Meta will finance Oklo's purchase of uranium for its reactors. It's a massive vote of confidence for both the startup and nuclear power, but challenges remain. There are two ways for tech companies to invest in nuclear power right now. One is to buy power from traditional reactors that are already built, either by purchasing electricity from the plants directly or financing the reconstruction of decommissioned units. The other is to invest in one of the dozens of reactor startups promising to commercialize designs and technologies never before used in the American market to generate electricity.

Why Google's custom AI chips are shaking up the tech industry Ironwood is Google's latest tensor processing unit Nvidia's position as the dominant supplier of AI chips may be under threat from a specialised chip pioneered by Google, with reports suggesting companies like Meta and Anthropic are looking to spend billions on Google's tensor processing units. The success of the artificial intelligence industry has been in large part based on graphical processing units (GPUs), a kind of computer chip that can perform many parallel calculations at the same time, rather than one after the other like the computer processing units (CPUs) that power most computers. 'Flashes of brilliance and frustration': I let an AI agent run my day GPUs were originally developed to assist with computer graphics, as the name suggests, and gaming. "If I have a lot of pixels in a space and I need to do a rotation of this to calculate a new camera view, this is an operation that can be done in parallel, for many different pixels," says Francesco Conti at the University of Bologna in Italy. This ability to do calculations in parallel happened to be useful for training and running AI models, which often use calculations involving vast grids of numbers performed at the same time, called matrix multiplication.

The second major cloud outage in less than two weeks, Azure's downtime highlights the "brittleness" of a digital ecosystem that depends on a few companies never making mistakes. Microsoft's Azure cloud platform, its widely used 365 services, Xbox, and Minecraft started suffering outages at roughly noon Eastern time on Wednesday, the result of what Microsoft said was "an inadvertent configuration change." The incident--which marks the second major cloud provider outage in less than two weeks--highlights the instability of an internet built largely on infrastructure run by a few tech giants. Microsoft's problems specifically originated from Azure's Front Door content delivery network and emerged just hours before Microsoft's scheduled earnings announcement. The company website, including its investor relations page, was still down on Wednesday afternoon, and the Azure status page where Microsoft provides updates was having intermittent issues as well.

Experts say outages like the one that Amazon experienced this week are almost inevitable given the complexity and scale of cloud technology--but the duration serves as a warning. A sprawling Amazon Web Services cloud outage that began early Monday morning illustrated the fragile interdependencies of the internet as major communication, financial, health care, education, and government platforms around the world suffered disruptions. As the day wore on, AWS diagnosed and began working to correct the issue, which stemmed from the company's critical US-EAST-1 region based in northern Virginia. But the cascade of impacts took time to fully resolve. Researchers reflecting on the incident particularly highlighted the length of Monday's outage, which started around 3 am ET on Monday, October 20.

Advances in low-communication training algorithms are enabling a shift from centralised model training to compute setups that are either distributed across multiple clusters or decentralised via community-driven contributions. This paper distinguishes these two scenarios - distributed and decentralised training - which are little understood and often conflated in policy discourse. We discuss how they could impact technical AI governance through an increased risk of compute structuring, capability proliferation, and the erosion of detectability and shutdownability. While these trends foreshadow a possible new paradigm that could challenge key assumptions of compute governance, we emphasise that certain policy levers, like export controls, remain relevant. We also acknowledge potential benefits of decentralised AI, including privacy-preserving training runs that could unlock access to more data, and mitigating harmful power concentration. Our goal is to support more precise policymaking around compute, capability proliferation, and decentralised AI development.

The Biden administration this week introduced new export restrictions designed to control AI's progress globally and ultimately prevent the most advanced AI from falling into China's hands. The rule is just the latest in a string of measures put in place by Donald Trump and Joe Biden to keep Chinese AI in check. With prominent AI figures including OpenAI's Sam Altman and Anthropic's Dario Amodei warning of the need to "beat China" in AI, the Trump administration may well escalate things further. Paul Triolo is a partner at DGA Group, a global consulting firm, a member of the council of foreign relations, and a senior adviser to the University of Pennsylvania's Penn Project on the Future of US-China Relations. Alvin Graylin is an entrepreneur who previously ran China operations for the Taiwanese electronics firm HPC.

For more than a decade, we have speculated about the impact of artificial intelligence (AI)/machine learning (ML) on the environmental sustainability of computing (see ACM2). It has become clear that Al's carbon emissions (scope 2), lifecycle carbon (scope 3), and other negative environmental impacts are growing explosively. Generative AI capabilities and applications exemplified and popularized in ChatGPT, DALL-E 2, Stable Diffusion, and Copilot, are the drivers. Giga$$$s of increased spending on AI computing equipment and infrastructure is driving a dramatic increase in infrastructure: AI computing silicon and datacenters. From May 2022 to April 2023 (12 months), Nvidia's datacenter group sold $15.5B of GPUs.

Jensen Huang getting carried away about an emerging technology is nothing new. This time last year, the charismatic and excitable co-founder and CEO of chip design giant Nvidia was telling anyone who'd listen about the potential of the metaverse (or the Omniverse, as Nvidia's marketing department prefers to call it). Since then, the metaverse bubble has suffered a slow puncture, and Huang is back to evangelising about one of his favourite topics: artificial intelligence. Describing the growth in power of generative AI systems like GPT-4 – the model that powers OpenAI's tools such as ChatGPT – as a "new era of computing", Huang told investors on his company's most recent earnings call that AI was at an "inflection point", stating that businesses have "an urgency to develop and deploy new AI strategies". However, Huang added that he believes many companies face "an insurmountable obstacle" in getting access to the resources and skills needed to make AI work, which is why, he says, Nvidia is getting into the services business.

A decade of big data investments, combined with cloud scalability, the rise of more cost effective processing and the introduction of advanced tooling, has catapulted machine intelligence to the forefront of technology investments. No matter what job you have, your operation will be AI powered within five years and machines may be doing your job in the future. Artificial intelligence is being infused into applications, infrastructure, equipment and virtually every aspect of our lives. AI is proving to be extremely helpful at controlling vehicles, speeding medical diagnoses, processing language, advancing science and generally raising the stakes on what it means to apply technology for business advantage. But business value realization has been a challenge for most organizations because of a lack of skills, complexity of programming models, immature technology integration, sizable up front investments, ethical concerns and lack of business alignment. Mastering AI technology and a focus on features will not be a requirement for success in our view. Rather, figuring out how and where to apply AI to your business will be the crucial gate.

As CTO of Solutions at Rackspace Technology, Jeff is fanatical about helping people and companies see more success with technology. For decades, since the 1950s when the term "artificial intelligence" (AI) was allegedly first coined, the concept of a machine brain that could think for itself, arrive at decisions and perform specific functions has been both tantalizing and frightening. It was tantalizing because it held the promise of greater efficiency, the elimination of mundane tasks and a world where machines could anticipate our needs, but it was frightening because of overheated visions of job losses in the billions and machines run amok. Even today, when we can see its positive effects in so many parts of our lives, "AI-phobia" is alive and well. Still, it's fair to say that despite breathless predictions, the advent of AI and its cousin machine learning (ML) has been slower to develop and far less threatening than many had imagined, despite massive leaps in computational ability, the rise of neural networks, more and more powerful chips and the ability to parse data in record time.