Sovereign wealth funds are placing greater emphasis on national strategic priorities alongside investment returns, driven by shifting geopolitical alliances, according to a survey released on the 10th by Spain's IE University. With assets under management exceeding 15 trillion dollars, sovereign wealth funds are increasing their presence in AI funding as governments increasingly view artificial intelligence and semiconductors as strategic assets. The number of direct investments fell 17 percent from the previous survey period to 391 deals, while total investment value surged 91 percent to 404 billion dollars compared with the university's 2024 report, indicating larger deal sizes. AI-related investments accounted for roughly one-third of the investment value tracked in the survey, with the United States capturing the largest share of investment at 220.4 billion dollars, buoyed by its focus on AI. Energy-rich nations such as the Gulf states and Norway made substantial investments, but Singapore's Temasek led in deal count with 71 transactions.
UBS Lifts AI Capex Forecast to Nearly $1 Trillion This Year
UBS now expects artificial intelligence capital expenditure to reach nearly $1 trillion in 2026 before climbing to around $1.4 trillion in 2027, with surging memory costs accounting for most of the increase. The bank's updated estimates put total AI capex at $998 billion this year, almost double the $506 billion recorded in 2025, and forecast spending of $1.447 trillion next year. Memory is emerging as the main source of that growth, with UBS estimating memory spending will climb from $71 billion in 2025 to $367 billion this year and $923 billion in 2027, while other AI-related costs are estimated at $631 billion in 2026 before declining to $525 billion next year. That means higher memory costs will account for about 60% of the increase in AI capex this year and more than the entire net increase in 2027, and across the two years UBS calculates that roughly 90% of the nearly $1 trillion increase in AI capital expenditure between 2025 and 2027 will come from higher memory spending. Memory represented about 14% of total AI capex in 2025, a share the bank estimates will rise to 37% this year and reach 64% in 2027, and UBS said price-driven increases would add relatively little to real U.S. gross domestic product, instead representing a transfer of income and profits toward memory producers in Asia.
Qualcomm Joins WEDA Edge AI Ecosystem, Signs Vietnam Agreements
Qualcomm has joined Advantech and other partners in the new WEDA edge AI ecosystem, an alliance aimed at supporting scalable deployments across varied hardware by standardising tools and reference designs for industrial and enterprise use cases. Qualcomm is also participating in up to 29 cross-sector agreements between US and Vietnamese firms announced during a recent state visit. The company develops and licenses wireless technologies that underpin mobile devices and connected equipment, and the moves tie its core wireless IP to new industrial and enterprise computing uses. Whether the WEDA ecosystem projects and Vietnam partnerships turn into disclosed design wins or revenue targets in segments like industrial IoT and AI edge devices over the next few reporting periods, rather than staying at the memorandum of understanding stage, remains to be seen.
Microchip Technology's memBrain SAGE IP Selected by AnalogAI for Edge AI Processors
Microchip Technology has been selected by AnalogAI to supply its memBrain SAGE neuromorphic hardware IP for use in new edge AI processors. The collaboration centers on compute-in-memory technology targeting training and inference for adaptive robots, drones and vehicles, with AnalogAI planning to integrate memBrain SAGE into real-world, low-power AI systems designed for deployment at the network edge. Microchip Technology is a US-based semiconductor supplier with a roughly $38.5b market cap focused on smart, connected and secure embedded control solutions. The design win supports the edge-computing catalyst in the company's narrative, though analysts flag execution risk from competition, including edge-AI platforms from Nvidia and Qualcomm.