2026/07/17 by Neil D Lawrence, Neil D. Lawrence, Jessica K Montgomery
Social Sciences · Medicine · #Ethics and Social Impacts of AI #Artificial Intelligence in Healthcare and Education #Computational and Text Analysis Methods
paper · doi:10.1093/scipol/scag070
Abstract Public dialogues have produced clear demand signals for AI. These dialogues imagine innovations that improve our shared wellbeing and prosperity while developing under democratic control. AI development has largely advanced along another trajectory. We argue that this gap is a structural feature of an innovation system whose incentives are captured by the attention economy. Closing it requires a different driver of the innovation cycle. We propose an attention reinvestment cycle, in which efficiency gains accrue as freed time that can be invested in community innovation, with frontline professionals adapting and sharing tools that meet priority needs. Operating the cycle at scale requires institutional infrastructure—dialogic, absorptive, and distributive capacities—that the existing science-policy system has yet to develop.