this post was submitted on 11 Aug 2026
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We have math PHDs with proofs current LLMs can't become AGI, because they'll always have a context issue
I would love it if you'd point me at these mathematical proofs.
This is the primary paper I reference.
https://arxiv.org/pdf/2507.07505
Vishal Sikka, advisory board member of BMW. Recommended to Stanford by Marvin Minsky, one of his professors were John McCarty. And I must stand corrected, he has a PHD of computer sciences, not Math as I remembered it as.
Varin Sikka is his son, co author of the paper and based on Stanford's site an undergraduate. https://profiles.stanford.edu/363374
Vishal has an AI based company himself, so there might be some personal reasons for why he'd advocate for using what AIs capable of rather than chasing an impossible (from his perspective) to hit milestone
That paper doesn't seem to rule out AGI, only an single LLM model that can answer every arbitrarily difficult question on demand.
AGI does not necessarily mean one model acting alone, or being able to answer any question on demand. Humans are the same way: we often need time or collaboration to arrive at conclusions, but that doesn't mean we don't have "general intelligence".
You're not going to context hack complexity, every time you summarise something and pass it onto the next agent. You're losing complexity.
We're going to get some massive models with incredible context, but AGI requires models to never hallucinate. Even when the complexity requires more context than it has available, which is not feasible