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Policy Regula
2026-09-30 21:00:57

Jensen Huang and Elon Musk frame “superintelligence” around power, computing and safety

ABMedia reported that U.S. President Donald Trump, through an executive order, set the policy direction for artificial intelligence around “Superintelligence,” or SI, and argued that the word “Artificial” should be removed as the industry moves beyond AI. At an America.gov event, Nvidia CEO Jensen Huang and Tesla founder Elon Musk discussed how that idea could translate into industrial practice, focusing on electricity supply, orbital computing, data center economics and controls for autonomous agents. According to the report, Musk linked power consumption directly to economic output, saying that every 1% increase in electricity use could create an opportunity for GDP to rise by 1% as well. He said the sector is looking at grid upgrades and market-driven energy development, including fusion, fission and advanced battery storage, to meet rising generative computing demand. He also described “Orbital Computing” as a way to send computing hardware into orbit with heavy-lift rockets and tap solar power in space. Huang, for his part, described the shift from traditional data centers to “superintelligence factories” that generate ongoing economic value. On safety, the report said Nvidia is using Open Shell sandboxing to isolate agents and restrict access, while Bluefield chips provide external real-time monitoring. ABMedia also said the industry has signed a joint statement with the White House covering internal controls and third-party audits.

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Jensen Huang and Elon Musk frame “superintelligence” around power, computing and safety
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Goldman Sachs
2026-09-30 07:39:29

Goldman Sachs says AI job drag has briefly reversed as data center construction offsets losses

Goldman Sachs’ latest AI adoption tracker points to a sharp change in the near-term employment picture in the United States. In a Sept. 1 report, economists Sarah Dong and Joseph Briggs said industries exposed to AI added roughly 4,000 jobs per month on average over the past three months, a result the bank described as a temporary reversal in the labor-market drag tied to AI. That contrasts with a separate estimate published in April by Goldman economist Elsie Peng, who calculated that AI was reducing U.S. employment by about 16,000 jobs a month on a net basis. In that earlier framework, AI substitution eliminated about 25,000 jobs per month over the prior year, while productivity gains and complementary hiring added back about 9,000, leaving a net loss. Goldman said the two figures should not be read as a single continuous trend because they come from different methods. The April number was based on a regression model, while the June and September updates used Bureau of Labor Statistics data to track actual employment changes in AI-exposed industries such as management consulting, graphic design, call centers and software publishing. The bank also said job losses in marketing, design, customer service and some tech roles are still visible, but have been offset by hiring tied to data center construction, where employment has risen by more than 200,000 since 2022.

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Goldman Sachs says AI job drag has briefly reversed as data center construction offsets losses
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