I made this market because I’ve been wondering if LLM’s aren’t necessarily the best model of intelligence or something else might come along that’s revolutionarily better.

This market resolves to YES if, at any point before January 1, 2030, a new paradigm or architecture of artificial intelligence is widely recognized as having surpassed Large Language Models (LLMs) based on standard Transformer architectures.

Some criteria that would be looked at are:

1. Benchmark Leadership

2. Dominant SOTA Paradigm Shift: A leading frontier AI research lab officially transitions its flagship general-intelligence foundation model away from the Transformer architecture

3. Industry Consensus: A major, reputable industry tracking report explicitly states in its annual analysis that a non-Transformer paradigm has surpassed standard LLMs in general capability, reasoning, or enterprise deployment.

Some quick AI generated research:

Since the introduction of the Transformer architecture in 2017, Large Language Models (LLMs) utilizing self-attention mechanisms have dominated the artificial intelligence landscape. However, as scaling LLMs faces physical, data, and compute limitations, researchers are actively looking to alternative paradigms to surpass them.

Prominent alternative frameworks include:

• State-Space Models (SSMs): Architectures like Mamba and Google Titans that offer linear-time complexity and highly efficient long-context processing.

• World Models / Joint Embedding Predictive Architectures (JEPA): Models that predict abstract representations in latent space rather than generating raw pixels or tokens, championed by Yann LeCun.

• Neuro-symbolic AI: Systems combining neural networks with symbolic logic to resolve the reasoning and hallucination flaws inherent in statistical LLMs.

• Large Concept Models (LCMs): Architectures designed to process sentence-level concepts and hierarchical structures rather than next-token sequential estimation.