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.

The plateauing performance-per-watt of GPUs suggests that simply scaling current matrix multiplication-heavy architectures is unsustainable. This hardware limitation may necessitate research into new computational primitives and neural network designs built for large-scale distributed systems, not single devices.

Intense market competition forces major AI labs to focus on scaling proven, profitable Transformer models for short-term gains. This creates a strategic blind spot, leaving a crucial gap for startups like Core Automation to explore fundamentally new, non-Transformer architectures that could redefine the field.

As AI models scale, their optimal architecture changes. Smaller models benefit from architectural "biases" like gating for efficiency. However, at massive scale (trillions of parameters), unstructured architectures like Transformers, which rely on simple matrix multiplication, become superior because they scale with fewer constraints.

According to scaling laws, increasing model size offers minimal improvement to data efficiency. Even an infinitely large model would only reduce data needs by about 10x, a trivial amount compared to the thousands-to-millions-fold efficiency gap between AIs and humans. This suggests current architectures are on the wrong scaling curve for true intelligence.

The core weakness of Transformers is their static nature. They are trained in a lab on a snapshot of data and then deployed. They cannot adapt to new events, tools, or user tasks without a full retraining cycle, making true continuous learning at test time impossible with the current architecture.

Chain-of-thought is a clever but inefficient "band-aid" for the Transformer's shallow architecture. It simulates deeper computation by generating more tokens, which is slow and expensive at inference. According to Core Automation's Rohan Anil, a superior architecture would have greater computational depth built-in.

Contrary to the prevailing 'scaling laws' narrative, leaders at Z.AI believe that simply adding more data and compute to current Transformer architectures yields diminishing returns. They operate under the conviction that a fundamental performance 'wall' exists, necessitating research into new architectures for the next leap in capability.

The most fundamental challenge in AI today is not scale or architecture, but the fact that models generalize dramatically worse than humans. Solving this sample efficiency and robustness problem is the true key to unlocking the next level of AI capabilities and real-world impact.