I know a few people for whom LLMs have been a near-immediate multiplier of attention and effort. I know a lot for whom LLMs clearly make them worse at thinking and doing things. So: why?
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.
Why do LLMs make stuff up? New research peers under the hood.
Claude's faulty "known entity" neurons sometimes override its "don't answer" circuitry.
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...
The scientific case for being nice to your chatbot
New research confirms that LLMs often perform better when you encourage them. But why?
LLMs and performative productivity
It's worth asking whether LLMs are actually making us more productive at all—and if so, what we might be sacrificing in return.
Illusions of Understanding from Outsourcing Thinking to LLMs
Some illusions of understanding are an inevitable part of the research process, while others can be avoided or overcome by careful critical thinking and observation. We are facing an increased risk of avoidable illusions as more research activities are delegated to large language models (LMM). LLMs can be useful but they cannot think, and their use can undermine our thinking and understanding. Thinking for ourselves is hard and error prone but worthwhile - and there are no shortcuts to understanding.
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.
Why Can’t Powerful LLMs Learn Multiplication?
These days, large language models (LLMs) can handle increasingly complex tasks, writing complex code and engaging in sophisticated reasoning. But when it comes to 4-digit multiplication, a task taught in elementary school, even state-of-the-art systems fail. Why? A new paper by Computer Science PhD student Xiaoyan Bai and Faculty Co-Director of the Data Science Institute’s …
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
Andrew Ho on Twitter / X
Not really observations that others before me haven’t made, but:- Despite the seemingly magical nature of LLMs, reflection over a >3 month timescale suggests my total productivity hasn’t increased by over 100%, or perhaps even by over 50%, and a lot of time is actually wasted…— Andrew Ho (@andrewho03) September 3, 2026
What Happens, Exactly, When a Person Talks to an LLM?
A phenomenology of thinking with a model.
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…
Solving a Million-Step LLM Task with Zero Errors
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans,...
Well-intentioned obscenity
An LLM lied about me at the office and I'm cranky about it. That interaction makes me think a lot about how LLMs are becoming normalized, though, and what we're looking at in terms of their role in human-to-human interactions in the future.
