While more data and compute yield linear improvements, true step-function advances in AI come from unpredictable algorithmic breakthroughs like Transformers. These creative ideas are the most difficult to innovate on and represent the highest-leverage, yet riskiest, area for investment and research focus.

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.

While desirable for adaptability, creating models that learn continuously risks a winner-take-all dynamic where one company's model becomes uncatchably superior. This also represents a risky 'depth-first search' toward AGI, prematurely committing to the current transformer paradigm without exploring safer alternatives.

The "Attention is All You Need" paper's key breakthrough was an architecture designed for massive scalability across GPUs. This focus on efficiency, anticipating the industry's shift to larger models, was more crucial to its dominance than the attention mechanism itself.

Despite concerns about the limits of Large Language Models, Microsoft AI's CEO is confident the current transformer architecture is sufficient for achieving superintelligence. Future leaps will come from new methods built on top of LLMs—like advanced reasoning, memory, and recurrency—rather than a fundamental architectural shift.

OpenAI is designing its custom chip for flexibility, not just raw performance on current models. The team learned that major 100x efficiency gains come from evolving algorithms (e.g., dense to sparse transformers), so the hardware must be adaptable to these future architectural changes.

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 2017 introduction of "transformers" revolutionized AI. Instead of being trained on the specific meaning of each word, models began learning the contextual relationships between words. This allowed AI to predict the next word in a sequence without needing a formal dictionary, leading to more generalist capabilities.

Today's transformers are optimized for matrix multiplication (MatMul) on GPUs. However, as compute scales to distributed clusters, MatMul may not be the most efficient primitive. Future AI architectures could be drastically different, built on new primitives better suited for large-scale, distributed hardware.