This week we’ll take a look at some of the recent buzz around nuclear energy for data centers, starting with a deep dive into a recent interview with the head of the DOE’s Loan Program Office, Jigar Shah.

-JM

Nuclear? Perhaps!

David Roberts from VoltsWTF logs a great interview with Jigar Shah, head of DOE's Loan Programs Office.

“The US needs to double or triple its nuclear capacity by 2050”

“Today, we're talking about persistent load growth that's six times higher than what we've experienced the last 20 years.”.

“[The hyperscalers are] writing white papers saying we don't know how to meet our 24 x 7 standard in 2030 without nuclear”

published: Feb 21, 2024 Source: VoltsWTF

Jake’s take: This is a friendly and thorough conversation around our growing need for “clean firm” energy sources, specifically nuclear. Jigar should need no introduction to this audience…

Full disclosure: I grew up in a household that was decidedly anti-nuclear. I fully acknowledge my bias here! That said, I believe that well-respected voices (like Jigar’s) are helping once-dogmatic folks like me to embrace the notion of nuclear power as something like a “least-worst option”.

I’m also very curious as to whether Mark Jacobson’s “No miracles needed” approach to our energy needs can accommodate the expected growth in demand.

Here’s what stood out to me from the podcast:

Nuclear is really hard, really expensive, and we have no existing economies of scale to support it.

“When it came time to build Vogtle, the National Building Trades trained 13,000 people in how to build a nuclear power plant”

“… I'm hoping that someone builds a new reactor soon so that they all don't get other jobs somewhere else”

“This country lost the ability to do big things — and on purpose.”

In the US, we seem to be much more effective when we think big, but start small and act locally. I believe the ongoing success of DERs (Distributed Energy Resources) like decentralized solar and storage is a good indicator here.

We missed the early opportunity to be the global leader in standardized, modular nuclear reactors.

“We have 92 operating reactors today in this country and no four of them are the same. So we decided that every single nuclear plant should be a snowflake”

I appreciated the history lesson here. The obvious question then is: Can we / Should we try to catch up? Does the US have a global obligation to lead the way in nuclear energy? Or, per Jacobson, would we better off taking all of time, money, and resources required to do so and apply it to what is already working and can scale (namely, renewables+storage)?

The data center load concerns are for real!

“And we have AI saying — I mean, Microsoft alone, I think, is saying we need 10,000 MW for ChatGPT. You've got Google, Amazon, Apple, Meta.”

“But let us just run a thought experiment for a second: The deciders of this are the hyperscale data centers, because they themselves need 200,000 MWh, 200 TWh of new power by 2030”

“close to 20 GW of new data centers that we need by 2030”

“So you know what they're [the hyperscalers] choosing to do instead? They're building a bunch of Bloom boxes powered by natural gas to run a lot of those off grid — and they're not off grid, it's like undergrid. So they have locations where the utility has half the power that they need. They get the other half from Bloom, and then they're waiting for the utility to catch up in two years when the interconnection gets full. And then, of course, the Bloom box continues to run. So I just want to make sure we're crystal clear about the fact that this is not a theoretical conversation.”

I’m not sure how precise Jigar’s numbers are, but you can find the report he references here, which does show 5yr growth expectations doubling from 2022 to 2023.

The hyperscalers (Amazon, Google, etc.) are driving major energy projects, and the federal loans to fund them.

“Microsoft and Google have publicly said that they need to be 24x7 matched with clean energy by 2030, they have a set of options”

“[Google’s] paper suggests that they believe that nuclear power is the likeliest to be able to meet this 24x7 matching requirement that they have.

“So then what they do with that data is they go to a utility and they say, ‘we would like a hamburger today, and I'll give you two hamburgers tomorrow.’"

“… so they're saying our CFO needs a data center next week because it's dependent upon us making $20 billion a quarter in profits. So we'll do that. And in exchange, we'll agree to pay an above-market tariff for 24x7 matched clean power that you will start construction of to supply us with this 24x7 matching that we need, whether it's in 2030 or whether it's in 2035.”

“So, if the utility says we just came to an understanding with Microsoft and Google and Apple and Amazon and Meta for geothermal at $150 a MWh or offshore wind at this price or whatever it is, we would do that loan.”

This section of the conversation took me a minute to digest. I think it goes something like this:

1. Climate-conscious Hyperscaler makes public commitment to clean energy.

2. Hyperscaler can’t buy 100% clean energy yet, because it isn’t available, so they contract in “24x7 clean energy matching”. They continue to consume less-than-clean local energy but pay (a premium) for clean energy to be generated somewhere else (consumed by someone else). This lets the Hyperscaler say they purchase 100% clean energy, even if that’s not exactly what they’re consuming.

