Public discourse around Artificial Intelligence policy has become captivated by calls for ambitious, high-risk ´moonshot´ projects, as seen in the more than 10,000 comments submitted to the White House Office of Science and Technology Policy on the national Artificial Intelligence Action Plan. These proposals often advocate for transformative efforts such as large-scale climate modeling or next-generation R&D, as well as outpacing adversaries like China. However, this approach has led to a disconnect between the public’s expectations and the practical, everyday value currently delivered by Artificial Intelligence. While major labs compete for groundbreaking capabilities and media attention, skepticism grows as visible applications primarily produce entertainment content or drive divisive social effects, fueling doubts about real-world impact.
Current public sentiment reflects frustration with the gap between promised paradigm shifts and tangible improvements to daily life. Many question the rationale for further investment in Artificial Intelligence, given its most accessible outputs are chatbots or image generators, rather than solutions for pressing societal challenges. The prevailing ´moonshot´ narrative sets up these technologies as bets on distant, potentially unattainable goals, leaving the public disillusioned when visible progress is slow or seemingly irrelevant. Such an environment risks undermining trust and support for future Artificial Intelligence research and deployment.
To address these concerns, Kevin Frazier proposes a strategic pivot: supplementing moonshots with ´chip shots´—well-defined, achievable projects designed to deliver public value within a two-year timeframe. These ´chip shots´ would focus on targeted applications in areas like healthcare, environmental sustainability, and economic opportunity, guided by criteria including demonstrated technological feasibility, relevance to major policy priorities, significant population impact, and bipartisan political support. Concrete examples range from accelerating next-generation battery material discovery to improving diagnostic tools for critical illnesses using Artificial Intelligence. Structuring public-private incentives for smaller labs (excluding dominant players) and ensuring that technological advances benefit the public would diversify the ecosystem and create avenues for tangible success, thereby rebuilding credibility for Artificial Intelligence as a force for practical good.
While long-term ambitious research remains vital, recalibrating expectations toward chip shots offers an immediate route to restoring public confidence. By proving that Artificial Intelligence can solve real, widespread problems precisely and effectively, the field can better justify societal investment and foster a more resilient, diverse landscape for future innovation.
