nerd

23 Aug 2026

Slashdot

Stanford Economist Now Believes an AI Job Apocalypse Is Unlikely

"There is still no sign of economy-wide job destruction," says economist Erik Brynjolfsson. Working with researchers at the Stanford Digital Economy Lab, Brynjolfsson has determined an AI "job apocalypse" is unlikely, reports the Washington Post, "even though it will likely impact entry-level jobs." And Brynjolfsson "predicts that demand for experienced workers such as senior coders will still be high even if AI takes on more routine knowledge and coding work in the future." Earlier this month, Brynjolfsson and his colleagues at the Stanford Digital Economy Lab updated their widely publicized report, "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence," to reflect that they do not see widespread, economy-wide job displacement associated with AI... Brynjolfsson: What has moved on the upside is productivity. Nonfarm business productivity growth is running over 2 percent, the best sustained stretch since the late-1990s boom... The gains show up years after the investment, and they're starting to show up... By 2030, we will have enormously more capable AI, meaningfully faster productivity growth and an unemployment rate that looks unremarkable - somewhere in its historical range. That surprises people, but it's what the mechanism implies... [T]he technology is becoming much more powerful, and it's going to be even more powerful. Secondly, there are huge implications for business and the economy, including some increases in living standards, but also, potentially, job disruption. And then, thirdly, we must act now to address this gap. We can't just sit back and wait for the tsunami to hit us. And one of the things we can do is put in place institutions, policies and research to make the technology more complementary - using AI to complement people so it creates more jobs. A common misconception among business managers is AI, in order to be effective, has to reduce jobs. That's just not true. You can use AI to increase employment and productivity at the same time. It's kind of a win-win... Right now, tax policy is very skewed toward favoring capital versus labor - and, as a consequence, a lot of entrepreneurs and managers, they're basically being guided by the federal government to replace workers with machines. And I don't think that's necessarily what we'd like to have happen. From an economist's perspective, what you want to be doing is not mimicking and replacing things. You want to be extending and complementing - have humans do new things they never could have done before. He acknowledges "real, persistent and widening" effects on entry-level jobs, with a labor market "closing the on-ramp for people starting their careers... If companies don't hire people at the base of the pyramid, then they're not going to have those people later when they need them." One he points out that one company is instead using AI to speed up its training of young employees.

Hacker News

GLM-5.3 (open-weight) beat Anthropic/OpenAI models – for 1/5 the cost

Comments

23 Aug 2026 4:24pm GMT

Being nude in public can make us feel better about our bodies

Comments

23 Aug 2026 4:21pm GMT

Why Sal Khan't: On Learning by Making but Teaching by Telling

Comments

23 Aug 2026 3:59pm GMT

Slashdot

23 Years After SimCity 4's Release, 'Eternal Commuter' Bug Finally Fixed

"It began life just a year after SimCity 4's 2003 launch," writes the blog PCGamesN. "Now, over 20 years later, it's still going strong with the arrival of Network Addon Mod update 50." The SimCity 4 Network Addon Mod, or simply 'NAM' among the community, has long been a de facto recommendation to anyone looking to pick up the classic city builder. It's a comprehensive overhaul to the game's transportation and infrastructure networks that combines key fixes with a vast set of additional build pieces such as overpasses, intersections, on-ramps, roundabouts, and so on.... [Y]ou'll be able to place down elements adjacent to one another that might previously have needed a one-tile gap between them. Streets can be dragged diagonally, slope tolerances have been improved, and you can build tunnels with the street network. The new version also includes a fix for the 'Eternal Commuter Loop' bug, which might sound fairly innocuous if you're not deep in the weeds. In actuality, it's a 23-year-long frustration that has plagued modders ever since launch. Essentially, it's a problem with the regional pathfinding across city boundaries in certain layouts, causing your Sims to bounce from location to location in search of work without actually taking up a job in any of them. Your zone demand is ruined, traffic builds to unsustainable levels, and the economy falls to pieces before your very eyes. Until now, the only real solution was to simply avoid building layouts that gave your commuters the chance to loop between locations. Now, by blocking specific neighbor-to-neighbor routes while allowing the rest to run as normal, the problem has been resolved... Equally impressively, the mandatory implementation of the DLL has reduced the size of the NAM codebase "by almost 90% and by more than ten million lines, making it easier to maintain and reducing the chance of bugs." Thanks to long-time Slashdot reader Striek for sharing the news.

Canonical is Funding a PhD to Automate C to Rust Translation

An anonymous reader shared this report from the blog It's FOSS: Canonical has committed funding to a three-year PhD project focused on building a system that can automatically translate large C codebases into Rust. And they are not alone; UK Research and Innovation is matching their funding for the project, which is set to run through the University of Bristol's Programming Languages Research Group. The PhD is aiming to build an all-encompassing platform that can take a C repository running into hundreds of thousands of lines and translate it into Rust that's "safe, behaviourally correct and maintainable Rust." The work will be carried out by a student, Alex Wood, who will be supported by Professor Meng Wang leading the work, with Dr. Cristina David and Canonical's Jon Seager serving as co-supervisors.