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The Misstated Emissions Developers working for Google have significantly misstated how much carbon two proposed AI datacentres will contribute to the UK’s total emissions in planning documents reviewed by the Guardian. The tech company wants to build two huge datacentres – one 52-hectare (130 acre) project in Thurrock and another at an airfield in North Weald, both in Essex. To do so, developers are required to submit planning documents calculating how much carbon these projects will emit as a proportion of the UK’s total carbon footprint. The Calculation Error In both cases, they appear to have compared one year of the proposed datacentre’s emissions with the UK’s entire five-year carbon budget, understating the significance of their emissions by a factor of five, according to experts at the tech justice nonprofit Foxglove. Google's Thurrock datacentre claimed its emissions would amount to 0.033% of the UK’s budgeted carbon footprint between 2028 and 2032, but it will actually be 0.165% of the total. The North Weald datacentre said it would emit 0.043% of the UK’s total carbon budget from 2033 to 2037, but it will actually emit 0.215% of the total. The Impact Analysis These apparent misstatements are another example of a pile-up of faulty calculations surrounding AI development and its environmental footprint in the UK. The three developments will account for more than 1% of the UK’s carbon budget in 2033, equivalent to the emissions of a mid-sized city such as Bristol. The Prediction “Google has serious questions to answer about its dubious datacentre pollution figures,” said Tim Squirrell, the head of strategy for Foxglove. “Unless they can explain themselves, it looks like they are seriously misleading the council and the public over the climate pollution their facility will cause.”

The US Government's Access to AI Models Tech giants Microsoft, Google, and xAI have agreed to allow the United States federal government access to their new artificial intelligence models for national security testing. The Center for AI Standards and Innovation (CAISI) Agreement The Center for AI Standards and Innovation (CAISI) at the Department of Commerce announced the agreement on Tuesday amid increasing concerns about the capabilities that Anthropic’s newly unveiled Mythos model could give hackers. The Data Analysis and Testing Under the new agreement, the US government will be allowed to evaluate the models before deployment and conduct research to assess their capabilities and security risks. Microsoft will work with US government scientists to test AI systems “in ways that probe unexpected behaviors”. The Impact Analysis on National Security Concern is growing in Washington over the national security risks posed by powerful AI systems. By securing early access to frontier models, US officials are aiming to identify threats ranging from cyberattacks to military misuse before the tools are widely deployed. The Future Outlook and Implications The move builds on 2024 agreements with OpenAI and Anthropic under President Joe Biden’s administration. CAISI, which serves as the government’s main hub for AI model testing, said it had already completed more than 40 evaluations, including on cutting-edge models not yet available to the public.

The Strategic Alliance Between SpaceX and AnthropicAnthropic announced a landmark agreement to tap the full computing capacity of SpaceX’s Colossus 1 facility in Memphis, Tennessee. The deal marks a rapid shift from previous criticism to collaboration, providing the Claude chatbot maker with a massive boost in AI‑compute resources.Colossus 1: 220,000 Nvidia GPUs Deliver 300 MW to ClaudeUnder the terms disclosed on Wednesday, Anthropic will access:More than 220,000 Nvidia processors housed in the Colossus 1 data centre.300 megawatts of power—enough for over 300,000 homes—to be added within a month.Dedicated capacity for the Claude Pro and Claude Max AI assistants, enabling higher request volumes and removal of peak‑hour caps.The new “dreaming” feature unveiled at Anthropic’s developer day will also benefit from the expanded hardware, allowing AI agents to retain context across sessions.Capacity Surge Translates to Billions in AI Compute ValueIndustry analysts estimate that each megawatt of AI‑focused compute can be valued at roughly $10 million per year, suggesting the 300 MW addition could represent a $3 billion annual capability boost for Anthropic. The partnership also positions SpaceX to monetize its under‑utilised GPU fleet, diversifying revenue beyond launch services.Ripple Effects Across the AI Landscape and U.S. PolicyThe deal arrives amid Musk’s ongoing lawsuit against OpenAI and its CEO Sam Altman, intensifying competition for compute resources. While Microsoft, Google and Musk’s own xAI are negotiating government access to AI tools, Anthropic was excluded from recent Pentagon contracts, highlighting a potential strategic disadvantage that the SpaceX alliance aims to offset.Furthermore, the agreement fuels Musk’s long‑term vision of orbital data centres, signaling a possible new frontier for ultra‑large‑scale AI infrastructure.Future Trajectory: Orbital Data Centres and Competitive PressuresAnthropic plans to explore “multiple gigawatts” of space‑based compute with SpaceX, a venture that could redefine latency‑critical AI services. If successful, the partnership may force rivals to secure comparable high‑density compute, accelerating a race for both terrestrial and orbital AI super‑clusters.In the short term, expect Anthropic to double rate limits for paid users, remove usage caps, and roll out the “dreaming” capability broadly, while SpaceX will likely package its GPU assets as a commercial service for other AI firms.

