According to @JeffDean, the Laude Institute has launched a new initiative to identify and fund non-profit computer science research with the goal of creating significant global impact. Board members include prominent AI figures such as @andykonwinski, @jpineau1, and Dave Patterson. This collaborative effort aims to support foundational AI research that can drive innovations in areas like machine learning, responsible AI, and open-source development, offering new business opportunities for technology transfer and public-private partnerships. (Source: @JeffDean, Twitter, June 23, 2025)

According to Yann LeCun on Twitter, the 2026 budget request for the National Science Foundation (NSF) by the Trump administration proposes a 55% reduction compared to 2024 levels (source: @ylecun, June 1, 2025). This significant decrease directly threatens federal funding for AI research, development, and foundational science in the United States. The budget cut could slow progress in core AI research areas, reduce opportunities for academic-industry collaboration, and weaken the U.S. position in the global AI race, potentially leading to a shift in AI talent and investment to regions with more robust government support. Businesses in the AI sector may need to seek alternative funding sources or adapt strategies to mitigate the impact of decreased federal investment.

According to Yann LeCun, leading AI expert and Chief AI Scientist at Meta, countries aiming to jump-start a vibrant tech industry should focus on creating appealing environments for AI scientists by offering strong research funding, open immigration policies, and supportive innovation ecosystems (source: Yann LeCun, Twitter, June 1, 2025; LinkedIn). LeCun highlights that simplified visa processes, generous grants for foundational AI research, and proactive public-private partnerships are proven strategies for attracting top global talent. Nations that implement these measures can accelerate the development of AI startups, foster academic-industry collaboration, and quickly scale up AI-driven economic growth and competitiveness (source: public Google Doc shared by LeCun). This approach is already producing results in countries like Canada and Singapore, where supportive policies have led to the rapid establishment of AI research hubs and thriving innovation clusters.

According to DeepLearning.AI, Andrew Ng cautioned that reducing research funding could threaten national competitiveness and security, emphasizing the strategic value of sustained AI investment (source: DeepLearning.AI, May 29, 2025). The newsletter also highlighted that Anthropic's new Claude 4 models have surpassed previous leaders on key coding benchmarks, signaling rapid advancements in AI model capabilities (source: DeepLearning.AI). Additionally, Google I/O introduced a suite of new AI-powered features and products, underlining Google's ongoing commitment to integrating AI across its ecosystem (source: DeepLearning.AI). DeepSeek revealed its approach to training top-performing large language models (LLMs) efficiently on a budget, demonstrating practical business opportunities for startups and enterprises seeking cost-effective AI deployment (source: DeepLearning.AI).

According to Andrew Ng, proposed reductions in U.S. funding for basic research could significantly undermine the nation's leadership in AI and related technology sectors. Ng emphasizes that while open research benefits the global community, the primary advantage goes to the country where the research originates, driving domestic innovation and business growth. The cuts may slow progress in foundational AI models, hinder collaboration between academia and industry, and reduce opportunities for U.S.-based startups and enterprises to leverage cutting-edge developments. This policy shift could lead to a competitive disadvantage for the U.S. in the global AI market, impacting both economic growth and technological influence (source: Andrew Ng, Twitter, May 29, 2025).

According to Yann LeCun, as cited in The New York Times, the Trump administration systematically reduced support for scientific research, which directly impacted the development of artificial intelligence in the United States. The administration's rollbacks on research funding, reduction of federal science advisory roles, and restrictions on data transparency created barriers for AI innovation and limited the scope for public-private partnerships essential for AI commercialization. This shift in policy increased competitive pressure from global AI leaders and narrowed opportunities for American businesses seeking to leverage government-backed AI research for commercial applications (source: The New York Times, May 24, 2025; Twitter/@ylecun).