Collection
AI augmentation vs automation
Updated 7h
Collection
Updated 7h
Humans are Missing from AI Coding Agent Research
Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows. As these systems make strides, however, the primary bottleneck to practical usefulness increasingly shifts away from pure task-solving capability, and toward challenges in how users communicate with, supervise, and trust agents. In this position paper, we argue for a reorientation from autonomous to human-centered coding agents: systems designed not only to complete tasks, but to collaborate effectively with people. We identify four core interaction-level dimensions that characterize the human-agent task-solving loop: task alignment, verifiability, steerability, and adaptability. Finally, we outline concrete research directions to advance these dimensions, including user-involved coding environments, comprehensive verification mechanisms, and principled measures of human-agent interaction quality.
Experimental Evidence on the Learning Impact of Generative AI
We study whether access to off-the-shelf generative AI changes how much students learn in a timed assignment. In a proctored, in-person randomized experiment, undergraduates study an unfamiliar topic and write an essay with or without AI, then complete unaided assessments immediately and one week later. AI access raises test scores, and a smaller gain persists a week later. Gains remain for students who use AI as a tutor ("augmentation") and fade for students who have AI write for them ("automation"). We document two mechanisms: students shift time from writing toward reading and searching for information, and report greater learning enjoyment.
Arvind Narayanan on Twitter / X
This week I had the honor of speaking to Princeton’s entire incoming undergraduate class to address their AI anxieties. I had three messages for them — good news, bad news, and a note of optimism. Here’s a condensed version.The good newsWe have enough evidence now to conclude…— Arvind Narayanan (@random_walker) August 27, 2026
Arvind Narayanan on Twitter / X
To understand and empathize with how workers in many or most fields outside software experience advances in AI capabilities, I propose a little thought experiment. https://t.co/QZG24aRiBe— Arvind Narayanan (@random_walker) July 28, 2026
Large language models are not the problem
If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes down to the fear that a machine could do what we do? Perhaps it says we should be doing something better.
AI Is Not Conscious, But It Is Our Unconscious
The Convivial Society: Vol. 7, No. 4
AI Is Not Conscious, But It Is Our Unconscious
The Convivial Society: Vol. 7, No. 4
Technology is a Siren Song
Orangutan thinking This started with a question I asked myself: How do our minds change when we can receive a plausible, informed, and convincing answe...
Why A.I. Isn’t Going to Make Art
To create a novel or a painting, an artist makes choices that are fundamentally alien to artificial intelligence.
Augmented human intelligence goes beyond the AGI mirage
Discover Artificial Intelligence - This paper argues that contemporary Artificial General Intelligence (AGI) approaches face significant challenges that make Augmented Human Intelligence (AHI) a...
AI tools are 'deskilling' workers, philosophy professor says
A philosophy professor warns that AI reliance is weakening workers' judgment, creativity, and problem-solving.
Human Intelligence, the Secret of Artificial Intelligence
Artificial intelligence is mysterious: we speak to it and it seems to understand what we say. Proof that it understands is that it responds with text or speech that makes sense, and sometimes more …
Thick practices for AI tools
This essay is the result of thoughts developed during the PIBBSS fellowship this summer. Thanks to Dusan and Maris for feedback on a draft of this essay. Thanks to Sahil and Niki for discussions that influenced the ideas of this essay. 1. Intro What if we could build AI tools...
Reality Roundtable #20 — Hacking Human Attachment: The Loneliness Crisis, Cognitive Atrophy and other Personal Dangers of AI - The Great Simplification
Mainstream conversations about artificial intelligence tend to center around the technology’s economic and large-scale impacts. Yet it’s at the individual level where we’re seeing AI’s most potent effects, and they may not be what you think. Even in the limited time that AI chatbots have been publicly available (like Claude, ChatGPT, Perplexity, etc.), studies show that our increasing reliance on them wears down our ability to think and communicate effectively, and even erodes our capacity to nurture healthy attachments to others. In essence, AI is atrophying the skills that sit at the core of what it means to be human. Can we as a society pause to consider the risks this technology poses to our well-being, or will we keep barreling forward with its development until it’s too late?
