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Artificial intelligence technology behind ChatGPT was built in Iowa — with a lot of water

Share this Story : Artificial intelligence technology behind ChatGPT was built in Iowa – with a lot of water Copy Link Email Facebook X Reddit LinkedIn Tumblr Breadcrumb Trail LinksPMN WorldPMN NewsArtificial intelligence technology behind ChatGPT was built in Iowa – with a lot of waterAuthor of the article:The Associated PressMatt O’brien And Hannah FingerhutPublished

Artificial intelligence technology behind ChatGPT was built in Iowa – with a lot of water

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DES MOINES, Iowa (AP) — The cost of building an artificial intelligence product like ChatGPT can be hard to measure.

But one thing Microsoft-backed OpenAI needed for its technology was plenty of water, pulled from the watershed of the Raccoon and Des Moines rivers in central Iowa to cool a powerful supercomputer as it helped teach its AI systems how to mimic human writing.

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As they race to capitalize on a craze for generative AI, leading tech developers including Microsoft, OpenAI and Google have acknowledged that growing demand for their AI tools carries hefty costs, from expensive semiconductors to an increase in water consumption.

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But they’re often secretive about the specifics. Few people in Iowa knew about its status as a birthplace of OpenAI’s most advanced large language model, GPT-4, before a top Microsoft executive said in a speech it “was literally made next to cornfields west of Des Moines.”

Building a large language model requires analyzing patterns across a huge trove of human-written text. All of that computing takes a lot of electricity and generates a lot of heat. To keep it cool on hot days, data centers need to pump in water — often to a cooling tower outside its warehouse-sized buildings.

In its latest environmental report, Microsoft disclosed that its global water consumption spiked 34% from 2021 to 2022 (to nearly 1.7 billion gallons, or more than 2,500 Olympic-sized swimming pools), a sharp increase compared to previous years that outside researchers tie to its AI research.

“It’s fair to say the majority of the growth is due to AI,” including “its heavy investment in generative AI and partnership with OpenAI,” said Shaolei Ren, a researcher at the University of California, Riverside who has been trying to calculate the environmental impact of generative AI products such as ChatGPT.

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In a paper due to be published later this year, Ren’s team estimates ChatGPT gulps up 500 milliliters of water (close to what’s in a 16-ounce water bottle) every time you ask it a series of between 5 to 50 prompts or questions. The range varies depending on where its servers are located and the season. The estimate includes indirect water usage that the companies don’t measure — such as to cool power plants that supply the data centers with electricity.

“Most people are not aware of the resource usage underlying ChatGPT,” Ren said. “If you’re not aware of the resource usage, then there’s no way that we can help conserve the resources.”

Google reported a 20% growth in water use in the same period, which Ren also largely attributes to its AI work. Google’s spike wasn’t uniform — it was steady in Oregon where its water use has attracted public attention, while doubling outside Las Vegas. It was also thirsty in Iowa, drawing more potable water to its Council Bluffs data centers than anywhere else.

In response to questions from The Associated Press, Microsoft said in a statement this week that it is investing in research to measure AI’s energy and carbon footprint “while working on ways to make large systems more efficient, in both training and application.”

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“We will continue to monitor our emissions, accelerate progress while increasing our use of clean energy to power data centers, purchasing renewable energy, and other efforts to meet our sustainability goals of being carbon negative, water positive and zero waste by 2030,” the company’s statement said.

OpenAI echoed those comments in its own statement Friday, saying it’s giving “considerable thought” to the best use of computing power.

“We recognize training large models can be energy and water-intensive” and work to improve efficiencies, it said.

Microsoft made its first $1 billion investment in San Francisco-based OpenAI in 2019, more than two years before the startup introduced ChatGPT and sparked worldwide fascination with AI advancements. As part of the deal, the software giant would supply computing power needed to train the AI models.

To do at least some of that work, the two companies looked to West Des Moines, Iowa, a city of 68,000 people where Microsoft has been amassing data centers to power its cloud computing services for more than a decade. Its fourth and fifth data centers are due to open there later this year.

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“They’re building them as fast as they can,” said Steve Gaer, who was the city’s mayor when Microsoft came to town. Gaer said the company was attracted to the city’s commitment to building public infrastructure and contributed a “staggering” sum of money through tax payments that support that investment.

“But, you know, they were pretty secretive on what they’re doing out there,” he added.

Microsoft first said it was developing one of the world’s most powerful supercomputers for OpenAI in 2020, declining to reveal its location to AP at the time but describing it as a “single system” with more than 285,000 cores of conventional semiconductors, and 10,000 graphics processors — a kind of chip that’s become crucial to AI workloads.

