By one estimate, Nvidia provides the hardware for more than 70% of the market for AI chips, and estimates from 2023/2024 suggested it was previously closer to 95%. This is because Nvidia chips are exceptionally efficient, performing the heavy computational tasks required for machine learning at a much lower energy threshold than common computer chips.

“This year we created a definition for sustainability in AI hardware based on energy processing per unit,” explains Michael Yow, director of rankings at Corporate Knights. The sustainability threshold used by Corporate Knights is measured in gigaFLOPS (floating-point operations per second) per watt, the number of computations performed per watt of energy. The threshold was first established by the Green500, a ranking in which Nvidia’s Grace Hopper chips power seven of the top 10 most energy-efficient supercomputer systems.

As a result, $75 billion of Nvidia’s 2025 revenues – about 57% of the total – was deemed sustainable according to the Corporate Knights methodology. That sustainable revenue was not only one of the largest gross totals in the ranking; it was also among the fastest growing, registering 123.5% growth since 2022. (All figures in U.S. dollars.)

The environmental impact of data centres populated with millions of these chips is increasingly well understood. One study in December reported that current AI infrastructure has roughly the carbon footprint of the city of New York (about 80 billion tons) and that its collective water use is in the range of the global bottled water industry (765 billion litres).

But these figures pale in comparison to the potential future impact. Last September, OpenAI’s Sam Altman issued an internal memo saying the company’s “audacious long-term goal is to build 250 gigawatts of capacity by 2033.” An analysis from Truthdig found that this would put ChatGPT’s energy use on par with India’s 1.5 billion people, which, depending how that energy is sourced, could produce carbon emissions twice that of ExxonMobil, currently one of the largest non-state emitters in the world.

Nvidia is the primary supplier of the chips populating OpenAI’s data centres, and the two companies recently announced a partnership to continue to build out this infrastructure.

The 2026 Global 100 list puts speed in the spotlight

The power sources

There is an irony to all this: the scale of these operations and their environmental impact is almost exclusively a result of the resource efficiency of Nvidia’s chips. Data centre operations for AI would be impossible if they relied on general-purpose servers. It’s the efficiency of Nvidia’s chips that has enabled this “insatiable demand” for computing power. This phenomenon, known as Jevons paradox, has a historical precedent: coal. In 1865, the English economist William Stanley Jevons observed that the more efficiently the industrial revolution’s primary fuel source could be used, the greater the demand grew.

In the United States, it’s estimated that AI data centres could account for as much as 12% of all energy consumption by 2028. Where is that power coming from? A 2025 report from the International Energy Agency found that coal currently accounts for about 30% of electricity generation for AI data centres globally, mostly in China and the United States. Renewables – wind, solar and hydro – account for 27%, and natural gas accounts for 26% (40% in the United States).

Corporate Knights’ Yow points out that Nvidia as a company cannot be held responsible for the power sources of the AI industry, even if its chips do make up much of its infrastructure. “Would we be having this conversation if all the electricity was 100% carbon-free?” he asks. “The problem is not with the product but rather with the lack of planning and faster deployment of renewable energy.”

Because of the outsized electrical needs, AI companies are exploring options for generating their own on-site power. Altman has, for example, personally invested in a start-up developing nuclear fusion, the holy grail of renewable power generation, and his company has joined the likes of Google, Microsoft and Amazon to invest in small modular nuclear reactor technology. Nevertheless, the most common on-site power sources at present are fossil fuels. At OpenAI’s Stargate facility under construction in West Texas, one of the largest in the world, they are currently deploying dozens of turbines adapted from aircraft engines.

That data centre is populated with Nvidia chips and operated by OpenAI and Oracle. Though the majority of Nvidia chips are not in Nvidia-owned or -operated data centres, the company reported that all offices and data centres under its control are powered exclusively by renewable energy and that it purchases additional carbon-free electricity to cover 100% of the footprint of its leased data centres.

The optimistic outlook

Nvidia has taken measures to address the water consumption at its own data centres, which use it to cool overheating servers. In its 2025 annual sustainability report, the company said it conducts annual water-risk assessments near all its facilities. Its sites in Santa Clara, California, and Hyderabad, India, have water treatment facilities so wastewater can be used for landscape irrigation. The company is also introducing closed-loop liquid cooling systems to reduce water use, and the Blackwell computing architecture – the successor to the Grace Hopper chips – is 300 times more water efficient than air-cooled architecture. (This does not address water use during manufacturing, however, which relies on ultra-pure jets of water to etch the silicone wafers, becoming contaminated in the process. Nor does it pertain to the data centres Nvidia does not control or operate.) A Nvidia spokesperson declined to participate in this story beyond referencing existing company communications.

Those bullish on the prospect of an AI-optimized future will argue that the environmental impact of AI itself will be dwarfed by the efficiencies the expansion of these systems will allow. On one blog, Nvidia cited reports looking at projected U.S. energy demand into 2035. If AI applications are “fully realized,” estimates suggest it could save nearly 2,500 petajoules of energy – about 25% of the entire country’s energy use in 2023.

Such savings would be transformative, and there is a much wider range of environmental applications that AI might come to revolutionize: wildfire detection and management, global climate simulations and extreme weather modelling, electrical grid applications to manage fires and outages, wildlife population tracking, materials science for cleantech applications, and other conservation or carbon-reduction efforts.

To date, the economy-altering value of these companies has been largely measured against their future potential, even as the imminent costs of doing business continue to mount. The environmental balance sheet isn’t so different.

Tristan Bronca is a magazine writer and editor based in Newmarket, Ontario.

The post There’s a paradox at the heart of Nvidia’s sustainability performance appeared first on Corporate Knights.

It’s energy efficiency.

Energy efficiency is the cheapest source of new energy because every kilowatt-hour that I save on the grid is one that someone else can use.

Within the world of computers, the price-performance efficiency is even more pronounced. A 2023 iPhone 15 is 60,000 times cheaper than a 1976 Cray-1 supercomputer. It’s also 180,000 times more energy efficient and 5,000 times more powerful.

