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A new startup called Hone, founded by former employees from Cognition and Ramp, just raised $60 million to build AI agents that can handle extended business tasks over weeks or months without human intervention. The company is valued at $285 million after the funding round led by Benchmark and Index Ventures.
- Hone is developing AI agents designed to function as autonomous professional staff, capable of managing complex, long-duration tasks
- The company raised $60 million in seed funding at a $285 million valuation, backed by top-tier VCs Benchmark and Index Ventures
- The founding team comes from Cognition (an AI agent company) and Ramp (a fintech platform), bringing expertise in both AI systems and business automation
This paper argues that AI's data-based prediction fundamentally differs from how humans think—humans use theory-based causal reasoning to generate genuinely new ideas, while AI works backward from existing data. The authors contend that human cognition won't be replaced by AI because humans can imagine counterfactuals and design experiments to create novel knowledge.
- AI relies on probability and pattern-matching from historical data, making it inherently backward-looking and imitative, whereas human thinking is forward-looking and capable of generating novelty that didn't exist before
- The computer-as-mind analogy (treating brains as input-output processors) has misled cognitive science for decades; humans actually reason through causal theories about how the world works
- Humans can intervene in reality through directed experimentation based on theory, creating new data and new possibilities—something AI cannot do without human guidance
Researchers trained language models on text from before 1930 to create AI that genuinely doesn't know what happened after that year, then had people interact with these "historical minds" to test whether it changes how they view the past. A preregistered experiment with 240 participants found that talking to a pre-1930 AI reduced people's tendency to think the past was more moral than the present.
- The core innovation is using temporal knowledge cutoffs as an experimental variable—training LLMs on historical corpora so they can authentically respond without knowledge of subsequent events, making the past interactable in ways archives and living testimony cannot.
- A randomized controlled trial showed interaction with a pre-1930 model significantly reduced the "illusion of moral decline," a cognitive bias where people perceive historical periods as more ethical than the present.
- This opens a new methodology called "science fiction science"—turning speculative thought experiments into testable empirical studies by using AI as a tool to reconstruct historical perspectives.
Researchers built AI models trained only on pre-1930 text to let people interact with "historical minds," then tested whether this changed how participants viewed the past. Talking to a 1930s-bounded AI reduced the "moral decline illusion"—the common belief that people were more ethical back then.
- A randomized experiment with 240 participants showed that interacting with historically-bounded LLMs reduced the illusion of moral decline compared to using contemporary AI
- The study demonstrates a new experimental method where temporal knowledge boundaries become controllable variables, turning philosophical thought experiments into testable science
- This approach reveals how our understanding of the past gets distorted by everything that happened after it—we can't see history clearly without the filter of hindsight