Experts have said it can make sense to “pretrain” an AI model at a single location because of the large amounts of data that need to be transferred between computing cores.

It wasn’t until late May that Microsoft’s president, Brad Smith, disclosed that it had built its “advanced AI supercomputing data center” in Iowa, exclusively to enable OpenAI to train what has become its fourth-generation model, GPT-4. The model now powers premium versions of ChatGPT and some of Microsoft’s own products and has accelerated a debate about containing AI’s societal risks.

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“It was made by these extraordinary engineers in California, but it was really made in Iowa,” Smith said.

In some ways, West Des Moines is a relatively efficient place to train a powerful AI system, especially compared to Microsoft’s data centers in Arizona that consume far more water for the same computing demand.

“So if you are developing AI models within Microsoft, then you should schedule your training in Iowa instead of in Arizona,” Ren said. “In terms of training, there’s no difference. In terms of water consumption or energy consumption, there’s a big difference.”

For much of the year, Iowa’s weather is cool enough for Microsoft to use outside air to keep the supercomputer running properly and vent heat out of the building. Only when the temperature exceeds 29.3 degrees Celsius (about 85 degrees Fahrenheit) does it withdraw water, the company has said in a public disclosure.

That can still be a lot of water, especially in the summer. In July 2022, the month before OpenAI says it completed its training of GPT-4, Microsoft pumped in about 11.5 million gallons of water to its cluster of Iowa data centers, according to the West Des Moines Water Works. That amounted to about 6% of all the water used in the district, which also supplies drinking water to the city’s residents.

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In 2022, a document from the West Des Moines Water Works said it and the city government “will only consider future data center projects” from Microsoft if those projects can “demonstrate and implement technology to significantly reduce peak water usage from the current levels” to preserve the water supply for residential and other commercial needs.

Microsoft said Thursday it is working directly with the water works to address its feedback. In a written statement, the water works said the company has been a good partner and has been working with local officials to reduce its water footprint while still meeting its needs.

_-

O’Brien reported from Providence, Rhode Island.

__

The Associated Press and OpenAI have a licensing agreement that allows for part of AP’s text archives to be used to train the tech company’s large language model. AP receives an undisclosed fee for use of its content.

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DES MOINES, Iowa (AP) — The cost of building an artificial intelligence product like ChatGPT can be hard to measure.

But one thing Microsoft-backed OpenAI needed for its technology was plenty of water, pulled from the watershed of the Raccoon and Des Moines rivers in central Iowa to cool a powerful supercomputer as it helped teach its AI systems how to mimic human writing.

Article content

As they race to capitalize on a craze for generative AI, leading tech developers including Microsoft, OpenAI and Google have acknowledged that growing demand for their AI tools carries hefty costs, from expensive semiconductors to an increase in water consumption.

Advertisement 2
Story continues below
Article content

But they’re often secretive about the specifics. Few people in Iowa knew about its status as a birthplace of OpenAI’s most advanced large language model, GPT-4, before a top Microsoft executive said in a speech it “was literally made next to cornfields west of Des Moines.”

Building a large language model requires analyzing patterns across a huge trove of human-written text. All of that computing takes a lot of electricity and generates a lot of heat. To keep it cool on hot days, data centers need to pump in water — often to a cooling tower outside its warehouse-sized buildings.

In its latest environmental report, Microsoft disclosed that its global water consumption spiked 34% from 2021 to 2022 (to nearly 1.7 billion gallons, or more than 2,500 Olympic-sized swimming pools), a sharp increase compared to previous years that outside researchers tie to its AI research.

“It’s fair to say the majority of the growth is due to AI,” including “its heavy investment in generative AI and partnership with OpenAI,” said Shaolei Ren, a researcher at the University of California, Riverside who has been trying to calculate the environmental impact of generative AI products such as ChatGPT.

Advertisement 3
Story continues below
Article content

In a paper due to be published later this year, Ren’s team estimates ChatGPT gulps up 500 milliliters of water (close to what’s in a 16-ounce water bottle) every time you ask it a series of between 5 to 50 prompts or questions. The range varies depending on where its servers are located and the season. The estimate includes indirect water usage that the companies don’t measure — such as to cool power plants that supply the data centers with electricity.

“Most people are not aware of the resource usage underlying ChatGPT,” Ren said. “If you’re not aware of the resource usage, then there’s no way that we can help conserve the resources.”

Google reported a 20% growth in water use in the same period, which Ren also largely attributes to its AI work. Google’s spike wasn’t uniform — it was steady in Oregon where its water use has attracted public attention, while doubling outside Las Vegas. It was also thirsty in Iowa, drawing more potable water to its Council Bluffs data centers than anywhere else.

