A core debate in AI is whether LLMs, which are text prediction engines, can achieve true intelligence. Critics argue they cannot because they lack a model of the real world. This prevents them from making meaningful, context-aware predictions about future events—a limitation that more data alone may not solve.

Solving key AI weaknesses like continual learning or robust reasoning isn't just a matter of bigger models or more data. Shane Legg argues it requires fundamental algorithmic and architectural changes, such as building new processes for integrating information over time, akin to an episodic memory.

Judea Pearl, a foundational figure in AI, argues that Large Language Models (LLMs) are not on a path to Artificial General Intelligence (AGI). He states they merely summarize human-generated world models rather than discovering causality from raw data. He believes scaling up current methods will not overcome this fundamental mathematical limitation.

Today's AI models are powerful but lack a true sense of causality, leading to illogical errors. Unconventional AI's Naveen Rao hypothesizes that building AI on substrates with inherent time and dynamics—mimicking the physical world—is the key to developing this missing causal understanding.

Simply making LLMs larger will not lead to AGI. True advancement requires solving two distinct problems: 1) Plasticity, the ability to continually learn without "catastrophic forgetting," and 2) moving from correlation-based pattern matching to building causal models of the world.

AI and formal methods have been separate fields with opposing traits: AI is flexible but untrustworthy, while formal methods offer guarantees but are rigid. The next frontier is combining them into neurosymbolic systems, creating a "peanut butter and chocolate" moment that captures the best of both worlds.

While both humans and LLMs perform Bayesian updating, humans possess a critical additional capability: causal simulation. When a pen is thrown, a human simulates its trajectory to dodge it—a causal intervention. LLMs are stuck at the level of correlation and cannot perform these essential simulations.

While a world model can generate a physically plausible arch, it doesn't understand the underlying physics of force distribution. This gap between pattern matching and causal reasoning is a fundamental split between AI and human intelligence, making current models unsuitable for mission-critical applications like architecture.

Instead of just expanding context windows, the next architectural shift is toward models that learn to manage their own context. Inspired by Recursive Language Models (RLMs), these agents will actively retrieve, transform, and store information in a persistent state, enabling more effective long-horizon reasoning.