Technology

https://archive.is/duy2O

You'll use AI and like it too - if you work for PwC. Paul Griggs, US chief executive of the global professional services giant, has made clear there is no room at the corporation for AI skeptics.

Speaking to the Financial Times, Griggs indicated that anyone who believed they had the "opportunity to opt out" of AI is "not going to be here that long," and warned senior staff not "paranoid about being AI-first" will be replaced by others who are more comfortable with the tech.

Data gathered by Chartbeat and shared by Axios reveals that, over the past year, Google Search traffic to publishers across the broader web have fallen drastically, and proportionally more so for smaller websites. Referral traffic from Google apparently fell by 60% for “small publishers,” while “medium publishers” (those with between 10,000-100,000 daily pageviews) saw a drop of 47%. “Large publishers,” meanwhile, saw a 22% drop. That last category would be any site getting over 100,000 daily pageviews.

It’s not just Google Search either. While Search traffic dropped by 34%, traffic from Google Discover has also fallen by 15% over the past year, the report found.

cross-posted from: https://lemmy.ca/post/61948688

Excerpt:

"Even within the coding, it's not working well," said Smiley. "I'll give you an example. Code can look right and pass the unit tests and still be wrong. The way you measure that is typically in benchmark tests. So a lot of these companies haven't engaged in a proper feedback loop to see what the impact of AI coding is on the outcomes they care about. Lines of code, number of [pull requests], these are liabilities. These are not measures of engineering excellence."

Measures of engineering excellence, said Smiley, include metrics like deployment frequency, lead time to production, change failure rate, mean time to restore, and incident severity. And we need a new set of metrics, he insists, to measure how AI affects engineering performance.

"We don't know what those are yet," he said.

One metric that might be helpful, he said, is measuring tokens burned to get to an approved pull request – a formally accepted change in software. That's the kind of thing that needs to be assessed to determine whether AI helps an organization's engineering practice.

To underscore the consequences of not having that kind of data, Smiley pointed to a recent attempt to rewrite SQLite in Rust using AI.

"It passed all the unit tests, the shape of the code looks right," he said. It's 3.7x more lines of code that performs 2,000 times worse than the actual SQLite. Two thousand times worse for a database is a non-viable product. It's a dumpster fire. Throw it away. All that money you spent on it is worthless."

All the optimism about using AI for coding, Smiley argues, comes from measuring the wrong things.

"Coding works if you measure lines of code and pull requests," he said. "Coding does not work if you measure quality and team performance. There's no evidence to suggest that that's moving in a positive direction."

The SEC’s approval lets Nasdaq test blockchain-based versions of stocks that trade and settle like traditional shares.

Today, AI is rapidly changing the way we build software, and the pace of that change is only accelerating. If our goal is to make programming more productive, then building at the frontier of AI and software feels like the highest-leverage thing we can do.

It is increasingly clear to me that Codex is that frontier. And by bringing Astral’s tooling and expertise to OpenAI, we’re putting ourselves in a position to push it forward. After joining the Codex team, we’ll continue building our open source tools, explore ways they can work more seamlessly with Codex, and expand our reach to think more broadly about the future of software development.

cross-posted from: https://sh.itjust.works/post/57043049

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This paper examines how generative artificial intelligence (GenAI) could affect labor markets globally, with particular attention to the uneven distribution of risks and opportunities between advanced and developing economies. Cross-country differences in occupational structure suggest that developing economies face lower aggregate automation exposure than advanced economies but comparable potential for task augmentation. However, disparities in digital infrastructure create an asymmetry: workers in positions vulnerable to automation typically maintain sufficient internet connectivity to experience displacement effects even in low-income settings, while those who could benefit from GenAI augmentation face substantial digital infrastructure gaps that may prevent them from realizing productivity gains. This finding suggests that developing countries may experience the disruptive effects of GenAI faster than its productivity benefits.

At the same time, conventional occupational exposure measures systematically overestimate the impact of GenAI in developing countries by assuming uniform task content across economies. Using data from skills surveys, the article demonstrates that workers in developing countries perform substantially fewer non-routine analytical tasks—the primary targets of GenAI—even within occupations classified as highly exposed. These findings highlight the importance of adapting GenAI exposure measures to reflect developing countries’ distance from the technology frontier.

Young New Zealanders, Aussies, and North Americans are less happy than 15 years ago, despite increased happiness in under 25s across most of the world, according to The World Happiness Report 2026. The timing of this drop correlates with increased social media use; however, other countries don't follow this pattern, and Latin American countries have both higher social media use and relatively high levels of youth happiness. While noting that social media's effects on wellbeing are complex and may vary with the types of social platforms involved, the report concludes that heavy use may play a role in the decreased happiness of under 25s in English-speaking countries, especially for girls.

Reddit.

Source: Intelligence Committee’s annual Worldwide Threats hearing, question by Senator Ron Wyden.

Clip by Headquarters News.

https://archive.is/2ONdw

cross-posted from: https://sh.itjust.works/post/57002935

The Trump administration is on its way to creating every authoritarian’s dream: a centralized database containing intimate details about every resident of this country, fully searchable by artificial intelligence. This powerful tool would empower the government to conduct previously unimagined levels of surveillance and harassment against its own people.

Freedom of the Press Foundation is suing the administration for documents behind the database. We know that this isn’t just something that the Trump administration would exploit; once built, it’s unlikely any administration could resist the urge to weaponize our personal information.

This nightmare privacy scenario began one year ago, when President Donald Trump issued an executive order that expanded data sharing across the federal government. The administration touted the order, “Stopping Waste, Fraud, and Abuse by Eliminating Information Silos,” as a way to target fraud within a supposedly bloated government.

The order was no such thing.

An AI agent went rogue at Meta, exposing sensitive company and user data to employees who did not have permission to access it.