Related posts

Professor at Wharton studying how people actually use AI, and author of Co-Intelligence. Writes One Useful Thing, where the claims are dated and testable because the thing they describe keeps changing under them.

• Our readingkorrents.com

  Competing with AI in producing outputs is a losing game; human advantages should be used to work with AI on things neither could do alone.

  Their words

  You are not trying to compete with AI in producing outputs, that is a losing game. Instead, you want to use your human advantages as basis of working with AI to do things that neither of you could do alone.

  The Overhangoneusefulthing.org

Co-founder of AngelList. Writes and podcasts at nav.al in short, aphoristic pieces on startups, wealth and judgement rather than long essays.

• Our readingkorrents.com

  Frontier AI labs improve via a flywheel of getting the smartest people on leading models, then distilling their inputs, model outputs, and synthetic data, with users locked in by mutual competition.

  Their words

  The Frontier Lab flywheel is to get the smartest people to use the leading models. Then distill their inputs, your own model’s outputs, and resulting synthetic data and environments. And the people have to use the leading models because they’re locked in mutual competition.

  @naval on Xx.com

Technical staff at METR, where she works on threat modelling and risk assessment for loss-of-control risks from advanced AI.

• Our readingkorrents.com

  Monitors that read an agent's chain of thought must be kept out of the reward signal, or you are simply training the agent to obfuscate its thinking.

  Their words

  But keep those methods you use to investigate things and monitor things very separate from the methods you use to generate reward which is something that AI companies including open AAI have held up as a principle especially in the case of avoiding putting training pressure on the chain of thought. So you might have monitors that read the agents chain of thought in order to alert you if something is going wrong somewhere but you don't train the agents with the outputs of that monitor.

  youtube.com

Professor at Wharton studying how people actually use AI, and author of Co-Intelligence. Writes One Useful Thing, where the claims are dated and testable because the thing they describe keeps changing under them.

• Our readingkorrents.com

  AI-generated writing tends to repeat the same themes, names, and underlying ideas across different outputs.

  Their words

  AIs don’t just repeat the same sentence patterns but also the same themes (memory is a favorite), names (Elara Voss, Marcus Chen), and underlying ideas.

  Agency and Agentsoneusefulthing.org

Political scientist writing Drezner's World on international relations, American politics and the marketplace of ideas; author of The Ideas Industry.

• Our readingkorrents.com

  AI output quality is mixed at best without strong underlying critical thinking skills.

  Their words

  Without a well-developed set of critical thinking skills, AI outputs will yield variegated results at best

  The Professor and the Plutocrat and AIdanieldrezner.substack.com

Co-founder of Ethereum. Publishes long essays on mechanism design, governance and what cryptography is for, and returns to earlier positions to say which parts they no longer hold.

• Our readingkorrents.com

  A sufficiently large random reversible circuit is plausibly a secure cryptographic permutation, while a large random irreversible circuit collapses to only a few possible outputs.

  Their words

  A big-enough random reversible circuit is plausibly a secure cryptographic permutation, a big-enough random irreversible circuit degenerates into having only a few possible outputs.

  Obfuscation (Part III): Local Mixingvitalik.eth.limo

Writer and teacher on productivity and personal knowledge management. Author of Building a Second Brain and founder of Forte Labs, which runs the course of the same name.

• Our readingkorrents.com

  AI tends to flatten outputs toward sameness, so personal taste and perspective become the main way to stand out.

  Their words

  AI flattens everything it touches. The way you stand out is by getting clearer about what you really want, your taste, your perspective, your opinions.

  Is Recall the Second Brain for the AI Era?fortelabs.com

Mathematician at UCLA, working mainly in harmonic analysis and partial differential equations. Writes What's new, a long-running blog on their research, open problems and expository notes.

• Our readingkorrents.com

  Natural-language LLM mathematical reasoners are fundamentally limited by lacking machine-checkable semantics, which blocks autonomous verification needed for open-ended research.

  LLMs

  Their words

  However, these LLM reasoners that generate informal reasoning in natural language are fundamentally limited by the lack of precise, machine-checkable semantics, making their outputs prone to hallucinations [Huang et al., 2025b] and precluding autonomous verification, a prerequisite for tackling open-ended mathematical research.

  arxiv.org

AI researcher at OpenAI; built the poker systems Libratus and Pluribus and the Diplomacy agent Cicero, and works on reasoning models.

• Our readingkorrents.com

  On everyday high-stakes questions, a model's answer can now be trusted more than an expert human's.

  Their words

  And I think they're at a point now where they've actually been at a point for a while now where I feel like I can just trust the outputs arguably more than I could trust the output from from a human,

  youtube.com

Writes Strange Loop Canon, on the intersection of technology, economics and business — "building models of the world to understand these problem areas and to define them better".

• Our readingkorrents.com

  Combining multiple AI models' outputs into one final answer tends to discard most of the good ideas that only one model produced.

  Their words

  the council does not simply keep the best bits from everyone. It keeps a minority of the good ideas, while peer review seems to give consensus ideas an extra push.

  LLM councils show groupthinkstrangeloopcanon.com

Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

• Our readingkorrents.com

  As AI agents take on bigger delegated tasks, the human bottleneck shifts to writing specs and reviewing output fast, hollowing out the middle of the work.

  Their words

  The bottleneck has shifted from doing the work to writing clear specs and reviewing outputs fast enough to keep the pipeline moving-the middle is hollowing out.

  How to Work and Compound with AIeugeneyan.com

Co-founder of Ethereum. Publishes long essays on mechanism design, governance and what cryptography is for, and returns to earlier positions to say which parts they no longer hold.

• Dislikedrcmnd.app

  Local Deep Research

  Their words

  Its responses are, in my view, pretty bland and not very high-quality. I did a side-by-side test of asking Local Deep Research a question, then asking pi the same question (telling it to use searxng to make as many internet searches as needed), and I fed both outputs into an LLM to ask which is better.

  vitalik.eth.limo

Writer and academic, author of The Cult of Smart and How Elites Ate the Social Justice Movement; writes a daily newsletter on politics, education and culture.

• Our readingkorrents.com

  Schools account for a tenth or less of the variation in how well students do.

  Their words

  That makes perfect sense when you consider that schools control 10% or less of the variance in student academic outputs.

  There Are No Miracles in Educationfreddiedeboer.substack.com

Creator of Flask and Jinja. Writes about software at lucumr.pocoo.org.

• Likedrcmnd.app

  Claude Sonnet

  Their words

  I exclusively use the cheaper Sonnet model. It’s perfectly adequate for my needs, and in fact, I prefer its outputs over the more expensive Opus model.

  lucumr.pocoo.org

Writes Where's Your Ed At and hosts Better Offline; runs a tech PR firm, and argues at length that the AI business does not add up.

• Our readingkorrents.com

  The modern executive is symbolic: what gets rewarded is the appearance of doing work rather than any output anyone can measure.

  Their words

  We live in the era of the symbolic executive, when "being good at stuff" matters far less than the appearance of doing stuff, where "what's useful" is dictated not by outputs or metrics that one can measure but rather the vibes passed between managers and executives that have worked their entire careers to escape the world of work.

  The Era Of The Business Idiotwheresyoured.at