In response to questions from The Associated Press, Microsoft said in a statement this week that it is investing in research to measure AI’s energy and carbon footprint “while working on ways to make large systems more efficient, in both training and application.”

Advertisement 4
Story continues below
Article content

“We will continue to monitor our emissions, accelerate progress while increasing our use of clean energy to power data centers, purchasing renewable energy, and other efforts to meet our sustainability goals of being carbon negative, water positive and zero waste by 2030,” the company’s statement said.

OpenAI echoed those comments in its own statement Friday, saying it’s giving “considerable thought” to the best use of computing power.

“We recognize training large models can be energy and water-intensive” and work to improve efficiencies, it said.

Microsoft made its first $1 billion investment in San Francisco-based OpenAI in 2019, more than two years before the startup introduced ChatGPT and sparked worldwide fascination with AI advancements. As part of the deal, the software giant would supply computing power needed to train the AI models.

To do at least some of that work, the two companies looked to West Des Moines, Iowa, a city of 68,000 people where Microsoft has been amassing data centers to power its cloud computing services for more than a decade. Its fourth and fifth data centers are due to open there later this year.

Advertisement 5
Story continues below
Article content

“They’re building them as fast as they can,” said Steve Gaer, who was the city’s mayor when Microsoft came to town. Gaer said the company was attracted to the city’s commitment to building public infrastructure and contributed a “staggering” sum of money through tax payments that support that investment.

“But, you know, they were pretty secretive on what they’re doing out there,” he added.

Microsoft first said it was developing one of the world’s most powerful supercomputers for OpenAI in 2020, declining to reveal its location to AP at the time but describing it as a “single system” with more than 285,000 cores of conventional semiconductors, and 10,000 graphics processors — a kind of chip that’s become crucial to AI workloads.

Experts have said it can make sense to “pretrain” an AI model at a single location because of the large amounts of data that need to be transferred between computing cores.

It wasn’t until late May that Microsoft’s president, Brad Smith, disclosed that it had built its “advanced AI supercomputing data center” in Iowa, exclusively to enable OpenAI to train what has become its fourth-generation model, GPT-4. The model now powers premium versions of ChatGPT and some of Microsoft’s own products and has accelerated a debate about containing AI’s societal risks.

Advertisement 6
Story continues below
Article content

“It was made by these extraordinary engineers in California, but it was really made in Iowa,” Smith said.

In some ways, West Des Moines is a relatively efficient place to train a powerful AI system, especially compared to Microsoft’s data centers in Arizona that consume far more water for the same computing demand.

“So if you are developing AI models within Microsoft, then you should schedule your training in Iowa instead of in Arizona,” Ren said. “In terms of training, there’s no difference. In terms of water consumption or energy consumption, there’s a big difference.”

For much of the year, Iowa’s weather is cool enough for Microsoft to use outside air to keep the supercomputer running properly and vent heat out of the building. Only when the temperature exceeds 29.3 degrees Celsius (about 85 degrees Fahrenheit) does it withdraw water, the company has said in a public disclosure.

That can still be a lot of water, especially in the summer. In July 2022, the month before OpenAI says it completed its training of GPT-4, Microsoft pumped in about 11.5 million gallons of water to its cluster of Iowa data centers, according to the West Des Moines Water Works. That amounted to about 6% of all the water used in the district, which also supplies drinking water to the city’s residents.

Advertisement 7
Story continues below
Article content

In 2022, a document from the West Des Moines Water Works said it and the city government “will only consider future data center projects” from Microsoft if those projects can “demonstrate and implement technology to significantly reduce peak water usage from the current levels” to preserve the water supply for residential and other commercial needs.

Microsoft said Thursday it is working directly with the water works to address its feedback. In a written statement, the water works said the company has been a good partner and has been working with local officials to reduce its water footprint while still meeting its needs.

_-

O’Brien reported from Providence, Rhode Island.

__

The Associated Press and OpenAI have a licensing agreement that allows for part of AP’s text archives to be used to train the tech company’s large language model. AP receives an undisclosed fee for use of its content.

Article content
Comments

Postmedia is committed to maintaining a lively but civil forum for discussion and encourage all readers to share their views on our articles. Comments may take up to an hour for moderation before appearing on the site. We ask you to keep your comments relevant and respectful. We have enabled email notifications—you will now receive an email if you receive a reply to your comment, there is an update to a comment thread you follow or if a user you follow comments. Visit our Community Guidelines for more information and details on how to adjust your email settings.

