Meanwhile its Audacious Project --a funding initiative that gives mature nonprofits the opportunity to pitch "moonshot" plans to a coalition of philanthropists--has raised over $1 billion in each of the last two years, in an epic Robin Hood operation for a handful of large-scale projects on climate, health, education, and criminal justice: The Audacious recipients here this year are taking this brief break from their work preventing 16 million unsafe abortions, helping governments in 20 countries prevent lead poisoning, or intercepting 5 percent of the world's river-borne plastic before it reaches the ocean.

As chaotic negotiations over the end of the Iran war continue, US negotiators think they have the framework for a deal in place. Now they just have to sell the president on it. President Donald Trump's negotiators face the arduous task of trying to convince the president that a deal he previously rejected is their best option in Iran . Last month, Trump initially gave his blessing for a so-called "cash for uranium" deal, under which the US would release around $20 billion in frozen funds in exchange for Iran handing over its stockpile of highly enriched uranium, sources familiar with the matter tell WIRED. Trump's negotiators, vice president JD Vance, special envoy Steve Witkoff, and Jared Kushner, Trump's son-in-law, received repeated approvals from the president while they were in Islamabad, giving them confidence a deal was close.

The Iran war has strengthened Ukraine in surprising ways. Could a ceasefire with Russia be closer? When Ukrainian President Volodymyr Zelensky, serious-faced and clad in black, strolled down a lilac carpet in Saudi Arabia in March, it marked a moment in the US-Israeli war in Iran. In a post on X, he said his visit was to strengthen the protection of lives. Zelensky, who carries the weight of Ukraine's own war with Russia on his shoulders, has been seizing the moment, flying to the Gulf to publicly showcase the international value and marketability of Kyiv's learned-on-the-battlefield military nous in drone warfare. Ukraine says it has now signed deals with Saudi Arabia, the UAE and Qatar - all hit by Iranian missiles and drones in recent weeks - to share drone expertise and technology, tightening alliances and benefitting from business - and it hopes defence deals - with wealthy US-allied countries.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russia has continued heavy attacks on Ukraine for the past 24 hours, with several coming overnight on Thursday and in the early hours of Friday. At least one person has been killed and several have been injured. A Russian drone attack overnight damaged port infrastructure in Ukraine's southern Odesa region and wounded two people in the Black Sea port city of Odesa, regional Governor Oleh Kiper said on Friday morning. Two high-rise residential buildings were damaged in the attack, which destroyed apartments and caused fires, Kiper wrote on the Telegram messaging app. "This night, Russia again massively attacked the civilian infrastructure of the Odesa region: two people were injured," he said.

One group of hackers used AI for everything from vibe coding their malware to creating fake company websites--and stole as much as $12 million in three months. The advent of AI hacking tools has raised fears of a near future in which anyone can use automated tools to dig up exploitable vulnerabilities in any piece of software, like a kind of digital intrusion superpower. Here in the present, however, AI seems to be playing a more mundane, if still concerning, role in hackers' toolkit: It's helping mediocre hackers level up and carry out broad, effective malware campaigns. That includes one group of relatively unskilled North Korean cybercriminals who've been discovered using AI to carry out virtually every part of an operation that hacked thousands of victims to steal their cryptocurrency. On Wednesday, cybersecurity firm Expel revealed what it describes as a North Korean state-sponsored cybercrime operation that installed credential-stealing malware on more than 2,000 computers, specifically targeting the machines of developers working on small cryptocurrency launches, NFT creation, and Web3 projects.

A modern data fabric makes it possible to turn existing enterprise knowledge into a trusted foundation for AI. Artificial intelligence is moving quickly in the enterprise, from experimentation to everyday use. Organizations are deploying copilots, agents, and predictive systems across finance, supply chains, human resources, and customer operations. By the end of 2025, half of companies used AI in at least three business functions, according to a recent survey. But as AI becomes embedded in core workflows, business leaders are discovering that the biggest obstacle is not model performance or computing power but the quality and the context of the data on which those systems rely. AI essentially introduces a new requirement: Systems must not only access data -- they must understand the business context behind it.

The AI Race Is Pressuring Utilities to Squeeze More From Europe's Power Grids As data center developers queue up to connect to power grids across Europe, network operators are experimenting with novel ways of clearing room for them. European countries are racing to bring new data centers online as AI labs across the globe continue to demand more compute. The primary limiting factor is energy--and specifically, the ability to move it. Though Europe is on track to generate enough energy, utilities experts say, grid operators broadly lack the infrastructure needed to transport it to where it needs to go. That's throttling grid capacity and, by extension, the number of new power-hungry data centers that can connect without risking blackouts.

AI transforms industries, but electricity demand surge could double American households' power bills as utilities build new infrastructure to support data centers.

We describe a new software framework for fast training of generalized linear models. Theframework,named Snap Machine Learning (Snap ML), combines recent advances inmachine learning systems andalgorithms inanested manner to reflect the hierarchical architecture of modern computing systems.

This happened as generative and agentic systems became essential in key sectors worldwide. This feature highlights the major AI ethics and policy developments of 2025, and concludes with a forward-looking perspective on the ethical and policy challenges likely to shape 2026.