Eric Drexler at AI Prospects:
The gates we push on now are the ones that will open first. Choose them well.
Most AI research pursues incremental advances — efficiency gains, domain extensions, specific capabilities. Groups seeking transformation typically bet on conceptual breakthroughs or brute scaling. Few tackle the implementation-heavy path: integrating many components into powerful system-level capabilities.1
But implementat…
Eric Drexler at AI Prospects:
The gates we push on now are the ones that will open first. Choose them well.
Most AI research pursues incremental advances — efficiency gains, domain extensions, specific capabilities. Groups seeking transformation typically bet on conceptual breakthroughs or brute scaling. Few tackle the implementation-heavy path: integrating many components into powerful system-level capabilities.1
But implementation barriers are flattening. As I explored in “The Reality of Recursive Improvement,” AI increasingly automates its own advancement. When complex integration — heterogeneous agency architectures, malleable latent-space knowledge stores, orchestrated AI services — shifts from years of human effort to months or weeks of heavily automated exploration, the strategic landscape shifts. The question becomes not what we can build, but what we should build first: systems that can yield broad benefits — scientific tools, medical advances, structured transparency, discovery of win-win options — not those that (further) compromise biosecurity, societal epistemics, or strategic stability.
Leading AI researchers expect transformative R&D automation soon. They’re working to make it happen, and the recursive dynamics suggest they’ll succeed. The implications for research planning are profound.
More here.
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