nuclear-news

What does Google’s move into nuclear power mean for AI – and the world?

” tech companies operate: as supranational organisations that manage to bend countries’ regulation to their will,”

In today’s newsletter: Google will soon use nuclear reactors to run its AI datacentres. What are the economic, ethical and environmental implications?

Archie Bland, Wed 16 Oct 2024

Good morning. If you were looking for an inkblot test for your view of big tech’s investment in artificial intelligence, you could hardly do better than the news that Google is ordering the construction of at least six small nuclear reactors to power the growth of the technology.

Here, in one view, is an enlightened business leveraging its size to invest in infrastructure that could change the world for the better. Here, in another, is a poorly regulated corporation ignoring democratic objections in the brutal race for control of an innovation with great potential to do harm – and leaving the rest of us with little say in its development.

Google is making this eye-catching move because the datacentres that power the explosive growth of generative AI consume huge amounts of electricity – more than the existing grid in the US or other western nations can readily supply. For today’s newsletter, I spoke to technology journalist Chris Stokel-Walker, author of How AI Ate the World, about why the demand for power is growing so quickly – and whether we can trust big tech to handle the consequences. Here are the headlines.

In depth: Why AI needs so much power – and what big tech will do to get it

They might be called “small nuclear reactors”, but don’t be fooled: the 500MW Google is buying from Kairos Power is enough to power a midsize city. To begin to understand the scale of the demand AI puts on the electricity grid, keep in mind that this is only enough to cover one datacentre campus equipped to handle the growing demands of AI. One company alone, OpenAI, is trying to get the White House to sign off on building at least five datacentres, needing 5GW each of power – 10 times as big.

The reason for this nuclear power rush: the vast energy consumption of the computer chips (called graphics processing units or GPUs) that power the training of the large language models crucial to the development of AI. Meanwhile, a ChatGPT query needs nearly 10 times as much electricity to process as a Google search.

“GPUs are more advanced and more powerful than the CPUs [central processing units] of the previous generation of datacentres,” Chris Stokel-Walker said. “So there’s more demand there immediately. But we are also starting to see massive ‘megaclusters’ of GPUs. It’s not just the individual chips getting bigger and needing more power: it’s the race to get as many together to amplify their power as possible.”

How much impact will AI’s demand for power have?

“The challenge in estimating this is that the companies are pretty coy about telling us their power usage,” said Chris. “But there is a settled understanding that the energy used by datacentres is going to increase hugely as AI becomes layered into everything we do.”

The increase in demand already is significant: where the average datacentre drew 10MW of power a decade ago, they need 100MW today. And the biggest can already demand more than 600MW each.

The New York-based Uptime Institute, which has created a benchmarking system that is now industry standard, predicts that whereas AI only accounts for 2% of global datacentres’ power use today, that will reach 10% by next year. “The growth in power consumption is not linear,” Chris said. “In the same way that we used to have whacking great transistors behind our TVs and now we have flatscreens with eco-friendly modes, they are getting more efficient. But that doesn’t mean it’s not going upwards – just that it’s going up more slowly.”

How are tech companies trying to get the power to meet their needs?

By building it or paying others to do so. And because most governments expect that control of AI will be crucial to their ability to compete globally in the future, tech firms have a very strong hand when negotiating what to build and where.

“The argument tech companies are making, and that they’re trying to cement in the minds of decision-makers around the world, is: you either buy into this and sign up, or you run the risk of falling behind,” Chris said.

This New York Times piece lays out a case study of how that plays out in practice. It reports that as part of a recent fundraising effort, OpenAI’s CEO, Sam Altman, told executives at a Taiwanese semiconductor company that it would cost about $7tn (£5.6tn) to fulfil his vision of 36 semiconductor plants and additional datacentres. That’s about a quarter of the total US annual economic output. OpenAI denies that claim, and says that its plans run to the hundreds of billions of dollars.

Meanwhile, Altman has also been considering building these centres in other countries, including the United Arab Emirates. But there are fears in Washington that placing the centres there could give China a back door to American AI advances, because of the links between Chinese and Emirati universities. And at the same time, Altman is exploring plans for centres within the US.

“The warning is being used as a stick alongside the carrot,” Chris said. “They’re saying: if you don’t do this, we will go elsewhere, and you will not just lose the investment, but face a national security risk.”