Kevin Weil, chief product officer at OpenAI, says that OpenAI’s models are improving 10-fold every year in energy efficiency. Deferring compute-heavy tasks such as AI training to times when data-centre energy use loads are light and assigning loads across many data centres – in essence adjusting when and where power is used – could have the same benefit of building 200 gigawatts of new capacity, argues Amory Lovins, one of the cofounders of the Rocky Mountain Institute, which is on a mission the transform global energy systems. That’s far more than enough to power all projected new data centres from existing utility assets.

AI driving efficiency in buildings

Computers aren’t the only arena where efficiency is making giant leaps. Buildings represent roughly 40% of global energy consumption and 75% of U.S. electricity use, making them an enormous target for efficiency improvements. Research published in Nature Communications found that AI applications could reduce building energy consumption by 8% to 19% by 2050, and up to 40% when combined with other policies such as retrofits and low-carbon power generation. Building efficiency can free up more electricity use than AI will ever require.

This creates a fascinating dynamic: while AI consumes energy, it also enables efficiency gains across the broader economy. The key is ensuring that AI deployment is strategic and coupled with robust measurement and verification protocols. AI for buildings isn’t theoretical; companies like BrainBox AI and others are already deploying systems that optimize HVAC (heating, ventilation, air conditioning), lighting, and energy storage in real time based on occupancy, weather and grid conditions.

“Artificial intelligence is the latest development in a long-standing megatrend in which information, analysis and innovation have been replacing the waste of energy and materials that characterize the overbuilt technologies of the 20th-century fossil economy,” notes Ralph Torrie, director of research at Corporate Knights. “Of course it uses electricity, but not nearly as much as it displaces.”

There are many examples of rapid, unexpected gains in energy efficiency. In 2021, all the blockchain-based cryptocurrencies combined used somewhere between 190 and 250 terawatt-hours of electricity, which is just about 1% of global electricity demand that year, or roughly the same as Taiwan’s consumption. Critics called blockchain technology fundamentally unsustainable.

Then in September 2022, Ethereum underwent a major transformation it called “The Merge,” shifting its consensus mechanism from “proof of work” to “proof of stake.” This cut the network’s energy consumption by 99.9%. The network’s annual energy use dropped from 80 terawatt-hours – the same amount of electricity that Austria or Finland uses in a year – to just 0.01 terawatt-hours.

Proof of stake achieved these gains by eliminating wasteful competition. Instead of miners racing to solve puzzles, Ethereum now relies on validators who are chosen to create new blocks and confirm transactions based on the amount of Ether (Ethereum’s native currency) they have staked as collateral. This method secures the network through financial commitment rather than raw computational power.

When real limits appear, innovation often accelerates in unexpected ways. Constraints can become the spark for new possibilities.

— Anthony Di Iorio, Ethereum co-founder

The shift to proof of stake also enhanced its security, scalability and environmental sustainability. This landmark move positioned Ethereum as a leader in sustainable blockchain innovation and demonstrated that large-scale decentralized systems can evolve to meet global energy and climate goals without compromising performance or decentralization.

Ethereum co-founder Anthony Di Iorio says that Ethereum’s massive efficiency gain is part of a broader pattern of disruptive innovation across technologies: “When real limits appear, innovation often accelerates in unexpected ways. Constraints can become the spark for new possibilities.”

For Di Iorio, the key lesson is that “fundamental architectural redesign beats incremental optimization. When you eliminate structural inefficiency rather than just making a system slightly less inefficient, you unlock orders-of-magnitude improvements.”

DeepSeek, a Chinese AI company, also showed that algorithmic innovation can cut costs – in its case by 97% even under severe hardware constraints. Meanwhile, SETI@home coordinated millions of personal computers to create a huge virtual supercomputer to power its search for extra terrestrial intelligence.

Better orchestration, multi-tenant GPU sharing (in which multiple users share computational resources) and carbon-aware scheduling could push AI infrastructure use from today’s 25% to 40% to 55% to 60%, effectively doubling capacity without building any new facilities.

“The question isn’t whether AI will consume catastrophic amounts of energy,” Di Iorio notes. “The question is whether we will apply what we’ve already learned about radical efficiency gains.” That means implementing the measurement and transparency frameworks that enable market discipline and establishing the policy guardrails that ensure that efficiency serves sustainability rather than just enabling endless expansion.

The path forward: Three essential actions

Three near-term actions could save 15 to 70 terawatt-hours by 2028:

Default to efficiency. Major cloud platforms and AI frameworks should make lean, right-sized models the default choice rather than requiring developers to opt in. When efficiency becomes the path of least resistance rather than an extra step, adoption accelerates dramatically.

Focus on hardware use. Better workload-management software can increase effective use from today’s 40% to 55 to 60%.

Mandate transparency. Energy consumption per task should be as visible as price and latency. When enterprises and governments demand kilowatt-hours-per-million-tokens disclosure in their AI procurement, providers will compete on efficiency. Market mechanisms work, but they require information.

The projections of AI’s looming energy crisis aren’t wrong if we assume nothing changes. But stasis isn’t how technology works when talented people face hard constraints with clear incentives to solve them.

Jim Harris is a #1 international bestselling author writing on AI, disruption and innovation. He speaks at 50+ events a year.

The post The single greatest tool for reducing greenhouse gas emissions is energy efficiency appeared first on Corporate Knights.

Days after OpenAI became the most valuable private company on the planet, a letter of intent was signed with a little-known Argentine firm called Sur Energy to develop all aspects of the $25-billion project.

“Sur Energy plans to ensure that the data-center ecosystem is powered by secure, efficient, and sustainable sources,” OpenAI said in a statement this week. The company said it “welcomes the potential opportunity to be an offtaker.” An offtake agreement – or a commitment to buy a portion of the energy produced – with OpenAI would help Sur Energy secure financing for the large-scale power facility.

