I and @sreeramkannan had an wonderful opportunity to sit down with @stuartbuck1, Executive Director at during Manifest to talk on what has been long-term structural issues with federal funding for science and what are the solutions to fix it. A lot of science that is done outside industrial apparatus (such as in academia) is supposed to geared towards pursuing open-ended science and not just mere hill-climbing. The core issue that we delved into is the paradox that most federal funding that had been allocated via peer review from an expert of panels has the tendency to fund only those proposals that are "safe" and "not risky/speculative." As Stuart points out, this funding style is probably fine with most science but if you look into the history of true scientific breakthroughs, this mechanism wouldn't have approved funding for pursuing that breakthrough by that council of peers. The example that Stuart cited is the chances of funding Einstein when he was patent-office clerk in 1902 to go and explore his out-of-box ideas. Stuart mentioned about alternative mechanisms that are being experimented with programs such as We also touched upon two of the most hotly-debated issues of our time: (1) given that there is increasingly high signal that AI is going to accelerate the iteration time for doing research and help humanity do great science, what should be the role of domain experts? (2) most of AI research and its applications to basic science are hyper-concentrated within frontier labs, what happens to those who are and want to pursue science outside the borders of those labs? Check out the full conversation. 03:18 Einstein in 1902 04:01 the National Institute for Irrelevant Ideas 04:20 Karikó and mRNA 14:37 what everyone knows about NIH grants 16:33 funding the polarizing proposals 17:34 where AI money goes next 24:18 the stack of papers 33:22 finding meaning after AGI