Stanford University economists updated research on AI’s employment effects and found that entry-level workers are losing jobs in more AI-exposed occupations while older workers show little change. Employment for ages 22 to 25 is now 19 percent below peers in less exposed fields, up from a 13 percent gap last year. The reported impact is persisting and expanding for younger workers in those occupations, based on the revised August 2026 analysis of new data.
Researchers at TU Delft developed a system that lets a passenger backseat-drive an autonomous car by turning natural-language requests into parameter changes for a safety-aware motion planner. In simulation on nuPlan across 8 prompts, the system adjusted speed and steering smoothness to match user intent. The work shifts control from direct driving decisions to an LLM-mediated tuning of planner criteria with a confirmation step and safety baselines.
Cheshire Academy in Connecticut adapted to student and teacher use of generative AI by training staff on general techniques, piloting an educator-focused platform, and expanding classroom reflection and labeling rules. The school’s individual MagicSchool plan costs just under $100 per year for unlimited access and complete records. As a result, teachers use AI mainly for preparation and administrative tasks while students follow “traffic light” guidance and activities like a Student AI Council to reflect on appropriate and inappropriate use.
Anthropic’s IPO plans, as reported by The New York Times, could target a stock-market debut that would be among the largest on record. The company’s bankers reportedly say the offering could raise more than $100 billion. If that happens, Anthropic could surpass OpenAI’s expected IPO timing and further validate its safety-focused approach against competitors’ “move fast” strategy.
Sam Altman warned that AI control could consolidate in the hands of a few companies or people, leaving most others without a say in how AI affects society. He said his original timeline estimate was wrong after expecting that GPT-4 (in 2023) would drive disruption much more quickly. He now expects a slower, steadier transition as businesses adapt at a different pace than model releases.
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