:
That has put these efficiency gains that NVIDIA has made in its chips in a totally different light. And so NVIDIA has unlocked enormous efficiency gains in recent chips. The new AI chips are far more efficient, I think 95% more efficient than previous generations. But this seems to be contributing to a dynamic like a so-called Jevons paradox where we’re using them more. I wonder how you think about the Jevons paradox and AI and do you think we’re going to get to a point where the raw efficiency gains from AI ultimately do lead to a leveling off of energy or right now are just all those efficiency gains from NVIDIA going basically to just using AI more?
Josh Parker:
So I love Devin’s paradox in this context, because I think it says something really fascinating about the unique moment that we’re in. So absolutely, the efficiency gains that we’re seeing in AI are just astounding. And I’m not aware of any technology in history that has seen the type of efficiency gains, the magnitude of efficiency gains that we’ve seen in AI over the past decade or so. So we’re talking 100,000-time improvement in energy efficiency in the past decade. And the IEA, their estimate, which is actually a little lower than ours, is that on average, we see a 10x improvement in energy efficiency year over year with AI. And that improvement, which means, by the way, if you’re running an AI task now and you run the same AI task in five weeks, on average, it will use half the electricity in just five weeks. Again, aggregate and average if you’re doing the same task.
Josh Parker:
So that is a huge countervailing variable in terms of aggregate energy use by AI. But of course, the reason we’re building out more data centers and we need more energy for them is because AI is so incredibly valuable that even despite those energy efficiency gains, we need more of it. The scaling laws are holding so that more compute does translate into significantly more intelligence. And that intelligence is what is driving value across sectors in so many different areas. So to answer your question about where do we end up, I think it’s very clear based on what we’ve seen over the past couple of years, aggregate energy is growing, that it’s focused on AI. Still relatively low baseline globally again, but it’s growing and we expect it to continue to grow rapidly. Now, the question is, is that a problem? And I think if you look at it, there’s, again, this risk of losing the forest for the trees. On the sustainability front.
Josh Parker:
Do we care if AI uses more energy consumption if at the same time it’s reducing energy in other sectors at a much faster rate? So what we care about with emissions is net emissions. What we care about in energy, it’s actually less clear because sometimes energy growth is actually a good thing for sustainability through advancements in clean energy and so forth. But if you just look at the emissions side, what matters globally is the net. And even if AI grows, doubles, doubles, doubles, and doubles its emissions as well, which I don’t think is the case based on the data, you’ll end up in a world that has emissions reductions because of the huge impacts
Josh Parker:
that it’s having positively in other sectors.
Robinson Meyer:
Is there a current sector, though, where we can point and say emissions reductions are happening on a scale commensurate to the increase in data center electricity use?
Josh Parker:
In the near term, at the sectoral level, I don’t think that’s true. And that’s because we’re not deploying AI rapidly enough. Back to the earlier point about what is the key variable to capturing those emissions reductions. And again, going back to the manufacturing case, that kind of makes sense. Because for the economics of energy efficiency to convince you to tear down your existing manufacturing facility and build a new one that’s optimized, that’s a much harder case. But as everything gets naturally upgraded, as you’re ready to build a new factory, because the old one is ready to come offline, AI is undoubtedly going to be utilized in those circumstances. So over the course of the next decade, we will see entire sectors, I think, driving those net reduction that we’re already seeing the proof points for.
Robinson Meyer:
But it does sound, we are kind of in an interesting moment here where we are making a big infrastructure bet. And I understand why we’re making this infrastructure bet. And it’s kind to be reversible. And we think there’s a benefit on the other side, but we don’t fully know that yet, at least on the emissions front.
Josh Parker:
I would say that’s true, but I don’t think, I haven’t heard any arguments that suggest that the fundamentals don’t compel us in that direction. So again, sticking with manufacturing, but transportation and buildings are similar. If you’re building a new building and you have the option of using AI to manage the HVAC, manage the energy consumption, and you expect a 15 to 20% reduction in your builds, of course you’re going to use it and the economics just work out. So I don’t think it’s a question of if, it’s just a question of how rapidly the AI gets used for those purposes.
Robinson Meyer:
NVIDIA is working with a lot of companies and industries who I think have a very natural and mechanistic interest in improving their efficiency and who are very interested in improving their efficiency. NVIDIA is also working with SLB, which I think of still being called Schlumberger, putting together an AI factory for energy and for conventional energy and unlocking more fossil fuels. And it does seem to me that this is the place where AI could run against some of these sustainability goals, that instead of improving efficiency everywhere, it could cause, in the same way that we’re talking about Jevons Paradox, it could cause a general acceleration and unlock more fossil fuels and unlock more oil and gas and have those fuels be cheaper and have them crowd out the clean energy that I know NVIDIA is also working with clean energy companies too. Can you talk about how your work with SLB fits into the sustainability goals? And it does seem to me, doesn’t it kind of push against this idea that AI applied to every industry is going to make everyone more sustainable and reduce our emissions?