Breaking Down the Complex Language of AI Artificial intelligence is changing the world, and simultaneously inventing a whole new language to describe how it’s doing it. Spend five minutes reading about AI and you’ll run into LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel insecure. This glossary is our attempt to fix that. We update it regularly as the field evolves, so consider it a living document, much like the AI systems it describes. Artificial General Intelligence (AGI) Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that’s more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman once described AGI as the “equivalent of a median human that you could hire as a co-worker.” Meanwhile, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Confused? Not to worry — so are experts at the forefront of AI research. AI Agent An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so “AI agent” might mean different things to different people. Infrastructure is also still being built out to deliver on its envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks. API Endpoints Think of API endpoints as “buttons” on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations — for example, allowing one application to pull data from another, or enabling an AI agent to control third-party services directly without a human manually operating each interface. Most smart home devices and connected platforms have these hidden buttons available, even if ordinary users never see or interact with them. As AI agents grow more capable, they are increasingly able to find and use these endpoints on their own, opening up powerful — and sometimes unexpected — possibilities for automation. Chain-of-Thought Reasoning Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller, a giraffe or a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows). Coding Agent This is a more specific concept that an “AI agent,” which means a program that can take actions on its own, step by step, to complete a goal. A coding agent is a specialized version applied to software development. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code autonomously, handling the kind of iterative, trial-and-error work that typically consumes a developer’s day. Compute Although somewhat of a multivalent term, compute generally refers to the vital computational power that allows AI models to operate. This type of processing fuels the AI industry, giving it the ability to train and deploy its powerful models. The term is often a shorthand for the kinds of hardware that provides the computational power — things like GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry. Deep Learning A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees.

The Lead At the Milken Institute Global Conference, leaders from across the AI supply chain gathered to discuss the current state and future of artificial intelligence. They touched on various challenges, including chip shortages, energy constraints, and the potential for new AI architectures. The Bottlenecks in AI Development The discussion highlighted several bottlenecks in AI development. Christophe Fouquet, CEO of ASML, noted that despite efforts to accelerate chip manufacturing, the market will likely remain supply-limited for the next two to five years. Francis deSouza, COO of Google Cloud, pointed out the immense demand for AI infrastructure, with Google Cloud's revenue growing 63% and its backlog nearly doubling to $460 billion. The Data and Energy Constraints Qasar Younis, co-founder and CEO of Applied Intuition, emphasized that the bottleneck for his company is not silicon but data gathered from the real world, which is essential for training physical AI models. The energy required to power AI infrastructure is also a significant concern. deSouza mentioned that Google is exploring data centers in space to address energy constraints, although this comes with its own set of challenges. New AI Architectures and Their Implications Eve Bodnia, founder of Logical Intelligence, discussed a different approach to AI, focusing on energy-based models (EBMs) that aim to understand the underlying rules of data, similar to human brain function. This approach could be particularly useful for applications requiring an understanding of physical rules, such as chip design and robotics. The Future of AI: Agents, Guardrails, and Trust Dmitry Shevelenko, chief business officer of Perplexity, talked about the evolution of its search product into a 'digital worker' called Perplexity Computer. This tool is designed to act as a staff that a knowledge worker can direct, raising questions about control and security. Shevelenko emphasized the importance of granularity in permissions and actions to ensure trust and security. The Geopolitical and Generational Impact The discussion also touched on the geopolitical implications of physical AI and its impact on national sovereignty. Younis noted that physical AI manifests in the real world in ways that governments can't ignore, leading to questions about safety, data collection, and control. Regarding the impact on the next generation, the panelists were optimistic, highlighting the potential for AI to help address significant problems and unleash new levels of creativity and opportunity.

The Unexpected Partnership On Wednesday, xAI and Anthropic announced a surprise partnership that has the Claude-maker buying out "all of the compute capacity at [xAI's] Colossus 1 data center," roughly 300MW that allowed Anthropic to immediately raise its usage limits. It's a huge deal for xAI, likely worth billions of dollars. More importantly, it immediately monetized one of the company's most impressive accomplishments, turning xAI from a consumer to a provider of compute. The Strategic Implications It's tempting to see the arrangement as a shot at OpenAI amid the ongoing lawsuit. But Musk's explanation on X was that xAI had already moved training to a newer data center, Colossus 2, and xAI simply didn't need them both. In the short term, there's an obvious logic at work. xAI's existing products are mostly focused on Grok, which has seen plummeting usage since the image generation debacles earlier this year. The Financial Impact xAI's partnership with Anthropic is likely worth billions of dollars. xAI was valued at $230 billion in its January funding round. CoreWeave, which oversees a comparable quantity of computing power, is worth less than a third of that. The Industry Context But beyond the short-term benefit, the Anthropic partnership sends an unusual message about where Elon Musk's priorities really lie. It suggests the company's real business may be more about building data centers than training AI models. It's rare to see a major tech company treat compute resources this way when companies like Google and Meta, which are also training models, are building more data centers. The Future Outlook By focusing on data centers (earthbound and otherwise), xAI is positioning itself more like a neocloud business: buying GPUs from Nvidia and renting them out to model developers like Anthropic. It's a far more difficult business, squeezed by both chip suppliers and the shifting cycles of demand. Musk's version of a neocloud is more ambitious, as you might expect. Some of the data centers might be in space — at least by 2035, if things go according to plan.