“This milestone is about more than just infrastructure, it’s about putting AI into the hands of more people across Argentina,” added Sam Altman, the CEO of OpenAI, who praised the country’s programming prowess and said it was among the top Latin American countries for developers building on OpenAI tools. “We’re excited to work with Argentina as it builds toward becoming an AI hub for all of Latin America.”

The deal falls under the umbrella of Stargate, a company formed in the United States to shepherd $500 billion worth of investments to secure data capacity for OpenAI. Plans are in place for five U.S. sites – two in Texas, one in New Mexico, one in Ohio and another in an as yet undisclosed location in the Midwest – that together have the potential to generate nearly seven gigawatts of data-centre capacity over the next three years.

The five locations are expected to generate 25,000 onsite jobs and “tens of thousands of additional jobs across the U.S.,” OpenAI said. The company has said the jobs will range from short-term construction roles to full-time operational jobs, but the employment heft appears to be in the set-up phase. Bloomberg reported that Stargate’s first data centre, for example, was slated to create 57 permanent jobs.

OpenAI has development agreements for data centres in the United Kingdom, Norway, Japan and South Korea. The specifics of the Argentina data-centre proposal are not yet known, nor is it clear how many jobs will be created in Argentina, which is going through a volatile economic period as its currency falters. President Javier Milei’s government has implemented deep spending cuts to rein in public spending and offered new incentives for foreign investment, under which the new data centre is said to have landed.

OpenAI is operating full speed ahead in growth and energy usage. It launched in November 2022, and by July 2025, some 18 billion messages were being sent each week by 700 million users, according to a study that was co-authored by researchers at Harvard, Duke University and OpenAI. That represents about 10% of the global adult population. “For a new technology, this speed of global diffusion has no precedent,” the usage report’s authors noted. The study found that non-work messages have grown faster in the last year of study and now represent more than 70% of all messages sent.

Last month, OpenAI unveiled Sora 2, its latest video-generation model that is “more physically accurate, realistic and more controllable than prior systems.” Its test videos included a figure skater performing jumps with a cat on her head and a man doing backflips on a boogie board in a lake.

Sophisticated computing carries a high energy cost

A study this year found that AI use already represents 20% of data-centre consumption and is on track to double by the end of 2025. The International Energy Agency said data centres represented up to 1.5% of global electricity consumption in 2024. Various studies have shown that the energy required to answer even the simplest of queries uses a significant amount of water, which is used to cool the data centres working hard to create all the AI slop now filling the Internet.

The consumption levels are fast changing as the technology changes, and there are competing assessments, but the cumulative impact seems clear. Google, for example, reported that it consumed more than six billion gallons of water to cool all its data centres in 2023.

An investigation by The Washington Post published last year found that the amount of water required to cool the data-centre processing for a 100-word AI-composed email via ChatGPT4 was more than a 500ml bottle of water. Meanwhile, in a blog post earlier this year, Altman said that the average ChatGPT query uses about 0.000085 gallons of water, or “roughly one fifteenth of a teaspoon.”

With scant details available about what shape the data centre will take in Argentina, the discussion about what such a piece of infrastructure could mean has yet to coalesce.

Natalie Alcoba is a Buenos Aires–based journalist and senior editor at Corporate Knights.

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In his first appearance on the world stage, Vance made clear that the United States wouldn’t be playing ball. The Donald Trump administration believes that “excessive regulation of the AI sector could kill a transformative industry just as it’s taking off,” he said. “We’ll make every effort to encourage pro-growth AI policies.”

His remarks confirmed a widespread fear that Trump’s return to the White House will signal a sharp turn in tech policy. American tech companies and their billionaire owners will now be shielded from effective oversight.

But upon a closer look, events this week point to signs that just the opposite may be unfolding. A host of nations took notable steps toward address growing safety and environmental concerns about AI, indicating that a regulatory tipping point has been reached.

Wide consensus

The two-day global summit in Paris, chaired by France and India, led to broad consensus. Some 60 countries signed on to a Statement on Inclusive and Sustainable AI. This included Canada, the European Commission, India and China.

Both the United States and the United Kingdom declined to sign the statement. But the prevailing winds are against them. The meeting in Paris was the third global summit on AI, following meet-ups at Bletchley Park in the United Kingdom in 2023 and in Seoul, South Korea, in 2024. Each of them ended with similar declarations widely endorsed.

The Paris communiqué calls for an “inclusive approach” to AI, seeking to “narrow inequalities” in AI capabilities among countries. It encourages “avoiding market concentration” and affirms the need for openness and transparency in building and sharing technology and expertise.

The document is not binding. It does little more than tout principles or affirm a collective sentiment among the parties. One of these – perhaps the most important – is to keep talking, meeting and working together on the common concerns that AI raises.

Environmental challenges

Meanwhile, a smaller group of countries at the Paris summit, along with 37 tech companies, agreed to form a Coalition for Sustainable AI – setting out a series of goals and deliverables. While nothing is binding on the parties, the goals are notably specific. They include coming up with standards for measuring AI’s environmental impact and more effective ways for companies to report on the impact. Parties also aim to “optimize algorithms to reduce computational complexity and minimize data usage.”

Even if most of this turns out to be merely aspirational, it’s important that the coalition offers a platform for collaboration on these initiatives. At the very least, it signals a likelihood that sustainability will be at the forefront of debate about AI moving forward.

Signing the first international treaty on AI

A further notable event at the summit was that Canada signed the Council of Europe’s Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law. In recent months, 12 other countries had signed, including the United States (under former president Joe Biden), the United Kingdom, Israel and the European Union.

The convention commits parties to pass domestic laws on AI that deal with privacy, bias and discrimination, safety, transparency, and environmental sustainability.

The treaty has been criticized for containing no more than “broad affirmations” and imposing few clear obligations. But it does show that countries are committed to passing law to ensure that AI development unfolds within boundaries – and they’re eager to see more countries do the same.

If Canada were to ratify the treaty, Parliament would likely revive Bill C-27, which contained the AI and Data Act.