Josh Parker:
Yeah, so that’s a good question. And the truth is, AI really does, back to your original point, drive efficiency very easily across whatever purpose you’re trying to apply it for. So if you want to be more efficient at extracting fossil fuels, it can help with that. Now, where we end up, again, if the important thing is the net.
Josh Parker:
Then we need to look at, okay, is AI poised to accelerate fossil fuels more than it’s poised to accelerate clean energy adoption? And I think the data pretty clearly demonstrates that clean energy is likely to benefit at least as much as fossil fuels, not least because clean energy is already in many cases, if not most cases, the most economic and most secure form of energy that can be used. And then when you layer in things like this growth in energy demand that’s being driven by AI, the companies that build out those AI data centers, by and large, are looking for every clean electron they can find. Their commitments to clean energy are huge.
Josh Parker:
World-leading. And so the demand that AI is creating itself is very much focused on clean energy. That’s what Microsoft and Google and Meta, that’s the type of energy they want. And then you factor in the concepts of smart grids, VPPs, which AI can enable, and the demand flexibility of data centers themselves. That makes variable generation like solar and wind, at least incrementally more valuable relative to fossil fuels. So I think it only accelerates and improves the economics of clean energy relative to fossil fuels. So I think if, you know, agreed, AI can, I think, help fossil fuel companies be more efficient in their operations. But I think the overall demand picture is in the economics of clean energy are driving us unavoidably in that direction.
Josh Parker:
And the last thing I’ll say on this is AI is a fantastic complement to policy. It’s not a replacement. AI is technology agnostic. It helps you be more efficient at whatever you’re doing generally. But if we want policies that drive prioritization of clean energy and things like transmission and permitting reform and smart grids will lead us down that road naturally, then the policies, we should focus on the policies that unlock that feature.
Robinson Meyer:
I agree with that. The current set of companies that are using a lot of NVIDIA’s chips, most of NVIDIA’s chips and are applying AI, especially in the United States, are very focused on these clean energy goals. That’s not true of globally, right? I mean, that’s not true of China. It’s not true of the Gulf states, which I think are the next buyer of some of NVIDIA’s chips. Does this mean when we think about how to regulate AI, focus on keeping it at these American tech companies that have these clean energy goals? Yeah.
Josh Parker:
I’m not our political specialist, so I won’t be able to comment on the geopolitics of everything. But I will mention that I think the trend towards net emissions reductions enabled by AI, to me, looks almost unavoidable at this point, because the technology fundamentally helps us take better advantage of the resources that we have. So even if in the near term, we see an increase in emissions globally due to the build out of AI, I think in the medium and long term, we will end up with net reductions for all the reasons that are covered in those papers that I mentioned.
Robinson Meyer:
So Heatmap has been tracking what to us has been a very sudden and shocking rise of local pushback against AI data centers. And of course, this has become a larger meme over the past few months, as it’s gotten more attention. For instance, we think about 50 AI data centers or data centers broadly were canceled last year after facing local pushback. And we think more than 50 have already been canceled this year. Are you seeing that at all at NVIDIA? I mean, it doesn’t look your quarterly results came out yesterday and they were they absolutely blew out expectations. And so evidently it’s not affecting demand yet. But do you hear it from customers? Is this affecting NVIDIA’s business at all? And how do you think about it as a risk going forward?
Josh Parker:
So I’m aware of the sentiment, the paranoia around AI, mostly on a personal level, because I see it on social media like other people do as well. I’m not aware of any direct impact on our sales, so I can’t comment on that. But what I will say is I do think it’s particularly tragic because this technology has the potential to be the most beneficial, both for environmental goals and for social goals. So things like education and health care and kind of across the board, social issues benefit from AI as well. And the concerns about AI, a lot of them are based on either erroneous data or old data. and I worry that some people.
Josh Parker:
Don’t fully understand the net impacts, the positive as well as the negative of AI. Plus, we have the uphill battle of it’s really hard if the data center is being built a few miles down the road to tie that data center, which they don’t always look beautiful and things like that, to the benefits that the whole world is going to get from AI. So if, obviously not promising this, but AI could unlock cancer cures or cures to other diseases. And we’re seeing trends in the direction of cures and treatments and drug discovery and so forth. But it’s really hard for us as humans to draw a line between the infrastructure that we see down the street and especially the speculative, the moonshot benefits, but even the more fundamental ones, like the benefits and productivity that we’re seeing in potential for wage growth and education and so forth, even though it’s hard for us to draw the line between the infrastructure. So it’s understandable, but I do think it’s tragic. And I think it’s our responsibility in the tech industry to help people see the bigger picture and to address people’s concerns head on about environmental impacts and social impacts. Because the data really does demonstrate that, by and large, these data centers are pro-sustainability. They don’t have the impacts that most people are concerned about, and they’re manageable. And most data center operators are trying to operate them in a sustainable way.