The Settlement Details Apple has agreed to pay $250 million to settle a class-action lawsuit over how it marketed its AI features ahead of the launch of the iPhone 16. The lawsuit alleged that Apple exaggerated the breadth of features Apple Intelligence would bring, which included a significantly upgraded version of its assistant, Siri. The Allegations Against Apple The complaint alleges that the company created the impression that advanced AI capabilities would be available to users sooner than they actually were. In particular, the plaintiffs allege that Apple overstated both the readiness and functionality of these features, particularly the promised improvements to Siri, which have yet to fully materialize. The Financial Impact Apple will pay up to $250 million to settle the lawsuit. Eligible U.S. customers who purchased the iPhone 15 or iPhone 16 between June 10, 2024, and March 29, 2025, could receive up to $95 per device. The Future of Siri Apple has been touting a more advanced version of Siri ever since it unveiled Apple Intelligence in 2024 during WWDC. The anticipated updates are expected to help Siri function more like modern AI chatbots such as ChatGPT or Claude. The upgraded experience is rumored to be powered by Google Gemini, though newer reports state the company’s next iPhone operating system may let users choose from a number of third-party large language models. The Upcoming Developer Conference The settlement arrives ahead of Apple’s annual developer conference on June 8, when the company is expected to preview a version of its AI-enhanced Siri.

Apple's AI Strategy Shift Apple is set to revolutionize its iOS experience with the upcoming release of iOS 27, later this year. The new operating system will introduce a feature called 'Extensions,' allowing iPhone users to choose from a variety of third-party large language models to power different functions within the iPhone's operating system. The 'Extensions' Feature The 'Extensions' feature will enable users to access generative AI capabilities from installed apps on demand, through Apple Intelligence features such as Siri, Writing Tools, Image Playground, and more. This move is expected to be available not only for iOS 27 but also for iPadOS 27 and macOS 27. AI Model Options Models from Google and Anthropic are currently being tested. The status of ChatGPT, currently available to users, remains unclear but may continue as an option. The Impact of AI on Apple's Strategy Apple's approach to AI is centered around integrating AI capabilities into its existing hardware rather than investing heavily in building out AI infrastructure and services. This strategy comes as the company is perceived to be behind in the AI space compared to its peers. The Future Outlook With Tim Cook stepping down and John Ternus taking over, Apple is poised to make significant changes in its AI strategy. The company's ability to generate substantial AI-based revenue suggests that its focus on user-centric AI experiences could pay off in the long run.

The BBC's Deepest Cuts in 15 Years The BBC's news operation is to cut costs by a steeper-than-expected 15%, with staff told to expect heavy redundancies. The division, home to about a quarter of all BBC staff, is being saddled with one of the highest cost-cutting targets as the corporation attempts to cut as many as 2,000 jobs in the biggest downsizing of the public service broadcaster in 15 years. The Impact on BBC News Staff at divisions across the BBC are being informed of the level of cuts, with details to be announced in June, and those affected to be told in September. During a video meeting held with BBC News staff, understood to have been attended by about 300 employees, staff were told to expect significantly deeper cuts than the 10% pan-BBC target. The Financial Implications The corporation spent £324m on news and current affairs in the year to the end of March 2025, with a significant proportion of that accounted for by wages, according to the BBC's latest annual report. Richard Burgess, the director of news and content, said on the video call that the entire news division can expect to have to make cost cuts of “around 15%”, with job cuts a major focus. The Future of BBC News Among employees, especially those involved in broadcasts away from studios, there is speculation there may be a push to introduce mobile journalism kits to reduce the use of relatively expensive satellite vehicles and dedicated crews. The BBC has already implemented cost-saving measures, including reducing travel by 40% and significantly tightening spend on consultants, conferences, events and awards. The Leadership Change The development comes as Matt Brittin, the former top Google executive, takes over as the corporation's new director general from 18 May. His appointment came after the resignation of Tim Davie in November after highly contested claims of bias were made by a former adviser to the corporation.