The act aimed to do much of what Canada agrees to do under the convention: impose greater oversight of the development and use of AI. This includes transparency and disclosure requirements on AI companies and stiff penalties for failure to comply.

What does this really mean?

While the United States signed the Framework Convention on AI and Human Rights, Democracy and the Rule of Law in the fall of 2024, it likely won’t be implemented by a Republican Congress. The same might happen in Canada under a Conservative government led by Pierre Poilievre. He could also decide not to fulfil commitments made under other agreements about AI.

And if Poilievre comes to power by the time Canada hosts the next G7 meeting in June, he might decline to honour the Trudeau government’s commitment to make AI regulation a central focus of the meeting.

The Trump administration may have ushered in a period of more lax tech regulation in the United States, and Silicon Valley is indeed a key player in tech – especially AI. But it’s a wide world, with many other important players in this space, including China, Europe and Canada.

The events in Paris have revealed a strong interest among nations around the globe to regulate AI, and specifically to foster ideas about inclusion and sustainability. If the Paris summit was any indication, the hope of sheltering AI from effective regulation won’t last long.

Robert Diab is a professor in the faculty of law at Thompson Rivers University.

This article was first published in The Conversation. It has been edited to conform with Corporate Knights style. Read the original story here. 

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Such dramatic moves underscore the extraordinary amount of energy required to power the exponential growth in generative artificial intelligence computing, which completely eclipses many current uses. For example, according to a recent calculation performed by The Washington Post and the University of California, Riverside, a single ChatGPT prompt (say, to construct a response to an email) consumes the equivalent of about a bottle of water (for cooling the massive data centres run by AI giants).   

Here’s another: the energy consumed by a prompt that generates a single high-resolution AI image is about equivalent to the electricity used to recharge a smartphone. It may sound small enough, but in both cases, the sheer scale of the collective impact of millions of users is bracing, and goes some way to explain why the large language models that are driving the massive surge in generative AI usage leave environmental footprints that are only getting larger as the number of users expands.   

The growth, in part, is also attributable to the fact that a steadily increasing number of generative AI users rely on these powerful platforms to carry out tasks that a standard search – which requires far less energy and water – can readily perform, according to some AI experts.      

“Using ChatGPT to generate text uses somewhere between 10 to 90 times more energy per query than a conventional Google search,” Ascelin Gordon, a senior lecturer at the Royal Melbourne Institute of Technology, testified earlier this fall before an Australian parliamentary committee investigating the impact of AI. “Producing an image via generative AI uses about 20 times more energy than a generative AI text query. This year, producing videos via generative AI is taking off, and the energy to produce videos is likely to be an order of magnitude higher again.”   

“I find it particularly disappointing that generative AI is used to search the internet,” Sasha Luccioni, the Montreal-based AI and climate lead for Hugging Face, a machine learning firm for builders, told AFP (Agence France-Presse) recently, adding that it’s important for AI firms to “explain to people what generative AI can and cannot do, and at what cost.”  

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Those costs – hidden and otherwise – have been drawing intense scrutiny from climate researchers and policy-makers who point to the dramatic growth in the data-centre industry and its voracious demands for both power and water. According to the International Energy Agency, data centres and bitcoin mining accounted for 2% of electricity demand in 2022, and the growth in those verticals has exceeded all other drivers of electricity consumption.   

Morgan Stanley published its own projection in September, estimating that the global data-centre sector would emit 2.5 billion tonnes of carbon dioxide – equivalent to the emissions created by 766 million automobiles – annually by 2030.   

The tech giants driving the generative AI revolution – Microsoft, Google, OpenAI, Amazon and Meta – all contend that they’re working hard to mitigate the impact of all this consumption with energy-efficiency improvements designed to reduce the carbon intensity of their AI applications, as well as the purchase of “renewable energy certificates” (RECs), which are essentially carbon-offset agreements. Microsoft’s new arrangement with Three Mile Island is an example.  

Yet the use of RECs has come under fire as some climate experts warn that these instruments essentially allow the double-counting of carbon-reduction measures, at both the production stage and at the consumption point, which is to say the AI giants. As an investigation by The Guardian published this past September concluded, the true impact could be in excess of seven times higher than what the tech giants are claiming in their environmental impact disclosures to investors. Soberingly, those conclusions were based on data published between 2020 and 2022 – a year before the advent of image-based generative applications.  

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In the 2024 federal budget introduced on April 16, Canada’s Liberal government earmarked $2.4 billion in new supports for the artificial intelligence sector – everything from upgrades, to infrastructure that can handle all the new computing power, to assists for start-ups and money for workers displaced by AI.

Conspicuously absent from their fiscal plan, however, was any reference to measures that will mitigate the environmental impact of AI, which, as Morgan Stanley predicted in early April, is triggering tipping-point growth in construction of the massive energy- and water-intensive data centres that are needed to deliver all this new computing heft.

That connection, however, was not lost on U.S. Senator Ed Markey, a veteran Democrat and one of the co-authors of the Green New Deal. He introduced legislation earlier this year proposing stringent environmental rules designed to check the escalating carbon, water and land impact of the data centres required to handle the vastly more intensive processing requirements of applications powered by generative AI and large language models – everything from customer chatbots and AI co-pilots to AI-powered image engines, like DALL-E.

Markey’s bill, which has yet to be passed, proposes that the Environmental Protection Agency undertake a detailed assessment of the full range of environmental impacts of AI, including energy, water, and e-waste from servers, and also recommends the development of a voluntary disclosure/reporting system for firms that use AI.

The tech sector’s reliance on data centres is not a new story. Until recently, these facilities were estimated to consume about 1% of the world’s daily diet of electricity, and the United States is home to about a third of all data centres. The International Energy Agency estimates that data centres and the associated transmission infrastructure yielded about 330 megatonnes of carbon dioxide in 2020, or about 1% of energy-related greenhouse gases, because much of the electricity comes from fossil fuel power generation. What’s more, data centres also require huge quantities of water as coolant, and all that consumption triggers even more electricity use. (In data centres, water is piped into and through chillers, pipes, heat exchangers and humidification systems to reduce the heat generated by high-powered servers; the spent coolant then needs to be purified before being returned to the environment.)

