The author describes a framework of three "pills" for understanding AI beliefs: the AI pill (current AI capabilities are real and useful), the AGI pill (AI will become much more capable), and the ASI pill (AI will surpass humans at nearly all tasks). Most people haven't taken the first pill and dismiss AI's current abilities, while reasonable debate requires at least the AGI pill. Those who reject the ASI pill often commit "intelligence denialism" by underestimating how far advanced intelligence can advance capabilities and impact the world.
The Algorithmic Bridge·2 months ago·
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● 9 sources
OpenAI and Anthropic's AI models solved or made progress on multiple long-standing mathematical conjectures in mid-2026, including the unit-distance problem, the Jacobian conjecture, and others—marking a shift in AI's mathematical capabilities from contest-level to frontier research. The models required approximately $2,000 in compute costs to resolve these problems, with OpenAI's Astra model alone addressing ten open problems. However, mathematicians remain divided: while some see opportunity, others worry about their role becoming redundant, though the author argues human mathematicians remain essential as intermediaries to translate and validate AI discoveries into meaningful knowledge.
Researchers tested frontier AI agents on open-ended AI research tasks by having them answer unpublished research questions, and both agents' papers were rejected by the original authors. The agents had $1,000+ in API credits and six days to work but spent less than 50% of their budget and failed to show creativity, backtracking, or effective judgment when facing setbacks. The findings suggest that recursive self-improvement through AI research remains limited to narrow, verifiable tasks, and open-ended research remains a bottleneck that could slow explosive AI progress.
OpenAI's Astra model solved ten major unsolved problems in mathematics and theoretical computer science, marking a moment when AI superseded human capability in a domain once reserved for individual genius. A prominent skeptic of AI's math research potential, Daniel Litt, conceded a major bet this week, acknowledging that AI will likely produce top-tier mathematical papers within the $100k budget range. The achievement signals the end of an era where individual mathematical heroism was necessary; future mathematics will be increasingly collaborative between humans and AI systems, fundamentally restructuring how the discipline operates and how researchers find meaning in their work.
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