3. Hyperscaler can’t even get enough local dirty energy, so they install natural gas-powered fuel cells to generate what they need. As Jigar mentioned, they’ll continue to run even after the initial clean energy goals are met, which I suspect is due to a long expected payback period on the fuel cells.

4. Demand for 100% clean energy is so high (driven by the need to build the next data center, etc.) that the Hyperscaler can’t find enough existing energy-matching contracts. So, they negotiate a deal with the utility to buy 100% clean energy in the future, and at a premium price.

5. Utility co takes that contract for future clean power to Jigar and says “give me a loan to build the new clean power plant I need to fill this contract”.

Jigar seems to be saying that he’ll fund nuclear projects all day long if the utilities come to the DOE with contracts to provide nuclear power to the hyperscalers.

For a look at volumetric vs. emissions vs. temporal (24x7) energy matching, check out: Most net-zero energy strategies have no real emissions benefits, researchers warn.

For a critical view of 24x7 energy matching: Every load an island: Requiring hourly matching of clean electricity purchases would raise emissions

SMR’s (small modular reactors) are not the panacea that many have suggested…

“They're neither small nor modular, but they are reactors”

“it is structurally more expensive to do an SMR than to do an AP1000 (an average-sized reactor)”

We’re also still many years away from commercial viability, with Jigar noting that the first commercial microreactor should come online “the day before 2030”.

Also, SMRs may “actually increase the volume of nuclear waste in need of management and disposal, by factors of 2 to 30” according to a recent study.

For some basics on SMRs, see this IAEA page.

And, don’t worry about the nuclear waste…

“coal plants in our country have produced ten times more nuclear radioactive waste than our nuclear fleet”

“I could make my bed out of it and sleep on it, and I would get more radiation from the atmosphere than I'm getting from the waste”

Though I appreciate the sentiment that coal is the real enemy, the idea that nuclear waste is nothing to worry about runs counter to everything I’ve ever learned. I’m certainly not an expert, but a quick look at the U.S. NRC seems to confirm that this stuff is nasty: “10 years after removal from a reactor, the surface dose rate for a typical spent fuel assembly exceeds 10,000 rem/hour – far greater than the fatal whole-body dose for humans of about 500 rem”

All in all, a really interesting conversation. I’d love to see David Roberts host a debate between Jigar Shah and Mark Jacobson on their dueling "All-of-the-Above vs. No-Miracles-Needed" approaches to our energy demands!

AWS Acquiring Hyperscale Data Center Directly Connected to Nuclear Plant

Cumulus data center campus owner and nuclear power plant owner Talen Energy agreed to sell the facility to AWS for approximately $650 million

Published: Mar 5, 2024 Source: MicroGridKnowledge.com

Jake’s take: Just in case you thought the conversation was too theoretical… Hyperscaler Amazon will be specifically and intentionally powered by nuclear energy.

…and I can see how it makes sense to put the data center “behind the fence”. The DC doesn’t then burden the downstream transmission lines, and its construction likely benefitted from easier permits due to its colocation with the power plant.

Also, $650M is big business! Cumulus Data reportedly developed the site speculatively and then found a buyer. I’ve gotta imagine we’ll see more deals like this in the coming years.

Future data centres may have built-in nuclear reactors

“A normal data centre needs 32 megawatts of power flowing into the building. For an AI data centre it's 80 megawatts”

“Our industry has to find another source of power”

“nuclear power is the only answer to the pressures the new AI data centre world faces”

Published: Feb 14, 2024 Source: BBC

Jake’s take: The focus of the article is SMRs in the UK, about which most of the interviewees are bullish. There is an acknowledgment of the timing difficulties; as one contributor puts it: “it will take a long time to deploy a nuclear-configured data centre in the UK, and AI is happening now”

That’s a wrap! EN+AI seems to be moving in a less frequent, but more detailed direction. Let me know what you think- do you prefer the lighter-weight, more frequent (roughly weekly) posts, or the more detailed (but slower, roughly monthly) view? Or, both?

Either way, thanks for reading!