AI, however, has turbocharged these impacts. The rapid uptake of applications like OpenAI’s ChatGPT has triggered spikes in the need for new data centres fitted out with the super-powered processors and servers manufactured by Nvidia, the Silicon Valley chipmaker that has emerged as a major enabling player in the AI revolution. Training large language models on seemingly endless supplies of online data is a highly energy-intensive process, but so too are all the searches, prompts and questions enabled by these applications.

“This accelerated development raises concerns about the electricity consumption and potential environmental impact of AI and data centers,” warned Alex de Vries, the founder of Digiconomist, in a 2023 paper published in Joule, an energy journal.

Environmentalists are concerned that the AI-driven surge in electricity and water consumption essentially creates an unwelcome source of competition for renewable power at a time when record high demand has placed enormous pressure on the existing system.

“In a moment when we need to be electrifying our transportation and heating and cooling in all sorts of industries, we’re adding on more and more use from AI and big data centres,” says Erik Kojola, a senior climate research specialist at Greenpeace USA. “For us, this is a major concern because we need to be driving increased renewables towards the things that we really need to electrify. If we’re just increasing demand and load at a faster pace than renewables, then we’re not decarbonizing the grid. Our worry is that this is going to offset any improvements that we’re getting in renewable generation and [create] a setback in our efforts to decarbonize.”

There’s a whole host of social and political questions about AI. But on the climate front and energy front, [we need] to put some guard rails on it.

 

– Erik Kojola, senior climate research specialist at Greenpeace USA

The numbers are bracing. A 2023 research paper published by a University of California, Riverside, team estimated that “global AI demand may be accountable for 4.2 – 6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of . . . half of the United Kingdom.” De Vries’s findings, in turn, indicate that an AI-powered internet search – which is becoming increasingly commonplace – uses 10 times as much power as a conventional one. Google is now responding to some searches with AI tools, and the various versions of ChatGPT quickly attracted more than 100 million users.

Of course, the impact of AI on the data-centre industry has a precursor, which is cryptocurrency, and the extraordinarily energy-intensive process involved in bitcoin (or equivalent) mining. Another de Vries study, published in Joule in early 2024, estimated that the annual water footprint of U.S.-based bitcoin mining was equivalent to the usage of a city the size of Washington, D.C. Moreover, much of the growth in crypto-related water consumption is taking place in Russia and Kazakhstan, which are not known for their environmental controls.

It’s worth pointing out that the largest developers of data centres – giants like Alphabet, Facebook and Amazon – aren’t newcomers to hard questions about their environmental footprints. In some cases, they’ve located these facilities in areas with ready access to water. However, data centres have also been situated in regions where they’re competing with other users for reserves in depleted water tables. Giant tech firms have also established power-purchase agreements with solar and wind farms as a means of decarbonizing their electricity consumption habits and managing the Scope 1 and 2 emissions associated with data-centre operations.

* * *

The accelerating growth pattern in just the past few years suggests that the environmental footprint of AI ­– together with those of more conventional data-storage services ­– could rapidly balloon into a carbon nightmare virtually unchecked by conventional constraints on consumer behaviour; after all, the end users don’t pay for much of this processing power. Kojola argues that governments need to step in rather than simply assume that the data-centre sector will sort out these issues using technology improvements and voluntary reporting measures.

“We’re kind of in the earlier stages of this technology, and now’s the time to get in and put in regulations to help direct it in a way that is socially beneficial,” he says. “There’s a whole host of social and political questions about AI. But on the climate front and energy front, [we need] to put some guard rails on it so that [the technology] is developing in a way that takes into account the energy use and water use and its other environmental impacts.”

John Lorinc is a Toronto-based journalist and author specializing in urban issues, business and culture. His most recent book is Dream States: Smart Cities, Technology, and the Pursuit of Urban Utopia. 

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Nine years after Muchnick created NotCo, a start-up launched in Chile with a mission to use technology to move the needle on sustainability, the trajectory is pointing upwards. NotCo’s line of plant-based milk, mayo, chicken, beef, ice cream and dulce de leche spread has paved the way for partnerships with Starbucks, Burger King, Papa Johns, Mars and, most recently, The Kraft Heinz Company, for which NotCo helped devise a plant-based version of its iconic mac and cheese. 

So far, NotCo has raised US$430 million and has some 500 employees spread across North and South America, almost half working in science development. “It’s an insane amount of money,” says Muchnick, NotCo’s CEO and co-founder, during a call from New York City, where the company’s corporate headquarters are now located. 

“The mission is not only to create products that appeal to the mass market that are better for you and better for the planet,” he says. “It’s to make sure that we’re holding hands with other companies that are trying to do that as well.” 

NotCo’s secret weapon has been an artificial intelligence dubbed Giuseppe, after Giuseppe Arcimboldo, an Italian artist known for paintings of human faces using fruits and vegetables. The algorithm mines the molecular and chemical properties of more than 300,000 plants for combinations that can mimic the components of animal-based foods and replace them. Bamboo fibre and pea protein to make burgers. Peach concentrate and corn to copy chicken nuggets. And a cocktail of chickpeas, sunflower oil and lemon juice to create a spread that tastes like mayo. 

In the plant-based explosion, it’s difficult to identify what moment the movement is in. In 2022, Euromonitor, a market research company, reported that global retail sales of plant-based meat, seafood, milk, yogurt and cheese had reached $28 billion (all amounts in U.S. dollars). Impressive, but, as the Good Food Institute noted, that amount represents “a tiny fraction of the multi-trillion dollar market for conventional animal products.” While dairy replacement is booming, profits at certain companies have faltered, and plant-based meat has stumbled in North America, amid a backlash over its processed nature and price point. A growing population, and growing incomes, means that global production of meat continues to climb, according to the United Nations Food and Agricultural Organization. In 2023, it was up slightly, 0.8%, from the previous year, despite more awareness of the environmental impact of massive livestock production.