What is the potential impact on the climate?

Big tech companies insist they are leaning into renewable sources of power as much as possible – and argue that AI could ultimately be a crucial tool to limit the damage caused by the climate crisis.

It is true that tech firms’ investment in renewable sources of energy has played an important part in their growth. But claiming that AI will help defeat the climate crisis is a theoretical benefit that won’t be seen until some point in the fairly distant future. And there are claims that emissions caused by current energy usage from datacentres owned by the likes of Google, Microsoft and Meta are much higher than they admit publicly.

In this piece published last month, Isabel O’Brien reported that big tech firms are using renewable energy credits – which may not actually be used to power the datacentres themselves and which may not even reduce emissions – to artificially deflate their reported emissions. That means the actual figures could be more than seven times higher than the numbers they report.

What about the use of nuclear power?

Google says its experiment makes it the first company in the world to buy nuclear energy from small nuclear reactors. But Amazon and Microsoft have already struck deals with conventional, larger nuclear power plants in the US this year. Don’t panic, but Microsoft’s deal will for the first time in five years activate a nuclear reactor at Three Mile Island in Pennsylvania – the site of the worst nuclear meltdown in US history. Sensibly, they’re emphasising its history of safe operation since the 1979 disaster at another reactor there – and renaming it.

With datacentres estimated to be on track to produce about 2.5bn tonnes of carbon-dioxide equivalent emissions by 2030, there is an environmental argument for the use of nuclear power. But that is a highly controversial case, which, because of the associated risks, has been the subject of charged democratic debate for many years. Wherever you stand on that question, it is remarkable that these companies appear to be able to simply decide on their own.

“One of the things that’s really striking here is what it says about how tech companies operate: as supranational organisations that manage to bend countries’ regulation to their will,” Chris said.

On the other hand, Google argues its investment in small nuclear reactors could be a necessary boost to a technology that has struggled to get off the ground. “In the end, some of this does trickle down,” said Chris. “They tend to commercialise technologies in a safe way. But it takes a long time, and the benefits are unequally distributed.”

Can governments bring these changes under control?

There are well-documented issues with regulating tech firms: without globally enforced agreements, there will always be another country ready to offer a better deal. See, for example, Ireland’s status as the European home of many big techfirms because of its favourable tax regime.

Regulation does not necessarily need to be globally agreed to be effective, however: in California, for example, new legislation intended to combat greenwashing will soon require all private companies with global revenue above $1bn to publish details of their carbon footprint. Since any big tech firm is bound to want to maintain operations in California, that could have much wider ramifications.

The impact of attempts at regulation and better data collection on the growth of AI may also depend on whether tech firms willingly cooperate – and if not, whether there is an appetite to force them to. The controversy over renewable energy credits is an example of how vexed even apparently positive steps can be.

And big tech firms have a valuable card in their hand: the desperate desire among governments around the world to win the AI race. “These companies point to astronomical figures of expected improvements in GDP and they say, this is the wave that is coming,” Chris said. “You can either ride it, or drown.”

October 17, 2024 Posted by Christina Macpherson | technology | Leave a comment

A new military-industrial complex: How tech bros are hyping AI’s role in war

The current debate on military AI is largely driven by “tech bros” and other entrepreneurs who stand to profit immensely from militaries’ uptake of AI-enabled capabilities.

the new military-industrial complex wherein business leaders are framing the future direction of war, despite their lack of military experience.

Bulletin, By Paul Lushenko, Keith Carter | October 7, 2024

Since the emergence of generative artificial intelligence, scholars have speculated about the technology’s implications for the character, if not nature, of war. The promise of AI on battlefields and in war rooms has beguiled scholars. They characterize AI as “game-changing,” “revolutionary,” and “perilous,” especially given the potential of great power war involving the United States and China or Russia.

In the context of great power war, where adversaries have parity of military capabilities, scholars claim that AI is the sine qua non, absolutely required for victory. This assessment is predicated on the presumed implications of AI for the “sensor-to-shooter” timeline, which refers to the interval of time between acquiring and prosecuting a target. By adopting AI, or so the argument goes, militaries can reduce the sensor-to-shooter timeline and maintain lethal overmatch against peer adversaries…………………..