In case you missed it- here are your favorite links from previous editions of EnergyNews.ai:

Rethinking Concerns About AI’s Energy Use

Daniel Castro provides a comprehensive look at the concerns around AI’s energy consumption.

published: Jan 29, 2024 Source: Center for Data Innovation, PDF here

Why AI and energy are the new power couple

AI is increasingly vital in managing complex, data-rich power systems, especially with the rise of renewable energy sources. This IEA.org article highlights AI's role in improving the predictability and efficiency of power supply and demand, particularly in renewable energy. It also delves into AI's contribution to predictive maintenance, ensuring more robust and reliable energy infrastructures.

source: iea.org

Have a great article or content suggestion for future editions of EnergyNews.ai? Please reach out here!

This week we’ll take a deep dive into a fantastic article on AI’s energy consumption, followed by a couple of buzz-worthy posts that were heavily circulated last week, and a podcast from the electric co-op perspective.

-JM

Rethinking Concerns About AI’s Energy Use

Daniel Castro provides a comprehensive look at the concerns around AI’s energy consumption. He suggests:

“Just as early predictions about the energy footprints of e-commerce and video streaming ultimately proved to be exaggerated, so too will the dire estimates about AI likely be wrong”.

and:

“There are measured steps policymakers can take to ensure AI is part of the solution, not part of the problem, when it comes to energy use and the environment.”

published: Jan 29, 2024 Source: Center for Data Innovation, PDF here

Jake’s take: This is a thorough and well-researched article targeting a policy-making audience, and should be considered a “must-read” for anyone tracking this space.

Let’s take a deeper look at some of the author’s points, starting with his 4 key recommendations:

• Develop energy transparency standards for AI models.

• Seek voluntary commitments on energy transparency for foundation models.

• Consider the unintended consequences of AI regulations on energy use.

• Use AI to decarbonize government operations.

The first 2 points are critical, in my view. A full stack of software tools, standardized metrics, and reporting mechanisms are required here. We need to build the systems that will allow the AI companies and data centers to evangelize their energy-saving efforts and to compete on carbon-reduction goals. I believe that a large segment of the AI market would choose the “greener” option, given similar cost and performance.

AI’s energy use is hard to measure

“Creating accurate estimates of the energy use and carbon emissions of AI systems over their lifetimes is challenging because these calculations depend on many complex factors, including details about the chips, cooling systems, data center design, software, workload, and energy sources used for electricity generation”

It is a very difficult challenge, and one that needs much more development. Simply measuring the electricity consumption at the server (or even, GPU) level doesn’t tell the whole story. I do believe we can get “close enough” by using models to infer energy consumption based on compute time, total FLOPS, etc... But, even then, we need to understand the dynamic carbon-mix of the energy provided throughout the compute job to get a real understanding of the true carbon costs.

I see a push to compare a sort of model-size/energy consumption ratio: “Despite GLaM being nearly 7 times larger than GPT-3 and outperforming the other AI model, GLaM required 2.8 times less energy to train”. I think we should resist this as an oversimplification; not all models are trying to achieve the same outcomes, and there may be a multitude of factors other than the size of the model which determine its ultimate energy consumption.

I found the following table to be very informative, and I have not seen these data assembled so clearly anywhere else:

The major trends that I notice are the increase in parameters (>10x from GPT-3 to 4) and number of chips (2.5x from GPT-3 to 4).

“AI’s Energy Footprint Ignores Substitution Effects”

I love this point! While we’re often focused on the energy consumption of AI, hopefully balanced by the direct energy and carbon savings provided by the innovations it unlocks, we often overlook the additional energy/carbon savings of using AI for non-energy related everday tasks. Mr. Castro quotes research that found: “AI writing a page of text emits 130 to 1,500 times less CO2e than a human doing so” and “AI creating an image emits 310 to 2,900 times less.” It’s easy for me to imagine a significant impact here, though quantifying that impact will be a challenge.

“Develop Energy Transparency Standards for AI Models”

Mr. Castro is focused on a policy-making audience here, though I think everyone in the industry would also benefit from his recommendation to “support the development of energy transparency standards for AI models, both for training and inference”. I appreciate that he specifies training AND inference, as I believe we tend to undervalue the workloads associated with inference after the training is complete.

In summary, I agree with Mr. Castro’s major points, and we should be skeptical of the headline-grabbing projections (see below). That said, Mr. Castro’s analysis does not seem to account for the massive expected growth in GPU cluster size; Meta has announced plans to build a cluster of 600k GPUs (up from 16K in 2022), while OpenAI is rumored to be building up to 1M GPUs (up from the 25k it used for GPT-4). The other major AI players will likely need to keep up; thus, while some of the fantastic estimates on energy consumption we’ve seen are likely overestimating the energy per chip, they’re probably also underestimating the number of chips to be deployed.