The first two years in NotCo [we were] tasting shitty stuff, to be honest. And telling the algorithm why we didn’t want it.

 

– Matías Muchnick, CEO and co-founder, NotCo

Indeed, the food industry has become the common denominator for every major environmental hurdle facing humankind, Muchnick says, from deforestation to land use, water scarcity and ocean depletion. “That is a problem that we need to fix. But we cannot charge that problem to the consumer and make shitty products. We need to make incredible products.” 

Which brings us back to Giuseppe. Muchnick likes to say it has been around since “before AI was trendy.” The seed of his own interest sprouted in 2010, as a keen finance intern trying to harness the power of AI to create a better-performing mutual fund. Soon after, he launched his first start-up: a Latin American version of the Fitbit, which flopped. But conversations with friends brought him to a broader topic, that had to do with existential survival. The food system, he noted, was broken. Studying at University of California, Berkeley, at a time when capital was rushing over to Crispr, the gene-editing technology, he saw the potential for a food system overhaul. 

“Why are we investing more in the aspirin than not getting the headaches?” he asks. He needed partners to disrupt the system, and he found that in Karim Pichara, who along with Pablo Zamora co-founded NotCo. Pichara is an astrophysicist who had developed algorithms for astronomers to understand the composition of a star. They decided to look at food in a similar way. “If we have an understanding of the complexity of food, then we will have the tools to extract value from it,” Muchnick says. 

The process involved trolling terabytes of data and overlapping enough information so that Giuseppe could understand what food actually is. Then came the human variable. It’s one thing to be able to identify that blue cheese and cacao beans share 73 molecules of flavour, Muchnick says; it’s another for the machine to understand that despite those similarities, the products have nothing to do with each other. There’s also a great deal of trial and error. Adding a whole strawberry to a mock chicken recipe might jeopardize the texture and colour of it, but extracting a particular molecule could be of use. 

“The first two years in NotCo [we were] tasting shitty stuff, to be honest,” Muchnick says. “And telling the algorithm why we didn’t want it.” Once they had the secret sauce, they started rolling out their own line of products in 2017 to prove to the market that what they were proposing could work. Their first product, NotMayo, secured 8% of the market share in Chile’s largest grocer in its first eight months, Muchnick says. Now, their NotBurger holds 6% of the total market share of burgers in Argentina, one of the world’s biggest beef-eating nations, and in Chile, he says. 

Mercedes Gómez, a photographer and artist in Buenos Aires, has noticed that change. The 32-year-old has not eaten meat in 13 years. She credits NotCo for creating branding that appeals to a broad audience and paving the way for other plant-based brands in Argentina, and earning them real estate in grocery stores. “They really opened the door,” says Gómez, who shies away from the NotBurger, because “it tastes too much like beef,” but is a fan of NotChicken and always has NotCream Cheese in her fridge. “I know a lot of people that aren’t going to give up beef, but they’re opting for more plant-based [foods] thanks to [NotCo],” she says – in some cases for other reasons.

That’s what prompted 29-year-old Emilia Mac Donagh, an anthropologist who lives in La Plata, southeast of Buenos Aires, to give NotCo products a try. The meat-eater needed to eat less sodium for health reasons and found that NotCo products allowed her to do that. She started with NotMayo, which she says tastes as good as any other mayo. “They allowed me to eat things that I had stopped eating,” she says.

This kind of reaction has translated into financial votes of confidence. In 2021, NotCo secured a financing round of $235 million that pushed its valuation to $1.5 billion, securing “unicorn” status. It recently opened a research and development centre in San Francisco in partnership with Kraft Heinz.

NotCo’s success has also made it a magnet of another kind. In Chile, the country where it launched, it has been the subject of a legal battle with dairy farmers who alleged that the company violated competition rules by using the word “milk” in its products. The first court ruled in favour of the farmers. An appeals court ruled in favour of NotCo. This comes amid a wave of similar disputes. Oatly, the Swedish dairy-alternative start-up, recently won a four-year court fight to be able to use the term “post milk generation” in its advertising in the U.K., while in France, the battle still brews over using terms like “steak,” “fillet” and “ham” to describe plant-based products. Last year, the country’s top court rejected the idea that there is confusion between the terms “steak” and “veggie steak,” but the French government has since issued a decree banning the use of 21 meat-related terms for plant products manufactured in France. Similar bans exist in Italy and several U.S. states. In the case of NotCo, the company armed its defence with a market research study that found that 99% of consumers understood that NotMilk is, well, not dairy milk. The decision has been appealed and is now heading up to Chile’s Supreme Court.

It’s not the only challenge that the plant-based sector is facing. The media, it can be said, is quick to declare the arrival – and the death – of new trends. Such is the case with some segments of the plant-based market, particularly plant burgers, which are seeing a slump after an explosive start a few years ago. The Good Food Institute’s 2022 State of the Industry Report, the most recent available, recorded a 7% increase in overall plant-based revenue in the United States from the previous year, but unit growth was down 8% for plant-based meat.

If we have an understanding of the complexity of food, then we will have the tools to extract value from it.

 

– Matías Muchnick, CEO and co-founder, NotCo

Muchnick says it’s important to be specific. The sector is growing in Latin America and Europe. In North America, dairy substitutes are also growing.

For NotCo, Muchnick says the key to growth is in collaborations. And he and his team have demonstrated an ability to build them. They partnered with Starbucks in Argentina, for example, to create a version of NotMilk that would foam in cold drinks. Burger King in Chile wanted a NotBurger that had the taste profile of a Whopper. 

In a November press release that launched its plant-based mac and cheese, Kraft Heinz noted the challenges facing the plant-based sector as a whole, and mac and cheese in particular. “Less than thirty percent of plant-based mac & cheese buyers are repeating purchase as taste and texture remain their largest pain points,” the release said. That’s why the fifth-largest food and drink company in the world says it reached out to NotCo: it has a “proven track record in creating mouthwatering plant-based foods.” 

