- Skepticism was voiced about the economics of simply scaling transformer models indefinitely, with one participant noting that 100 k NVIDIA H100 GPUs may not even qualify as a “big” datacenter anymore.  
- A counter-argument emphasized that algorithmic progress (new architectures, training tricks, etc.) is happening in parallel to brute-force scaling, suggesting that pure compute increases are not the only path forward.  
- Reproducibility concerns were raised: although many papers appear, few groups can afford to replicate large-scale experiments due to prohibitive training costs, leading to a gap between published claims and independently verified results.  
- Participants joked that the “secret to AGI” might turn out to be trivial or absurd (e.g., “macaroni glued to paper”), highlighting uncertainty about whether current scaling trends will actually yield artificial general intelligence.  
- A link to Govista Space’s work on structural batteries (https://www.govista.space/) was shared, pointing to integrated energy-storage materials as a potentially transformative hardware technology for future devices and vehicles.