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Key Takeaways

  • Microsoft shareholders will vote in December on a proposal driven by the NCPPR regarding Bitcoin investment.
  • NCPPR warns that Microsoft’s decision not to invest in Bitcoin could lead to shareholder litigation if Bitcoin’s value rises.

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Microsoft shareholders will vote in December on whether the company should assess investing in Bitcoin, a proposal driven by the National Center for Public Policy Research (NCPPR).

According to a report by Cointelegraph, the NCPPR warns that Microsoft could face shareholder litigation if it decides against Bitcoin investment and the digital asset’s value subsequently rises.

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With Decentralized AI and Tokenized Ownership, We Can Fight ‘The Six’

Opinion Share Share this article Copy link X icon X (Twitter) LinkedIn Facebook Email With Decentralized AI and Tokenized Ownership, We Can Fight ‘The Six’ Orthodox venture capital will never provide the resources for decentralized AI to take on Microsoft, Alphabet, Apple, et al. The only way is to supplant equity financing with user-owned, token-based

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With Decentralized AI and Tokenized Ownership, We Can Fight ‘The Six’

Orthodox venture capital will never provide the resources for decentralized AI to take on Microsoft, Alphabet, Apple, et al. The only way is to supplant equity financing with user-owned, token-based systems, says Michael J. Casey, Chairman of The Decentralized AI Society.

By Michael J. Casey|Edited by Benjamin Schiller
Updated Nov 1, 2024, 7:20 p.m. Published Nov 1, 2024, 7:16 p.m.
(Pixabay)

The past two days’ share price moves for the six most heavily capitalized companies in the U.S. tell you all you need to know about why we must urgently decentralize the artificial intelligence economy.

The first headlines were that the third-quarter profits and revenue from Microsoft, Alphabet, Apple, Meta and Amazon all beat or met expectations. Yet, with the exception of Amazon’s on Friday, Big Tech’s shares all sold off in response to their earnings announcements, dragging down with them chip-maker Nvidia, the sixth member of the group, whose quarterly reporting is scheduled a month later.

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What spooked investors were some daunting capital expenditure numbers on AI computing power and model development. Alphabet, for one, said it did $13 billion in capex last quarter and expects to do the same in this one while Meta upped its full-year projected spending to $38-40 billion. The giants are in a spending war as each tries to outrace the others toward AI supremacy. Each one of them stands to lose profit margins if it gets out of control.

Let’s be clear: between them, The Six are booking $1.8 trillion in annual revenues, a number that would put their combined inflows in 10th place of global country rankings if we viewed them as a proxy for national GDP – just behind the gross output of Brazil’s 220 million people. Meanwhile, The Six have a combined market capitalization of $15 trillion, capturing an astounding one third of the entire S&P 500 index. Despite – or perhaps because of – this unprecedented scorecard, these companies are relentlessly competing for world domination. Doing what great American companies have always done, they’re unleashing a competitive instinct that, in a normal capitalist economy of diversified goods and services, is the core driver of technological progress.

So, don’t worry about The Six. Worry about us. Because our problem amid the dizzying advance of AI is definitely not one of a shortfall in technological progress. It’s that this particular form of technological progress comes with risks to human autonomy and safety. And to mitigate them, the question of who controls AI’s development and whether their incentives are aligned with the broadest base of humanity is fundamental.

Just as was the case for Alphabet’s Google, Meta’s Facebook and Amazon’s marketplace, the development of these six companies’ large language models (LLMs) and other AI machinery is occurring within closed, black-box systems.They’ve ingested the troves of data we all unwittingly poured into internet sites, and have built highly complex codebases into which no one has visibility. Between them, they dominate all layers of the AI stack: the storage (Amazon Web Services), the chips for computation (Nvidia), the AI models (Microsoft, with its investment in Open AI), the data (Alphabet and Meta) and the devices we use to interact with AI services (Apple). They might be competing with each other, but they form a vertically diversified oligopoly. Or rather, given the undeniable power that their technology can wield over people’s lives, they’re an oligarchy. Indeed, the secrecy around the means by which they exercise that power is characteristic of most oligarchical dictatorships.

Toward the latter phase of the Web2 era, people eventually came to understand Bruce Schneier’s memorable observation that we are not the internet platforms’ customers; we are their products. With that awareness, we’re now also finally opening our eyes to how these companies have long been incentivized to modify people’s behavior in unhealthy ways to maximize shareholder returns. It is no longer controversial to talk of the psychological harm done by the algorithms of Facebook, YouTube, Tik Tok and their ilk, which were blatantly designed to exploit dopamine releases to encourage continued, addictive engagement.