Robinson Meyer:
Josh Parker, so much more to talk about, but we’re going to have to leave it there. Thank you so much for joining us here on Shift Key.
Josh Parker:
My pleasure. Thanks, Rob.
Robinson Meyer:
And that will do it for us on Shift Key today. We’ll be back soon with another episode. Until then, Shift Key is a production of Heatmap News. Our editors are Jillian Goodman and Nico Lauricella. Multimedia editing and audio engineering is by Jacob Lambert and by Nick Woodbury. Our music is by Adam Kromelow. Thanks so much for listening. See you next time.
Plus a startup harvesting energy from roadways nabs a new funding round and more of the week’s big money moves.
Uncertainty may have dried up venture funding for early stage climate, but that doesn’t mean there aren’t still deals getting done — or past commitments now coming to light as funding rounds close. This week, for example, brings early-stage backing for a European startup working to convert wasted kinetic energy from braking vehicles into power at ports, as well as a software company helping utilities visualize and manage the increasingly complex electrical grid. Meanwhile, nuclear company Deep Fission proved that the private markets aren’t the only game in town — after going public via SPAC, it’s now planning to list its shares on the Nasdaq stock exchange.
There’s also some promising news for companies looking to scale up, with thermal battery company Antora turning on its first commercial plant in South Dakota this week. That project was made possible in large part by backing from one Australian billionaire. But there’s also S2G Investments, which last week closed a $1 billion fund focused on growth-stage companies and will perhaps help more climate technologies reach that critical commercial milestone.
Every day, hundreds of millions of vehicles travel the world’s roads, converting fuel into motion and exerting mechanical force on the roads’ surface. Much of that kinetic energy is shed as heat when a vehicle throws on the brakes to navigate curves, intersections, ramps, and traffic signals. Austria-based startup REPS plans to capture some of that wasted energy, raising $23.6 million to “turn roads into power plants” by embedding hydraulic plates into road surfaces in braking zones, converting a vehicle’s momentum into clean electricity.
The mechanism is straightforward: As cars and trucks drive over the plates, they compress hydraulic cylinders built into the system, generating pressure that drives an onsite generator. The resulting electricity is routed to on-site battery storage systems, where it’s put to use powering on-site operations or feeding directly back into the local grid, turning high-traffic roads, ports, industrial sites, and other logistics hubs into their own small power sources. The company claims that capturing the energy lost through traffic could account for about 5% of global electricity demand, at least in theory.
REPS isn’t the first to attempt this form of so-called "energy harvesting,” but it says past efforts have failed due to the inferior efficiency and durability of existing mechanical energy converters. The company says its proprietary system, however, can operate for over 20 years. It’s already got one commercial system up and running in the Port of Hamburg, and says that if it were to install hundreds of such systems around the port, costs could be recovered in under four years. Now the startup is engaging with ports around the world and looking to build installations in other logistics hubs and cities.
At the end of last year, I identified Deep Fission, a startup looking to build small nuclear reactors inside underground, water-filled boreholes, as one of the wackiest recent bets in climate tech. Now the company has announced plans to go public at a target valuation of roughly $1.7 billion, seeking to raise $156 million in the process. Its thesis is that placing car-sized, 15-megawatt reactors about a mile underground could dramatically reduce both costs and safety risks. The surrounding rock would effectively serve as a natural barrier and containment vessel, negating the need for many of the bulky structures typically required to house reactors and prevent radioactive leaks.
The planned Nasdaq listing comes less than a year after the company’s somewhat unusual SPAC merger, which listed Deep Fission on the lesser-known and lightly traded OTCQB stock exchange and netted just $30 million. According to an SEC filing, the stock never actually traded, and at the time of the offering, it read as a quick attempt to secure cash. The startup had been attempting to raise a $15 million seed round earlier in the year that never panned out, and to date has raised only a modest $4 million in venture funding.
Deep Fission’s fortunes might be shifting, however, given that it’s transferring its listing to a major national exchange. The company’s public markets strategy does appear to be working as of late — In February, the startup raised $80 million by selling over 5 million restricted shares directly to investors. Whether this will all be enough to achieve its goal of beginning commercial operations in 2027 or 2028 remains to be seen, however. As a part of the Department of Energy’s Reactor Pilot Program, Deep Fission initially aimed to reach criticality — the point at which a nuclear chain reaction becomes self-sustaining — by this July, a target that now looks highly unlikely.