AI Needs So Much Power That Old Coal Plants Are Sticking Around

Power companies are scrambling to satisfy the needs of data centers and new factories in a country where the grid is already strained.

“To cope with the surge, some power companies are reconsidering plans to mothball plants that burn fossil fuels”

“Soaring electricity demand is slowing the closure of coal plants elsewhere. Almost two dozen facilities from Kentucky to North Dakota that were set to retire between 2022 and 2028 have been delayed, according to America’s Power, a coal-power trade group”

Published: Jan 25, 2024 Source: Bloomberg (Paywall warning!)

Jake’s take: Apologies for the paywalled source here, but this one was all over my feeds last week so I felt it deserved attention.

Though the estimates of AI’s future energy demands may be often inflated, as stated in the previous article, this article suggests that very real energy constraints are affecting the AI industry today. There’s some conflation of issues here (is the problem AI, EVs, or green manufacturing? All of the above?), but I certainly agree that we’ll likely see a future where “we might have to delay the timing at which new large loads are added (to the grid)”.

If you’re ok with Bloomberg’s paywall, also check out:

NextEra Pushes Renewable Plants to Fuel Explosive Demand From AI

Blackstone Is Building a $25 Billion Empire of Power-Hungry Data Centers

What the AI Revolution Means for Electric Co-ops

How are co-ops already using AI, and how will this evolving technology shape and change the way we approach operations, member services and our workplace culture in the years to come?

“there's going to be a kind of a merger between these smart energy networks…because there's no point having a very sophisticated data center running these AI models if your energy networks are not stable and able to support it” (07:47)

“The next set of villains are going to be big tech because people are starting to realize that the energy use and the carbon impact of people being on Chat-GPT 24 -7 is going to be significant.” (10:16)

Published: Jan 16, 2024 Source: NRECA

Jake’s take: Guest speaker Mike Walsh gives some good background on AI but only very generalized suggestions of specific applications for electric co-ops. I’m not sure there’s anything in here at all that’s specific to co-op utilities. He does pose the question, “what does an AI -powered utility of the 21st century look like if you are planning it from scratch?”, but doesn’t offer any specific suggestions.

All in all, not a bad generalist view of the transformitive nature of AI, but very light on utility-industry specifics.

In case you missed it- here are your favorite links from previous editions of EnergyNews.ai:

Why does AI need so much energy?

Armand Ruiz, Director of AI at IBM, dives into the drivers of AI’s energy consumption.

Source: newsletter.nocode.ai

Why AI and energy are the new power couple

AI is increasingly vital in managing complex, data-rich power systems, especially with the rise of renewable energy sources. This IEA.org article highlights AI's role in improving the predictability and efficiency of power supply and demand, particularly in renewable energy. It also delves into AI's contribution to predictive maintenance, ensuring more robust and reliable energy infrastructures.

source: iea.org

Have a great article or content suggestion for future editions of EnergyNews.ai? Please reach out here!

Hi Folks, there was a lot of news last week regarding the energy consumption of AI, especially with Davos capturing so much of the media cycle. Though I hadn’t intended for this newsletter to carry a weekly theme, it made sense to focus exclusively on that topic for this edition of EnergyNews.ai.

Also, I’m going to start experimenting with some additional features/segments to the newsletter, with the goal of eventually establishing a subscriber-paid and/or sponsor-worthy version of the weekly post. I’d highly value your feedback as I stumble through figuring out how all of that works.

I’m calling this week’s new feature “Jake’s Takes”, where I will provide a bit of my own (ever-evolving) view on the main ideas presented by each link. Please let me know what you think!

-JM

OpenAI CEO Altman says future AI depends on energy breakthrough

While speaking at Davos, OpenAI's CEO Sam Altman said an energy breakthrough is necessary for future artificial intelligence, which will consume vastly more power than people have expected. Anticipating this demand, Altman has invested at least $375 million in nuclear fusion.

Video of Altman’s comments can be found below. Relevant comments begin at about 23:00:

published: Jan 16, 2024 Source: Reuters, Bloomberg

Jake’s take: There’s a lot to digest here. Fusion? Really? We’re still (and, seemingly, perpetually) 20-25 years away from any meaningful commercialization of fusion tech, and it almost certainly won’t happen soon enough to address climate change. Apparently, Altman doesn’t believe that existing/known energy tech (renewables, nukes, etc.) will keep up with AI’s demand. He does suggest “radically cheaper solar plus storage or something at massive scale, like a scale that no one is really planning for.” as an alternative approach.

