Meta Is in Crisis, Google Search's Makeover, and AI Gets Booed by Graduates This week on, the team discusses Meta's recent layoffs and what they've been hearing from employees about the increasingly grim vibes at the company. They also talk about Elon Musk losing his lawsuit against OpenAI and share highlights from Google's annual conference--including an ambitious AI vision to change how people search the web. Finally, what do recent college graduates and women whose spouses work in AI have in common? Google Search Goes Agentic--and Doesn't Need You Anymore Write to us at [email protected] . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . We spoke to more than a dozen employees and it turns out the job cuts are far from the only reason why Meta employees are really going through it. He lost his lawsuit against Sam Altman and OpenAI in really as full a way as you can, as dramatically as possible. I know, Zoë, you're looking forward to talking about that. We're going to get into why young adults might be using AI, but they have very complicated feelings about it. And later in the show, we're going to hear about why women married to AI bros have had enough . This week, the company is letting go of roughly 10 percent of its workforce, which is about 8,000 employees total. It's the latest round of job cuts, adding to the roughly 25,000 jobs that have been cut in the past few years as part of Mark Zuckerberg's Year of Efficiency that started in 2023 and now the latest AI-forward workplace, which he is trying to develop and impose. And while these latest cuts are not as big as some of the rounds of layoffs that have already happened, they're getting a ton of attention because Mark Zuckerberg, the CEO, has said that the reason they're happening, in part at least, in large part, is because the company is spending so much money on AI and data centers.

If you take a sheet of paper and add some dots, how many pairs can be the same distance apart? If you take a sheet of paper and add some dots, how many pairs can be the same distance apart? OpenAI has claimed a further advance in AI reasoning after its technology successfully tackled an 80-year-old maths problem. The company behind ChatGPT said it had made a breakthrough with a challenge first posed by Hungarian mathematician Paul Erdős in 1946: the planar unit distance problem. The question posed by Erdős is simple to explain.

Transfer learning is an essential technique for many machine learning/AI models of complex structures such as large language models and generative AI. The essence of transfer learning is to leverage knowledge from resolved source tasks for a new target task, especially when the sample size $m$ of the training data for the latter is low. In this work, we rigorously analyze the potential benefit of transfer learning in terms of sample efficiency. Specifically, taking an optimal transport viewpoint of transfer learning, we find that when the data dimension $d$ is higher than $3$, the sample complexity for transfer learning is $O(m^{-(α+1)/d})$, with $α$ indicating the smoothness of the data distribution, as opposed to the $O(m^{-p/d})$ sample complexity for direct learning with $p$ indicating the smoothness of the optimal target model. Our finding theoretically supports a better sample efficiency for transfer learning, when the target task is optimizing over a family of not-so-smooth models (i.e., highly complex networks with the possible use of non-smooth activation functions). Using image classification as an example, we numerically demonstrate the sample efficiency for transfer learning, that is, in the data hungry regime, the model performance can be significantly improved by transfer learning.

A long and bitter legal battle between tech billionaires Elon Musk and Sam Altman has culminated in victory for the OpenAI boss. Musk has vowed to appeal the verdict. But what did the trial reveal about big tech and the global AI race?

The ex-employees, who cofounded a new AI watchdog group, say investors deserve more information about xAI's safety practices before SpaceX goes public. Two former OpenAI employees and a group of AI safety nonprofits are warning that Elon Musk's AI lab, xAI, could become a liability for prospective investors in SpaceX, which is preparing to file what's expected to be the largest initial public offering in Wall Street History. In a letter directed to investors published on Tuesday, the ex-staffers highlighted what they describe as "unpriced risks" related to xAI that could complicate SpaceX's reported plans to raise up to $75 billion as part of its IPO. The rocket company's private valuation shot up to over $1 trillion after it acquired xAI last year . Musk claimed his rocket company could launch data centers into space for his AI lab, but the letter's authors argue that xAI's poor record on safety issues could complicate how investors view the combined company as it gets ready to submit its IPO prospectus filing .

