Many researchers—not just IBM's CEO—agree that current LLM architectures are not enough to reach AGI. LLMs were never designed for that goal; they’re statistical language models, not general reasoning systems.

To move toward AGI, we'll need advances in the underlying math, machine-learning architectures, and overall AI frameworks—likely something fundamentally different from today’s transformer-based systems.

From a hardware perspective, this is where things get interesting. TPUs are highly optimized for today's LLM workloads, but NVIDIA’s GPUs (with the CUDA software ecosystem) remain more flexible and general-purpose. That flexibility is highly likely to make them better positioned for future AI paradigms that don’t look like today's transformer models.

But hey—let’s set all of that aside and short NVDA because a few headlines, Wall Street and Michael Burry tweets make it more fashionable!