Scaling AI...Bigger isn't always better

About this episode

Is the era of "bigger is better" coming to an end? For the last five years, the entire Artificial Intelligence industry has relied on a single strategy: Scaling Laws. The assumption was that adding more data, more GPUs, and more compute would inevitably lead to Artificial General Intelligence (AGI).

But new data suggests this curve is flattening.

In this episode of ARQIV, we investigate the forensic evidence that the "Scaling Era" is hitting a hard wall—physically, financially, and cognitively. We dig into the energy crisis threatening the grid, the Nvidia H100 supply bottlenecks, and the diminishing returns of trillion-parameter models.

More importantly, we reveal what comes next. If scaling is failing, what replaces it? We break down the 5 Alternative Paths researchers at DeepMind, Meta FAIR, and Anthropic are quietly exploring:

1. Multimodality & Agents: Why tool use (ReAct) is replacing raw parameter count.

2. Memory-Centric Architectures: Solving the "Goldfish Problem" and LLM forgetfulness.

3. World Models: The shift from predicting the next word to predicting the next state (JEPA).

4. Diffusion & Post-Transformer Architectures: Why the tech behind Sora and Stable Diffusion might beat the Transformer.

5. Distributed Intelligence: The geopolitical shift away from centralized "Oracle" AI.

Join us as we analyze the economics of $100 million training runs, the arguments of Yann LeCun, and why the future of AI won’t be bigger—it will be different.