 It encourages policymakers and defense officials to follow what can be called a “primrose path of AI-enabled warfare,” which is codified in the US military’s “third offset” strategy……………………

The current debate on military AI is largely driven by “tech bros” and other entrepreneurs who stand to profit immensely from militaries’ uptake of AI-enabled capabilities. Despite their influence on the conversation, these tech industry figures have little to no operational experience, meaning they cannot draw from first-hand accounts of combat to further justify arguments that AI is changing the character, if not nature, of war. Rather, they capitalize on their impressive business successes to influence a new model of capability development through opinion pieces in high-profile journals, public addresses at acclaimed security conferences, and presentations at top-tier universities.

To the extent analysts do explore the implications of AI for warfighting, such as during the conflicts in Gaza, Libya, and Ukraine, they highlight limited—and debatable—examples of its use, embellish its impacts, conflate technology with organizational improvements provided by AI, and draw generalizations about future warfare.

It is possible that AI-enabled technologies, such as lethal autonomous weapon systems or “killer robots,” will someday dramatically alter war. Yet the current debate for the implications of AI on warfighting discounts critical political, operational, and normative considerations that imply AI may not have the revolutionary impacts that its proponents claim, at least not now.

………………………………………………….. Our research suggests that three related considerations have combined to shape the hype surrounding military AI, informing the primrose path of AI-enabled warfare. First, that primrose path is paved by the emergence of a new military industrial complex that is dependent on commercial service providers. Second, this new defense acquisition process is the cause and effect of a narrative suggesting a global AI arms race, which has encouraged scholars to discount the normative implications of AI-enabled warfare. Finally, while analysts assume that soldiers will trust AI, which is integral to human-machine teaming that facilitates AI-enabled warfare, trust is not guaranteed.

………………………………………………………………….The primrose path of AI-enabled warfare is paved by a new military-industrial complex. Countries typically acquire military technologies, such as drones, for reasons that relate to supply, demand, and status considerations………………………………………………………………………

The political economy of the primrose path of AI-enabled warfare is different. It flips these defense acquisition processes on their heads such that industry drives, rather than responds to, militaries’ requirements for new capabilities. This approach reflects the United States’ historical preference for technology standards that are based on a “bottom-up, laissez-faire corporate-led strategy,” which emphasizes the anticipated economic advantages of leading-sector innovation.

These industry drivers consist of businesses that are funded by venture capitalists, including Anduril, Black Cape, Inc., Clarifai, CrowdAI, and ScaleAI; established defense contractors such as AWS, ECS Federal, IBM, Maxar, Microsoft, Palantir, Raytheon, and the Sierra Nevada Corporation; and business magnates like Elon Musk, Palmer Luckey, and Eric Schmidt. ………………………………………….. Luckey, founder of Anduril, promises to “save western civilization…as we make tens and tens of billions of dollars a year.”

Similarly, Musk’s Starlink uses low-earth orbit satellites to provide militaries’ assured communication in expeditionary and contested environments. Earlier in its war with Russia, Musk decided if Ukraine could use the Starlink satellite network, thus shaping the country’s military operations against Russia on the basis of his fears of crisis escalation. Schmidt’s new start-up, White Stork (previously Swift Beat), is designed to develop fully-autonomous drones. Schmidt, capitalizing on his previous roles as Chairman of the National Security Commission on AI and Director of the Defense Innovation Board, also instantiates the new military-industrial complex wherein business leaders are framing the future direction of war, despite their lack of military experience.

……………………………………………………………………………..the primrose path of AI-enabled warfare is also shaped by a military-industrial complex that provides technical warfighting solutions as a service, meaning they often do not respond to validated military requirements. Thus, companies’ have hedged their bets, investing billions of dollars into end-to-end AI-enabled technologies that they assume militaries will need to purchase to maintain lethal overmatch of adversaries during future conflict. This also means that businesses, especially their software engineers referred to as field engineers, are embedded within military organizations to an unprecedented degree that may muddle the legitimate use of force, at least for some critics.

………………………………………….. an assumption that a monopoly over these technologies will result in economic gains that undergird military power and shape the global balance of power. Russian president Vladimir Putin argued that whoever leads the development of AI will dominate the world; President Xi Jinping intends for China to surpass the United States as the world’s leader of AI development by 2030; and the United States is outspending other countries for AI development……………………………….survey research in the United States shows that support for AI-enabled warfare among both the public and military is strongly shaped by a perceived AI arms race globally.

