Google DeepMind Paper Argues LLMs Will Never Be Conscious

There's Something Fundamentally Wrong With LLMs

LLMs aren't trained on the "vast majority of speech," experts warn, a major blind spot that could have sweeping consequences.

The LLM Critics Are Right. I Use LLMs Anyway.

I almost agree with all of the LLM critics, yet I still use LLMs a lot. I know this sounds like I am delusional, but I don't think I am alone with it.

The LLM Critics Are Right. I Use LLMs Anyway.

I almost agree with all of the LLM critics, yet I still use LLMs a lot. I know this sounds like I am delusional, but I don't think I am alone with it.

Dan Shipper 📧 on Twitter / X

this is true and is a big reason why you don’t need to be a highly technical researcher to use LLMs in surprising and novel ways https://t.co/TuxNzXzToU— Dan Shipper 📧 (@danshipper) July 27, 2025

My AI Skeptic Friends Are All Nuts

My smartest friends have bananas arguments about LLM coding.

My AI Skeptic Friends Are All Nuts

My smartest friends have bananas arguments about LLM coding.

What Happens, Exactly, When a Person Talks to an LLM?

A phenomenology of thinking with a model.

I’m Tired of Talking About AI

I don’t want to talk about LLMs anymore.

LLMs and self-referentiality

I woke up yesterday with the following thoughts, which are probably either obvious or dumb. A central thesis that many readers, including me, took from Douglas Hofstadter’s Gödel Escher Bach when y…

Why Are LLMs Smart?

A popular way to explain how current LLMs work is to say that “all” they do is predict the next most likely word in a sentence.

Rotating the Space: On LLMs as a Medium for Thought

Essays and writing on AI

Defeating Nondeterminism in LLM Inference

Reproducibility is a bedrock of scientific progress. However, it’s remarkably difficult to get reproducible results out of large language models. For example, you might observe that asking ChatGPT the same question multiple times provides different results. This by itself is not surprising, since getting a result from a language model involves “sampling”, a process that converts the language model’s output into a probability distribution and probabilistically selects a token. What might be more surprising is that even when we adjust the temperature down to 0This means that the LLM always chooses the highest probability token, which is called greedy sampling. (thus making the sampling theoretically deterministic), LLM APIs are still not deterministic in practice (see past discussions here, here, or here). Even when running inference on your own hardware with an OSS inference library like vLLM or SGLang, sampling still isn’t deterministic (see here or here).

How LLMs are and are not like the brain

Hi from buttondown! At the bottom of this newsletter is a bit of administrivia about the new platform How LLMs are and are not like the brain Beneath all the...