Perplexity and Nvidia have launched "Portable Computer," a local-first version of Perplexity's agent platform that runs AI models, files, tools, and workflows directly on Nvidia-powered Linux hardware. Local tasks incur no token charges and keep data on-device by default, with users asked for permission before the system escalates a step to a cloud model. VentureBeat reports: For Nvidia, which has spent the past two years selling the world on trillion-dollar AI data centers, the announcement signals something subtler but strategically important: the chipmaker believes local AI has crossed a threshold from hobbyist curiosity to practical tool -- and it wants to sell the hardware that runs it. "Local AI reached an inflection point," said Nader, Nvidia's director of developer technology, who focuses on developer tooling and open source. "For the longest time, it was hobbyists and enthusiasts, and they were running these quantized models that were quantized down to be super tiny... And while that's cool, it's not super practical. But all that changed with a lot of these new open source models that have come out that are super useful." [...] Portable Computer arrives today for Pro, Max, Enterprise Pro, and Enterprise Max subscribers on Linux, with Windows support following in September. Any RTX GPU with at least 24GB of VRAM -- roughly a GeForce RTX 3090 or newer -- clears the bar, a threshold Nate called "sort of the floor where we really want to make sure that we can deliver a great experience, but balance that with making it broadly available."

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An anonymous reader quotes a report from Ars Technica: For years, AI industry watchers of all stripes have been warning of a coming jobs apocalypse driven by ultra-intelligent AI systems that will be able to replicate most human tasks more cheaply. Now, newly updated research from Stanford University economists suggests AI seems to be causing significant entry-level job losses for younger workers in some fields, even as older workers appear largely unaffected so far. The August 2026 edition of "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence" updates and revises a paper of the same name published last year with fresh data and refined statistics. In that update, the Stanford researchers find the employment trends they identified for entry-level workers last year are persisting and expanding. Specifically, employment levels for workers ages 22 to 25 in the most "AI-exposed" occupations are now 19 percent below those of their peers in fields less exposed to AI disruption. Last year, that gap measured just 13 percent. [...] Digging deeper into the data, the researchers found that this phenomenon is mainly manifesting itself through lower hiring rates for entry-level workers in AI-impacted fields, rather than increased firings or employees quitting. They also found that the labor market effects among this age group were mostly seen in lower overall employment, rather than reduced pay rates. But not all jobs that show potential for AI "disruption" are created equal, the researchers found. In its Economic Index, Anthropic differentiates between queries related to tasks that are "automative" (i.e., fully replacing work previously done by a human) or "augmentative" (i.e., helping human workers be more effective at tasks they are still needed for). By this measure, jobs like "accountants and auditors" and "receptionists and information clerks" were among those judged most susceptible to AI automation, while jobs like "chief executive" and "registered nurse" were among those using AI augmentation most often. Unsurprisingly, jobs where AI automation is prevalent are the ones showing the worst relative employment levels for entry-level workers these days. "The findings are consistent with automation-oriented uses of AI substituting for labor while complementary uses are associated with flat or rising employment," the researchers write. Interestingly, the researchers found that the impact is strongest in entry-level jobs built around "codified" knowledge, which is the formal, documented skills that AI can more easily replicate. Meanwhile, experienced workers in roles relying on tacit, practice-based knowledge have seen stronger employment growth.

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