This perspective has implications for the legal, moral, and ethical considerations that shape countries’ use of force, which scholars emphasize to greater or lesser degrees when characterizing future war. Skeptics caution that AI-enabled warfare will deskill humans and supplant their agency, leading to unintended consequences including crisis escalation, civilian casualties, and accountability and responsibility gaps for these outcomes.

………………………………………………………….Soldiers do not trust AI. The military-industrial complex, and the narratives of an AI arms race that encourages it, assumes that soldiers will trust human-machine teaming. In a recent opinion piece with Schmidt, Mark Milley, formerly chairman of the US Joint Chiefs of Staff, pontificated that “soldiers could sip coffee in their offices, monitoring screens far from the battlefield, as an AI system manages all kinds of robotic war machines.” Despite this sanguine prediction, it is unclear what shapes soldiers’ trust in AI, thus encouraging them to overcome inherent skepticism of machines.

…………………………………………………………………. Our findings suggest that soldiers’ trust in AI is not a foregone conclusion. Further, we found that trust is complex and multidimensional. Importantly, these findings are consistent across the military ranks.

First, senior officers do not trust AI-enhanced capabilities. …………………………………………………………………………………

Visualizing the future of war. Still, it is not only likely, but probable, that AI will shape future warfare in unique ways. As discussed above, the development of AI for commercial applications is re-ordering the defense acquisitions process…………………………………………………………………….

The use of AI to create novel text, images, and video will likely exacerbate the challenge of cognitive warfare. This form of non-lethal warfare explains the social engineering of adversaries’ beliefs, with the overall intent to affect their defense priorities, military readiness, and operations. In this way, countries will attempt to harness AI to produce misinformation and disinformation, which are designed to mislead and deceive opponents, and across the competition continuum ranging from peace to war.

During competition, countries will likely use AI to stoke social, political, and economic grievances among their opponents, such that their defense planning and military readiness are embroiled by increasing levels of partisanship, social unrest, and even political violence…………………………………………………………………………………….

During armed conflict, the confusion created by AI-generated psychological operations will threaten situational awareness required for timely decision-making. In the worst case scenarios, this could cause misidentification of friendly forces, leading to fratricide…………………………………………………………….

In the more distant future, as AI matures, further delegation of military operations would likely go to autonomous systems. This is often referred to as minotaur warfare, such that machines control humans during combat and across domains, which can range from patrols of soldiers on the ground to constellations of warships on the ocean to formations of fighter jets in the air…..  https://thebulletin.org/2024/10/a-new-military-industrial-complex-how-tech-bros-are-hyping-ais-role-in-war/

October 13, 2024 Posted by Christina Macpherson | technology | Leave a comment

Unrealistic expectations for So-Called Novel Nuclear Reactor Concepts

 Analysis and Evaluation of the Development Status, Safety and Regulatory
Framework for So-Called Novel Reactor Concepts.

For all technology lines considered, extensive research and development work has been taking place for several decades, in some cases since the middle of the last century.

Depending on the technology line, technical test stands for individual
phenomena have been built and operated, and so have smaller experimental
reactors (for SFR, for example, the U.S. EBR-I and II plants or the Russian
BR-10 and Bor-60) and larger demonstration reactors (for SFR, for example,
the French Phoenix and Super-Phoenix plants or the Russian BN-350 or BN-600
plants).

Nevertheless, until today no commercially competitive reactor
concept exists in the field of SNR. To plan, license, construct and operate
such experimental and demonstration reactors, a period of at least one to
two decades must be assumed for each reactor project, probably
substantially more based on historical experience. The knowledge gained
with these facilities needs to be evaluated and incorporated into the
technical design of an eventual prototype reactor.

The expectation, often expressed in public discourse and by developers themselves, that SNR
concepts can make a significant contribution to solving today’s problems in
nuclear technology cannot be considered realistic in view of the current
state of development of these systems and the actually proven and expected
advantages and disadvantages of the individual technology lines.

 German Federal Office for the Safety of Nuclear Waste Management
(accessed) 18th Sept 2024

https://www.base.bund.de/SharedDocs/Downloads/BASE/EN/expert-info/f/final-report-novel-reactor-concepts.pdf

September 20, 2024 Posted by Christina Macpherson | technology | Leave a comment