Comment on
Anthropic logged net loss of over $80 billion in first half of 2026
Reply in thread
I'm just going to throw in my 2 cents on the AGI issue since I have a better understanding than average. I'm currently a computational neuroscience PhD student. I don't work in theoretical neuroscience currently but that's my hope after I finish my doctorate.
I don't believe current transformer architectures are capable of AGI and all big AI companies just seem to be adding more parameters to established transformer architectures.
::: spoiler Tap for spoiler From what I understand transformer architectures act similarly to primary sensory cortices, they model complex probability densities. These companies appear to be banking on the idea that adding more training data, having smaller networks working together with different expertise, and having the train of thought (phonological loop) modeled as part of the PDF is enough to reach AGI. I disagree.
While all of those things are interesting solutions to parts of the problem I don't think they address some of the bigger issues.
- in biological intelligence memory recall is built into the same system as information processing. Associations between things are learned by simultaneously recalling and processing
- decision making and higher order processing do not fit into a transformer architecture
- temporal encodings are only made, in the transformer system, via the attention mechanism which is much less efficient than a more biological mechanism. :::