It is not only OpenAI but the AI race itself that was vindicated in the California courtroom last night . Even though Elon Musk essentially lost on a technicality, there's a clear signal from the verdict that making lots of money from AI and competing fiercely with rivals is simply business. The industry sometimes tries to display a united front, especially when it comes to safety, research and inclusivity. But this case served as a powerful reminder that none of the AI giants are charities and don't have to be, even if they once said otherwise. Cracks in the façade of industry collaboration for the sake of humanity have been exposed before.

On Monday morning, a jury in Oakland, California, announced its verdict in one of the most-watched tech feuds between billionaire Elon Musk and OpenAI CEO Sam Altman. The nine-member jury handed a decisive victory to Altman, saying Musk had waited too long to bring his claims against the artificial intelligence company and its top executives. Musk, who cofounded OpenAI as a nonprofit, had filed a $150bn lawsuit against the organisation, Altman and its president, Greg Brockman, accusing them of turning it into a for-profit entity for personal enrichment. Instead, the case became focused on a procedural issue. After deliberating for less than two hours, the jury unanimously found that the statute of limitations had expired before Musk filed the lawsuit in 2024, meaning jurors concluded he had waited too long to bring his claims under the applicable legal deadline.

Elon Musk loses case against Sam Altman over OpenAI's overhaul Elon Musk arrives at the Ronald V. Dellums Federal Building for court in Oakland, California on April 30. A jury rejected Elon Musk's claims that OpenAI under Sam Altman's leadership betrayed its mission to benefit the public by morphing into a for-profit business, finding that he waited too long to sue the company. The verdict reached Monday in federal court in Oakland, California, follows a trial over the bitter feud between the entrepreneurs who worked together to launch the startup in 2015. OpenAI has since evolved into one of the world's most valuable and powerful artificial intelligence companies. "I think there is a substantial amount of evidence to support the jury's findings," U.S. District Judge Yvonne Gonzalez Rogers said when she accepted the nine-member jury's unanimous conclusion after about two hours of deliberations.

Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness and the perpetuation of demographic disparities that exist in the world. In this context, causal inference provides a principled framework for reasoning about fairness, as it links observed disparities to underlying mechanisms and aligns naturally with human intuition and legal notions of discrimination. Prior work on causal fairness primarily focuses on the standard machine learning setting, where a decision-maker constructs a single predictive mechanism $f_{\widehat Y}$ for an outcome variable $Y$, while inheriting the causal mechanisms of all other covariates from the real world. The generative AI setting, however, is markedly more complex: generative models can sample from arbitrary conditionals over any set of variables, implicitly constructing their own beliefs about all causal mechanisms rather than learning a single predictive function. This fundamental difference requires new developments in causal fairness methodology. We formalize the problem of causal fairness in generative AI and unify it with the standard ML setting under a common theoretical framework. We then derive new causal decomposition results that enable granular quantification of fairness impacts along both (a) different causal pathways and (b) the replacement of real-world mechanisms by the generative model's mechanisms. We establish identification conditions and introduce efficient estimators for causal quantities of interest, and demonstrate the value of our methodology by analyzing race and gender bias in large language models across different datasets.

Elon Musk, left, and Sam Altman. Elon Musk, left, and Sam Altman. How Sam Altman's victory over Elon Musk clears way for OpenAI's trillion-dollar ambitions OpenAI's plans now seem all but guaranteed, given that the world's richest man couldn't put a stop to them On Monday morning, a jury in Oakland, California, handed a resounding victory to Sam Altman and OpenAI in their long, bitter courtroom battle with Elon Musk. The federal jury found Altman, OpenAI and its president, Greg Brockman, not liable for Elon Musk's claims that they unjustly enriched themselves and broke a founding contract made with Musk when founding the startup. The unanimous verdict, delivered after less than two hours of deliberation, is a stark rebuke of Musk and his lawyer's claims that Altman "stole a charity" through his leadership of OpenAI.