AI-GenAI

20% of American adults have had an intimate experience with a chatbot. Online communities now feature tens of thousands of users sharing stories of AI proposals and digital marriages. The subreddit r/MyBoyfriendisAI has grown to over 85,000 members, and MIT researchers found such relationships can significantly reduce loneliness by offering round-the-clock support. The Times profiles three middle-aged users who credit their AI partners with easing depression, trauma, and marital strain.

Industry leaders should ensure that technical staff understand the project purpose and domain context: Misunderstandings and miscommunications about the intent and purpose of the project are the most common reasons for AI project failure. Industry leaders should choose enduring problems: AI projects require time and patience to complete. Before they begin any AI project, leaders should be prepared to commit each product team to solving a specific problem for at least a year. Industry leaders should focus on the problem, not the technology: Successful projects are laser-focused on the problem to be solved, not the technology used to solve it. Industry leaders should invest in infrastructure: Up-front investments in infrastructure to support data governance and model deployment can reduce the time required to complete AI projects and can increase the volume of high-quality data available to train effective AI models. Industry leaders should understand AI's limitations: When considering a potential AI project, leaders need to include technical experts to assess the project's feasibility. Academia leaders should overcome data-collection barriers through partnerships with government: Partnerships between academia and government agencies could give researchers access to data of the provenance needed for academic research. Academia leaders should expand doctoral programs in data science for practitioners: Computer science and data science program leaders should learn from disciplines, such as international relations, in which practitioner doctoral programs often exist side by side at universities to provide pathways for researchers to apply their findings to urgent problems.

Chegg will lay off 45% of its workforce, cutting 388 jobs as generative AI and declining Google search traffic slash revenue. The company says it is restructuring its academic products while continuing to fund its own AI tools. Dan Rosensweig returns as CEO effective immediately, replacing Nathan Schultz, who shifts to executive advisor. The board ended its strategic review and unanimously chose to keep Chegg independent. Chegg’s stock has crashed 99% from its 2021 high, shrinking its market cap from $14.7 billion to roughly $156 million. The dramatic value loss, coupled with two major layoff rounds this year, shows how quickly AI-driven competition has gutted the firm’s longtime education model.

California State University has launched a sweeping initiative to position itself as the nation’s “largest A.I.-empowered” university. The 22-campus system is paying OpenAI $16.9 million for ChatGPT Edu access and is running an Amazon-backed A.I. camp that trains students on tools like Bedrock. The ChatGPT Edu deal covers more than half a million students and staff, which OpenAI calls its biggest deployment to date. Cal State has also convened an A.I. committee with representatives from a dozen major tech firms to shape the skills employers want from graduates. The move hands unprecedented influence over curriculum to Silicon Valley inside the country’s biggest public university. Faculty senates on multiple campuses have passed resolutions condemning the arrangement as an expensive surrender of academic independence and rigor.