When Frank McCourt and I published Our Biggest Fight in March 2024, we were overwhelmed by parents’ horror stories of the harm social media had done to their kids. And then a Harris Poll coordinated by NYU Professor Johathan Haidt found that young people are just as concerned: nearly half of Gen Z wishes that TikTok and X (Twitter) never existed, even as 83% of the same cohort said they spend four hours a day or more on social media.

So, if we now know of the harms, why on earth would we extend the same oligopolistic control structure into the AI era? AI will put the Web2 oligopoly on steroids.

This is why I believe the creation of distributed, collectively owned open-source AI is a vitally important use case for Web3 and blockchain technology. It’s the only way to avoid the problem of misaligned incentives.

Sure, there are technical challenges, such as the latency that, for now, makes distributed machine learning inefficient, the capacity limits of on-chain data, or the privacy risks inherent to public blockchains. But innovators are already hard at work on outside-the-box solutions to these problems, motivated by the huge economic and reputational payoff promised by overcoming them. And when they do, the inherent information advantages enjoyed by open systems over closed systems will give decentralized AI a fighting chance. Achieve that, and “DeAI” will represent not only the right moral path but also the economic winner.

Here’s the rub: time is not on our side. And the fight is heavily lopsided. As cited above, The Six have an unprecedented $15 trillion war chest. In the 2000s, Facebook and Google learned that their high-value share prices gave them a currency with which to relentlessly acquire startups that could either enhance or threaten their dominance. Now, The Six have even greater capacity to buy up and integrate whatever breakthroughs in AI are coming, be it in independent AI agents or more efficient systems of compute. Their financial clout means that the most important innovations, those that offer the best hope for a more decentralized AI economy, are at risk of being subsumed into their centralized system. Remember, they’re competing with each other and are incentivized to do whatever it takes to win.

To fight their centralized approach, we must flip the paradigm. Orthodox venture capital will never provide anywhere near enough resources for decentralized competitors to take on the big guys. The only way is to supplant equity financing models with full user-owned, token-based systems. In the future, when your home devices provide the compute and deliver your privacy-preserved data into open-source models that are proven to act in your interests, you will earn tokens for that work. And, with that currency, you will pay for all the cool services delivered by your personal AI agent. It’s a new, distributed financing and payments system for a new, decentralized AI economy. It is the only way.

Yet, to succeed, the crypto and blockchain industry has to reimagine itself. If startup founders see DeAI merely as a new source of get-rich-quick token-pump opportunities, or if the leaders of the Layer 1 platforms now turning to the field are fixated more on applications that temporarily drive up the dollar value of their tribe’s cryptocurrency rather than on those that address real, economy-wide problems, this movement will fail. To win this fight, this industry must become more interoperable. It must become more collaborative.

This is not to say we should squash the competitive instincts that are vital to innovation. But it is to acknowledge a need for better cross-industry organization. Through collaborative bodies such as the new Decentralized AI Society, different stakeholders can work with each other to advance common interests around standards, reference architectures, taxonomies, policy objectives and open-source, cross-chain protocols that everyone can use regardless of the token they hold. We’re not building to pump our bags or take our token “to the moon.” We’re building to create a new decentralized AI economy for the benefit of all humanity.

Come join the fight.

Note: The views expressed in this column are those of the author and do not necessarily reflect those of CoinDesk, Inc. or its owners and affiliates.

Note: The views expressed in this column are those of the author and do not necessarily reflect those of CoinDesk, Inc. or its owners and affiliates.

Opinion
Michael J. Casey

Michael J. Casey is Chairman of The Decentralized AI Society, former Chief Content Officer at CoinDesk and co-author of Our Biggest Fight: Reclaiming Liberty, Humanity, and Dignity in the Digital Age. Previously, Casey was the CEO of Streambed Media, a company he cofounded to develop provenance data for digital content. He was also a senior advisor at MIT Media Labs’s Digital Currency Initiative and a senior lecturer at MIT Sloan School of Management. Prior to joining MIT, Casey spent 18 years at The Wall Street Journal, where his last position was as a senior columnist covering global economic affairs.

Casey has authored five books, including “The Age of Cryptocurrency: How Bitcoin and Digital Money are Challenging the Global Economic Order” and “The Truth Machine: The Blockchain and the Future of Everything,” both co-authored with Paul Vigna.

Upon joining CoinDesk full time, Casey resigned from a variety of paid advisory positions. He maintains unpaid posts as an advisor to not-for-profit organizations, including MIT Media Lab’s Digital Currency Initiative and The Deep Trust Alliance. He is a shareholder and non-executive chairman of Streambed Media.

Casey owns bitcoin.

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