I found this to be a powerful statement:

“My whole model of the world is that the two important currencies of the future are compute/intelligence and energy”

Sam has a massively influential voice here. If he’s right, we’ll have a significant energy hole to fill, and I think we’ll see a big uptick in interest around nuclear power again (remember Crypto?). I’ve got to wonder- might his $375M investment have a larger, faster impact applied towards solar+storage innovations?

Why does AI need so much energy?

Armand Ruiz, Director of AI at IBM, dives into the drivers of AI’s energy consumption. He also covers:

• Environmental Impact of High Energy Consumption

• Case Studies: AI's Energy Usage in the Real World

• Sustainable Solutions and Innovations

• Industry Initiatives and Corporate Responsibility

• Looking Ahead: The Future of AI and the Environment

Published: Jan 13, 2024 Source: newsletter.nocode.ai

Jake’s take: Great info from a credible source. Armand is at the bleeding edge of AI development, and the fact that his article is (in my opinion) light on actionable sustainability solutions for AI developers suggests that there’s a big opportunity here. How do we develop the tools so that AI engineers can make intelligent decisions about how their models are run? Surely not every model training job has to be run immediately and at 100%. I’d love to see the dev tools provide energy-saving options to the engineers, e.g. “Run my model optimized for energy (and cost!) savings, even if it takes 50% longer”.

Also, (and related to the above post), the suggestion that nuclear power is “sustainable” might raise some eyebrows.

Deploying high-performance, energy-efficient AI

Investments into downsized infrastructure can help enterprises reap the benefits of AI while mitigating energy consumption, says corporate VP and GM of data center platform engineering and architecture at Intel, Zane Ball.

Published: Jan 10, 2024 Source: MIT Technology Review

Jake’s take: Ball is saying good things here, like “Enterprises need to be very aware of the energy consumption of their digital technologies, how big it is, and how their decisions are affecting it" and projecting “facilities that train AI models on a large scale while modulating energy consumption based on its availability” and the “continued growth of liquid cooling”.

I appreciate the focus on the Data Centers, and the suggestion that the DCs will need to offer more tools to their energy-aware AI customers is spot-on.

AI Is Ravenous for Energy. Can It Be Satisfied?

“As long as the competition between makers of AI continues to spur companies to use these ever more capable, ever more power-hungry models, there’s no end in sight to how much more electricity the global AI industry will demand. The only question, then, is at what rate its consumption of power will increase.”

Published: Dec 15, 2023 Source: WSJ

Jake’s take: There are some strong claims in this article, like: “global power usage for AI systems could ratchet up to 15 gigawatts… requiring about 15 average-size nuclear power plants”, and “the amount of electricity required to power the world’s data centers could jump by 50% by 2027, thanks to AI alone”.

The quoted researcher, Alex de Vries, goes on to say that those estimates don’t include the cooling load, which could be equal to the compute load… so, 30 new nukes?

I think the main takeaway here is that the estimates are still a bit “finger to the wind”, and likely on the low side.

If you’re looking for a more conservative view, check out David Mytton’s blog. David is highly credible on data center energy consumption and argues that most estimates depend upon inaccurate extrapolation techniques.

In case you missed it- here are our favorite links from previous editions of EnergyNews.ai:

How can AI help with climate change?

In this episode of Volts.wtf with David Roberts, Priya Donti, executive director of nonprofit Climate Change AI, speaks to how artificial intelligence and machine learning are affecting the fight against climate change.

source: volts.wtf

Why AI and energy are the new power couple

AI is increasingly vital in managing complex, data-rich power systems, especially with the rise of renewable energy sources. This IEA.org article highlights AI's role in improving the predictability and efficiency of power supply and demand, particularly in renewable energy. It also delves into AI's contribution to predictive maintenance, ensuring more robust and reliable energy infrastructures.

source: iea.org

Have a great article or content suggestion for future editions of EnergyNews.ai? Please reach out here!

