(long-term) Will existing LLMs have ~0 risk of catastrophic instrumental convergence if scaled up 1000x more compute?

Will the best LLM in 2026 have <500 billion parameters?

Will the best LLM in 2027 have <250 billion parameters?

Will the best LLM in 2027 have <500 billion parameters?

[Carlini questions] Will we still use (slight modifications of) transformer-based LLMs we currently use

More than 80% of all user queries to LLMs will be served by LLMs less than 10 Billion parameters in size by 2050?

Which High-risk threshold as defined by OpenAI will be reached first by an LLM, whether or not that LLM is released?

Will LLMs be worse than human level at forecasting when they are superhuman at most things?

Will LLM inference for the largest models run on analogue circuitry as the primary element of computuation by end 2028?

Will second-order optimizers displace first-order optimizers for training LLMs by 2030?