Scale AI

Classified budget estimates indicate the National Security Agency is paying billions of dollars to test artificial intelligence models, according to a new report. The figures suggest US intelligence agencies are investing heavily in evaluating advanced AI capabilities, prompting debate about the scale of government involvement in the AI sector and its oversight implications.

Google has announced Project Suncatcher, an initiative to place machine learning infrastructure in space, moving AI compute beyond Earth-based data centers. The company published details on its research blog, and the announcement is drawing attention in technology circles as a bold step toward scaling AI hardware with solar power and orbital deployment.

Meta is launching a new push to win business customers for its artificial intelligence products, as the company works to turn its heavy AI investments into revenue. The Wall Street Journal reports the effort is aimed at convincing companies to adopt Meta's AI tools, marking a shift toward monetising the technology after years of large-scale spending on data centres, models and infrastructure.

Anthropic has agreed to a $12 billion deal with Akamai for AI computing capacity, according to a Bloomberg report. The agreement gives the AI company access to large-scale infrastructure to train and run its models, marking a significant expansion of its computing arrangements. The size of the deal and the choice of Akamai as a provider are drawing attention as competition for compute resources intensifies among leading AI developers.

The Wall Street Journal reports on a startup developing AI tools designed to counter future large-scale AI-driven threats, described as a potential 'AI pandemic'. The company aims to build defenses against misuse of artificial intelligence, such as automated disinformation or cyberattacks. Details of the company's name, funding and technology were not provided beyond the report itself.

Major technology companies are increasingly using guarantees to back debt for AI data centres and chips, issuing up to $300bn in commitments in under a year while recording little of that exposure on their balance sheets. The scale of off-balance-sheet support is drawing scrutiny, with analysts questioning how much financial risk the firms are carrying beyond what their accounts show.

Google is planning to launch an experimental satellite into orbit, marking the first move by a Big Tech company toward building AI data centers in space. The report, from the New York Times, signals the company's interest in shifting computing infrastructure beyond Earth, a concept floated to address the enormous power and cooling demands of AI.

Bill Gates said creating a global framework for artificial intelligence would be more difficult than negotiating the nuclear arms limitations of the Cold War era. He argued that AI's rapid spread among companies and governments, and the difficulty of verifying its development, make international agreement far harder than the nuclear treaties that shaped the 20th century. His remarks add to a growing debate over how the world should govern increasingly powerful AI systems.

Land prices for data centers in the United States have risen almost tenfold over two years, driven by the AI building boom. In the first half of 2026 companies spent roughly $6 billion on land for future data centers, 79 percent more than a year earlier, with land now taking a growing share of overall development budgets.

Elon Musk said xAI plans to expand its computing capacity to more than 1.2 million Nvidia GPUs by the end of the year, more than double its current count. The plan underscores the escalating race among AI companies to secure massive GPU fleets for training larger models, and highlights Nvidia's central role in supplying the global AI buildout.

A new publication examines the economics of open-weight inference, looking at what it actually costs to run openly available large language models compared with closed, API-based alternatives. The discussion centers on how hardware, hosting and efficiency trade-offs shape whether open-weight models are genuinely cheaper for organizations running AI at scale.

A debate is under way among developers and AI watchers over whether the growing demand for AI compute will be met on personal devices or in data centers. One iOS developer says they push as much processing on-device as possible for privacy and cost reasons, noting Apple is betting on the same approach. Others point out that frontier models keep growing larger, favoring cloud infrastructure.

A researcher says OpenAI's automated agents scanned a United Nations statistics website roughly 16,500 times, raising fresh questions about the load and consent issues created by AI web crawlers. The claim highlights growing friction between AI companies collecting web data at scale and the sites whose infrastructure absorbs the traffic.

Bridgewater argues that the surge in capital spending on artificial intelligence can only be justified if adoption scales broadly across the economy. The investment firm's view feeds into a growing debate about whether massive outlays by tech companies on data centres and chips will deliver returns, or whether current spending levels rest on unproven demand for AI products and services.

The power industry is turning its attention to a new scale of electricity demand: not just data centers, but so-called AI factories that consume far more power and connect differently to the grid. Utilities are being urged to plan ahead for this next wave of load growth, with questions raised about infrastructure investment, grid capacity and how to serve these massive facilities.

Acurast says it is scaling an open-source AI system by running the Laya large language model across its decentralized network of smartphones. The approach uses spare capacity on consumer devices instead of centralized data centers, positioning Acurast as a lower-cost, censorship-resistant alternative for AI compute. Discussion is centering on whether phone-based decentralized networks can realistically handle serious AI workloads at scale.

Financial technology firm Finastra has announced the launch of an AI-powered supply chain finance solution aimed at helping banks grow their working capital offerings. The product is designed to let lenders automate and scale supply chain finance services for corporate clients. News of the launch is circulating across financial technology trade coverage, with attention focused on how banks may adopt artificial intelligence to expand working capital business.

Google plans to test its AI chips aboard satellites in orbit under a project called Suncatcher, according to a report in The Daily Star. The initiative suggests the company is exploring whether space-based hardware could help run artificial intelligence workloads. Details on timing, partners and the scale of the orbital tests have not yet been made clear.

Engineering teams using AI coding agents are finding that continuous integration pipelines have become their biggest constraint, according to a report circulating among developers. As agents generate far more code and commits than human programmers, test suites and CI infrastructure struggle to keep up, forcing teams to rethink how they validate machine-written code at scale.

The Financial Times reports that AI hyperscalers — the largest cloud and AI infrastructure providers such as Microsoft, Amazon, Google and Meta — are transforming debt markets. Their enormous spending on data centres and computing power is driving new borrowing at historic scale, changing how credit is priced and who dominates corporate bond issuance.

Commentary is warning that AI agents — autonomous software that can perform work tasks — are poised to enter workplaces at scale, and that companies are unprepared. The argument: your next coworker may be an AI agent, and managing human-AI collaboration will require entirely new models of workplace interaction, training and management, rather than simply adding a new software tool.

A new analysis argues that Europe's U-space framework for managing drone traffic does not yet account for artificial intelligence, creating an unresolved regulatory gap. As AI increasingly flies and coordinates unmanned aircraft, EU rules built for conventional operations may no longer fit. Commentators say regulators must adapt U-space rules before AI-driven drones scale up across European airspace.

A startup founder has shut down his artificial intelligence company even though it was showing early signs of success. In an interview with Tech in Asia, he explained the reasoning behind the decision, offering a rare candid account of why a promising AI venture was deliberately wound down rather than scaled.