The text discusses the impact of LLMs (Language Model Models) and the concept of information post-scarcity. It highlights how AI has reduced the marginal cost of producing content to zero, allowing for the remixing, summarization, and transformation of content across different formats. Attention scarcity is emphasized as a consequence of the abundance of information available on the internet. The need for information managers and aggregation is discussed, with the role of influencers and aggregators like Google in handling the abundance of content. The text also touches on the scarcity of trust in a world where bots can mimic human voices and the importance of cryptographic signatures for verifying content. Finally, it explores the potential commoditization of LLMs and the advantages of open source models in promoting permissionless innovation.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| LLMs and information post-scarcity | Shift from scarcity to abundance of content | Increased availability and manipulation of content | Reduction in marginal cost of producing content |
| New abundance creates new scarcities | Scarcity shifts to attention and trust | Increased competition for attention and need for trust verification | Overabundance of information sources |
| Trust becomes scarce | Need for cryptographic signing and individual-level security | User-owned keys and reimagined security models | Rise of AI-generated content and identity theft |
| Data lock-in is not a moat | Shift from data lock-in to content aggregation | Faster emergence and collapse of aggregators | Superabundance of content and AI-generated ecosystems |
| LLMs are a moat, but for how long? | Potential commoditization of LLMs | Pressure to reduce costs and increase accessibility | Strategic interest in commoditizing complements |
| Noosphere fixes this? | Tools for thought in the age of LLMs | Integration of AI agents and shared knowledge graphs | Alignment with future technological trends |