Why AI and energy are the new power couple

AI is increasingly vital in managing complex, data-rich power systems, especially with the rise of renewable energy sources. This IEA.org article highlights AI's role in improving the predictability and efficiency of power supply and demand, particularly in renewable energy. It also delves into AI's contribution to predictive maintenance, ensuring more robust and reliable energy infrastructures.

source: iea.org

AI-powered permitting is speeding up solar deployments in California

Symbium has signed up 22 California cities to try out its software, which uses automation techniques to slash the time it takes to process solar permits.

source: canarymedia.com

AI-Driven Advancements in Perovskite Solar Cell Production

An innovative new approach leverages AI technologies to enhance the efficiency and effectiveness of manufacturing perovskite solar cells, a promising alternative to traditional silicon-based solar cells.

source: pv-magazine.com

AI comes up with battery design that uses 70 per cent less lithium

Researchers used AI to design a new material that they used to build a working battery – it requires up to 70 percent less lithium than some competing designs.

source: new scientist.com

In case you missed it- here are our favorite links from previous editions of EnergyNews.ai:

How can AI help with climate change?

In this episode of Volts.wtf with David Roberts, Priya Donti, executive director of nonprofit Climate Change AI, speaks to how artificial intelligence and machine learning are affecting the fight against climate change.

source: volts.wtf

A.I. Could Soon Need as Much Electricity as an Entire Country

“By 2027 A.I. servers could use between 85 to 134 terawatt hours (Twh) annually. That’s similar to what Argentina, the Netherlands and Sweden each use in a year, and is about 0.5 percent of the world's current electricity use.”

source: New York Times

Have a great article or content suggestion for future editions of EnergyNews.ai? Please reach out here!

Thank you so much for finding EnergyNews.ai!

I believe that the world is changing fast- for better and for worse. I think that one of the fundamental questions of our time is: Will the pace of technological innovation provide us with the tools we need to address the effects of climate change? Or will our new, energy-hungry AI tools only accelerate our dependence upon carbon-intensive energy sources and raw materials?

EnergyNews.ai is a curated periodic email newsletter that aims to shed some light on that very question.

Who is this for?

Are you passionate about ClimateTech? Are you curious about how AI might help to advance our clean energy goals? Are you worried about the intense energy consumption required by AI? Are you looking for opportunities to apply your AI skills to address climate change?

If your answer is yes to any of these, I hope you’ll subscribe! EnergyNews.ai is currently free to all, so please come along for the ride :)

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About me

Connect with me here or email me here

I started my first company in 2007 to develop demand-side energy management for commercial and industrial buildings. We called it “CleanTech” back then- and a wave of new investments flooded the space. Most of us failed. Now we call it “ClimateTech”, which is both a) a brilliant (if cynical), collective marketing re-brand that allows us to sweep under the rug all of the failed CleanTech companies, and b) an indication of real progress; we can safely say “Climate” now, and though it’s certainly still a politicized term, it’s finally widely accepted amongst investors, corporate partners, and the general customer base.

I’ve been in the space ever since, serving as CxO and board member of several innovative technology startups. I’m a technologist at heart- I like to build things, I like to write code, and I generally (and often, naively) believe that technology solutions exist to address our most pressing issues, even if we haven’t discovered them yet.

I was initially very skeptical when “AI” started dominating my newsfeeds (led mostly by ChatGPT in late 2022). My general reaction was that this was just another fad, another cycle of techno-hype that would soon go the way of blockchain. I had personally worked on several machine learning projects, and my general response to the hype was something like, “What’s the big deal? We’ve been doing ML stuff for more than a decade; it’s cool and all, but why all the noise now?”

The answer, of course, is that I had missed all of the innovation happening within the world of LLMs.

Fast forward to today, and I’m very much an AI convert. I believe that we absolutely hit an inflection point sometime in 2023, and it’s for real. AI will touch all aspects of our personal and professional lives (whether we like it or not). I believe that AI has the potential to unlock incredible innovations in Climate and Energy, and it just might be the core technology that helps humanity survive the effects (if not address the causes) of climate change.

Why am I doing this?

AI has exploded onto the scene. The pace of innovation is staggering. It’s hard to keep up with all of the developments- even for a technologist like myself. EnergyNews.ai is my attempt to distill all of the news from the world of AI into a regular collection of the most relevant content, curated for an energy and climate-focused audience.

The content I select will relate to the following topics:

• Exciting uses of AI for energy and climate

• Innovative ClimateTech companies using AI

• Energy consumption of AI

• How to reduce the carbon footprint of AI

Ultimately, I have two lofty goals:

• Help the energy and climate audience understand and keep up with all of the developments in the AI space, and, hopefully, inspire you to leverage AI to unlock groundbreaking innovations in your work.

• Help the AI folks in my audience understand the climate implications of their work, and, hopefully, inspire you to direct your talents towards applications in the energy and climate space.

Can we achieve this with a simple weekly newsletter? I don’t know! But please join me as we give it a go…

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Thanks again- and stay tuned for the next post!

J.M.

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