As utilities scramble to keep pace with surging electricity demand, expanding grid-scale renewables, increasingly extreme weather while also coordinating new, distributed resources coming online, modern grid management is getting too complex for traditional software to keep up. Texture, the startup billing itself “the operating system for the energy grid,” wants to simplify the ecosystem by giving utilities, virtual power plant operators, and grid service companies a unified view of every device and associated data sources across their network — and it just raised a $12.5 million Series A to scale this solution further.
Texture’s software aggregates data from various sources — everything from smart meters to battery storage systems, electric vehicles, and smart thermostats — and consolidates it into a single layer for grid operators, flagging problems such as voltage irregularities or outage risks in real time. The platform sits atop an operator’s legacy software infrastructure, thus avoiding the need for utilities to overhaul their existing systems or implement customized and expensive enterprise solutions that require dedicated engineering teams to maintain.
The tech has gained traction among utility cooperatives — customer-owned nonprofits that often serve rural communities and maintain smaller staffs and tighter budgets than investor-owned utilities. With this latest raise, the startup is looking to access greater scale in the co-op market through a partnership with the National Rural Telecommunications Cooperative, a network of 850 utility cooperatives across the country which will now gain access to some of Texture’s software. As Texture’s CEO Sanjiv Sanghavi said about its co-op customers in the company’s press release, "They wanted to run modern grid programs but didn't have software built for their scale or budget. A co-op serving 15,000 members shouldn't have to build custom technology to launch a battery program or manage transformer load. We built Texture so they don't have to."
I was off last week, which means I missed the chance to bring you a piece of news that I’m particularly excited about: The sustainability-focused firm S2G Investments closed a $1 billion fund in what managing partner Aaron Rudberg described in a post on the firm’s website as “one of the most difficult fundraising environments in over a decade.” What’s more, this fund is specifically designed to help growth-stage companies bridge the persistent capital gap that emerges for climate tech companies after early-stage venture rounds but before institutional investors deem them bankable. This void often prevents startups from building first-of-a-kind facilities or deploying their solutions broadly enough to prove out their tech and drive down costs.
This fund is also a milestone for S2G itself, marking the firm’s first close after spinning off two years ago from Builder’s Vision, a family office managing investments for Walmart heir Lukas Walton. According to Rudberg, the fund is writing checks in the $25 million to $100 million range, and has already invested $300 million across 10 companies, largely in food and agriculture, energy, and ocean systems. The various recipients include the agricultural input startup Exacto, maritime battery supplier Echandia, and the industrial power optimization company ANA, Inc.
So-called missing middle financing is difficult precisely because it often involves technologies that, at least initially, carry a green premium or depend on policy support. But S2G is adamant that there are plenty of competitive startups, even in a political environment where climate policy is on the outs and affordability is a top concern.
“We believe some of the most attractive investment opportunities are in growth-stage businesses that deliver economic superiority through improved efficiency, margins, and resilience in industries fundamental to the global economy,” Rudberg wrote, as companies with unfavorable economics are being weeded out. “What remains are businesses with genuine commercial advantage, and those are the companies this Fund is built to back.”
Bonus: Antora Turns On Colossal 5 Gigawatt-Hour Thermal Battery in South Dakota
Over two years ago, I wrote about how super hot rocks — that is, thermal batteries — were one of the coolest things in climate tech. Since then, the companies I profiled, Rondo Energy and Antora Energy, have both brought their first commercial plants online, with the latter announcing that milestone this week. On Tuesday, as we covered in Heatmap AM, Antora turned on its 5 gigawatt-hour project in South Dakota, which stores excess wind power as heat for a bioethanol plant operated by POET, the world’s largest biofuel producer. Once the facility ramps to full capacity later this year, it will rank among the world’s largest energy storage projects, relying on over 200 of Antora’s thermal batteries.
For this project, Antora’s tech works by absorbing surplus wind power that would otherwise go to waste in windy South Dakota, where generation often outpaces what the region’s congested transmission lines can handle. The startup converts that renewable electricity to heat using resistive heating, essentially the same technology as a toaster. That’s then stored in insulated carbon blocks for later use, where it can be delivered as direct heat to power high-temperature industrial processes, or converted back into electricity. In this case, the heat is transferred to a circulating fluid that carries it to the POET plant, where it’s then delivered as steam to power boilers, distillers, and other machinery used in ethanol production.
Neither POET nor Antora have disclosed the value of this long-term offtake agreement. The sole external investor providing project-level financing was Australian firm Grok Ventures, a climate-focused investment company bankrolled by Mike Cannon-Brookes, co-founder and CEO of enterprise software company Atlassian. One of Australia’s richest people, Cannon-Brookes has emerged as one of world’s foremost climate investors, pledging $1.5 billion of his wealth to climate projects by 2030. Perhaps its telling of the investment environment at large that an Australian billionaire — rather than the U.S. government or institutional investors — had to push this first-of-a-kind project over the finish line.