In other words, NotCo has become the “garage of innovation,” Muchnick says, and is already fielding requests to apply its algorithm to other sectors, including personal care products and pet food. 

“We are beyond food, which is pretty crazy,” he says. 

Natalie Alcoba is Buenos Aires-based writer and associate editor with Corporate Knights.

Check back here as we roll out our Plant Power package this week, along with the release of the 2024 Spring issue of Corporate Knights.

The post How AI is helping NotCo cook up a plant-based takeover of Big Food appeared first on Corporate Knights.

Some important developments are coming for this year, including the ways in which we plan cities, emerging applications for artificial intelligence in the energy transition, and the comeback of traditional ideas about how we design buildings in a warming climate. Here are four trends to keep an eye on. ‘

AI and renewable energy

The broad acceleration of both private and public investment in renewables, charging stations for electric vehicles, and electric heat pumps is continuing to amplify the complexity associated with managing electricity grids on both the supply and demand sides of the energy market. According to industry experts, the massive uptick in both AI computing power and emerging applications for large language models holds out the promise of sharply improved demand forecasting and grid management systems.

“Consulting firm Indigo Advisory has counted more than 50 possible uses for AI in the energy sector,” Reuters reports. “The company estimates that 100 vendors have already introduced AI solutions into their products and that the market for AI is now worth up to US $13 billion in the energy sector alone.”

In a commentary published in December, the IEA noted that links are deepening between the power system and the transportation, industry, building and industrial sectors. “The result is a vastly greater need for information exchange – and more powerful tools to plan and operate power systems as they keep evolving.”

The inputs run the gamut from the growing inventory of solar and wind farms to the data that is gathered by sensor-based home heating systems and shared with utilities or equipment providers.

Other highly promising applications, according to the IEA, include the use of machine learning algorithms and the deployment of sensors on grid assets like power lines to predict the maintenance requirements and thus reduce downtime in order to optimize energy production.

‘Unplanning’ reaches critical mass

Urbanists have understood for many years that dense, compact cities represent the most effective way to build low-carbon urban regions. But zoning laws and seemingly bottomless highway budgets have long conspired to produce, in North America anyway, a tenacious sprawl cycle that has greatly exacerbated global warming.

Yet the housing affordability crisis that has inundated big cities in many countries seems to have finally forced local, regional and national governments to cast aside some of the most sacrosanct principles of planning in favour of blanket “up-zonings” and a broad relaxing of other rules governing development and parking.

This epochal shift in outlook, akin to changing the direction of an ocean liner, didn’t begin last year, but it feels like 2023 will be remembered as a critical turning point – the moment when the post-World War II  planning consensus finally gave up the ghost.

Justin Trudeau’s Liberal government took the constitutionally risky step of using lucrative federal “housing accelerator” grants as a carrot/stick technique to force municipalities to increase minimum residential densities across the board, and the seeming success of this initiative has attracted the attention of a growing number of cities of various sizes.

In the U.S., Minneapolis, California and Oregon have led the way with zoning reforms that triggered a boom in so-called accessory dwelling units (small rear-yard dwellings) and multi-unit residential buildings with significantly less on-site parking. Last year, a bi-partisan “YIMBY” act was tabled in Congress and aims to provide a similar set of incentives as Canada’s Housing Accelerator grants. Also as in Canada, the momentum and expert consensus behind such reforms – which expand supply but don’t necessarily create a lot more affordable housing – means they’ll almost certainly spread to many other parts of the U.S. in the coming year.

The single-staircase ‘radicals’

For decades, multi-unit residential buildings have been required, and with good reason, to provide at least two sets of exits/entrances to satisfy fire safety regulations. But for generations prior to the advent of such rules, many apartment buildings, especially low-rise ones in the core areas of older cities, were constructed around a single central stairwell – a configuration that allowed apartments to have not only more exterior windows but also cross-breeze ventilation. (The staircases often encircle an elevator shaft.)

Why? The central staircase in those older buildings allowed units to, in effect, straddle a corner, which means that windows could be opened in such a way as to allow the air to flow through, thus providing inexpensive and low-carbon cooling and ventilation. In the apartments of the fire-code era, by contrast, apartments tend to be situated on either side of a central (aka “double loaded”) corridor, which means there’s no possibility of cross-ventilation.

Numerous climate-minded architects have been trying to crack the riddle of how to bring back single-staircase design without compromising safety. The other benefits, according to proponents: more efficient use of space, better light, better apartment floor plans, including layouts suitable for families with children.

A related trend is the design of mid-rise European-style apartment blocks built around a central courtyard and, crucially, single-loaded corridors that overlook this space. These corridors also permit cross-ventilation – they can be outdoor spaces or enclosed but with windows that open on one side into the apartments and on the other onto the courtyard space.

In North America, California is leading the push to reform its outdated building codes. Last October, the state legislature set in motion a process to develop changes to the building code meant to allow single-exit multi-family buildings.

Will 2024 be geothermal’s breakout year?

The prospect of tapping into the energy from the earth’s molten core has long been seen as one of the most promising forms of low-carbon heat. Volcanic regions, such as Iceland or central Turkey, have an abundance of relatively shallow reserves and do in fact rely extensively on geothermal energy. But in areas where the earth’s heat is much deeper, the cost of bringing geothermal energy to the surface has remained economically prohibitive.

In 2023, however, policy-makers in the U.S. and Canada stepped up investments in geothermal systems, through, for example, the Inflation Reduction Act’s 30% tax credit and a $200-million loan by the Canada Infrastructure Bank to Enbridge Sustain, which builds and operates residential clean energy systems. This could be geothermal’s breakout year.

Much of that funding is directed at stoking the geothermal heat-pump market for residential use, and in particular new subdivisions. In Barrie, Ontario, for example, Enbridge is partnering with a local builder, Sean, to construct a 73-unit development that will use geothermal, as well as other green building techniques, such as solar shading and cross-laminated timber.

Yet the development of large-scale, commercial geothermal power plants to provide baseload energy remains in its infancy in much of North America (except for hot spots in places like California), despite the enormous potential and falling costs due to technology improvements, which make it competitive with coal, nuclear and some solar applications.

The Swan Hills geothermal power project in Alberta came online in early 2023, and several others in Western Canada are in the pipeline. While the U.S. is the world leader, with more than 3,500 megawatts of geothermal capacity, countries like Indonesia, Turkey and the Philippines have seen substantial new investment in recent years, the IEA says.

At least one Canadian venture, Deep Corp., has raised $52 million in private and public capital to use spent oil and gas wells in Saskatchewan as conduits for accessing geothermal reserves. Its plant is expected to come online next year.

The post All is not lost: Four climate-saving trends for 2024 appeared first on Corporate Knights.

With the surge in demand for sustainability expertise and a growing number of AI-backed technologies, a vital question emerges: how will AI affect and transform roles in a discipline that continues to grow and evolve in response to socio-economic, cultural and environmental changes?

The prevailing narrative of job losses resulting from AI reflects the general scarcity mindset we live in; however, if we choose to position AI as an opportunity to improve collaboration and co-evolution, countless possibilities come into view.

A horizon of opportunities

For ESG professionals, the fourth industrial revolution – as this era of rapid technological advancement is known – represents a new horizon of opportunities. AI will create roles that demand a blend of technical acumen and deep sustainability expertise, making way for hybrid ESG roles and specialized roles that didn’t exist a decade ago.

“AI in the context of ESG should be viewed as a powerful tool in the arsenal of sustainability professionals,” says Sharmila Singh of Green Lens Consulting.

This means that sustainability specialists will get much-needed respite from sifting through voluminous sustainability reports, navigating convoluted regulatory guidelines, and crafting multifaceted reports catering to diverse stakeholders. Salesforce, the U.S. software giant, has already announced it will be using AI to automate aspects of its Net Zero Cloud ESG reporting tool. By automating repetitive tasks, ESG as a discipline can move from a retrospective review and reporting on a standard set of key performance indicators to much more interesting work that includes integration of ESG data into business strategy, stakeholder relationships, future-forward actions and innovation.

In a notable collaboration between Microsoft and the biodiversity platform iNaturalist, ESG professionals showcased the transformative power of AI in transitioning from mere data collection to meaningful action. The iNaturalist platform, which enables users to map and share biodiversity observations, faced challenges in processing vast amounts of data. Partnering with Microsoft’s AI for Earth initiative, the iNaturalist team implemented machine-learning models for automated species identification. This not only expedited data verification but also provided immediate, actionable insights. Researchers and conservationists were empowered to quickly move from collecting observations to implementing data-driven conservation strategies, highlighting the potential of AI in bridging the gap between data accumulation and actionable ESG outcomes.

We are already seeing a clear shift from generic ESG positions that encompass a wide range of skills and competency to specialized expertise that enhances the capacity of ESG professionals to influence strategic decision-making.

Striking the right balance: Ensuring AI reliability in ESG

Adaptability, lifelong learning and a forward-thinking mindset will be the cornerstones of success in this evolving landscape.

Despite the evident benefits of AI, “reliability is a concern,” cautions Toby Sparwasser Soroka, business development lead at Brrink. The Berlin-based company creates AI infrastructure that can be integrated into ESG workflows to make data collection more efficient so essential insights can be generated faster. “AI hallucinations, which involve generating seemingly accurate yet contextually erroneous outputs, are a real possibility.”

Given the intricacies of ESG regulations and the consequences of misreporting, it’s paramount to ensure the accuracy of AI outputs. Privacy concerns, too, loom large. The technology’s vast data collection can include sensitive information, while its powerful inference abilities can predict private attributes or behaviours without relying on direct data. The governance category – or “G” in ESG – is a logical place for a company’s disclosure on AI policy and use.

And AI further bolsters the need for the ESG professional to move from data collector to critical reviewer of data and context, thereby providing a layer of human scrutiny.

“AI isn’t a substitute but an enabler, augmenting the productivity of the existing workforce,” Soroka says. “The human touch – a nuanced understanding, strategic mindset and engagement skills – remains irreplaceable.”

As AI manages the routine tasks, there will be a growing demand for professionals who can navigate the intricate juncture of AI and ESG – experts who can understand the AI outputs, validate them and integrate them seamlessly into broader ESG strategies. ESG professionals equipped with AI tools are better positioned to navigate the evolving sustainability landscape, ensuring transparency, compliance and positive impact.

“What AI promises is to elevate these strengths by fostering efficiency,” Soroka says.

A symbiotic relationship

Singh highlights that “the symbiotic relationship between AI and ESG is the key to unlocking a sustainable future that balances profit with purpose.” She also raises the possibility of connecting AI to Indigenous knowledge in the context of sustainability. “How can we better connect AI to Indigenous knowledge to improve environmental management or address, avoid or minimize the impacts of changing climatic conditions, for example the wildfires in Hawaii?” she asks.

Moreover, with AI’s propensity to adapt and evolve, continuous learning becomes critical. ESG professionals must be equipped with not only deep sustainability knowledge but also a foundational understanding of AI mechanisms. This dual expertise will ensure that AI tools are used to their fullest potential while maintaining the sanctity of ESG metrics.

The job market will likely see a surge in positions focused on AI-ESG integration, AI ethics in ESG, and even roles dedicated to the continuous training and fine-tuning of these AI models, ensuring their outputs align with the rapidly evolving ESG standards.

The ESG landscape is on the brink of a transformative era. As AI becomes an integral tool in ESG reporting and management, the key lies in harmonizing the strengths of both. AI should not be viewed as a panacea but rather as an ally – one that can empower ESG professionals to craft a more sustainable and transparent corporate world.

Shilpa Tiwari is the founder and CEO of Isenzo Group, an ESG strategy and communications firm.

The post The AI revolution is reshaping ESG jobs appeared first on Corporate Knights.