Steering AI Beyond Automation
By Gabriel Unger and Arvind Narayanan. Unger is an economist at Stanford’s Digital Economy Lab.
12 stories
By Gabriel Unger and Arvind Narayanan. Unger is an economist at Stanford’s Digital Economy Lab.
A middle ground between the cybersecurity and AI safety communities
The goal of leading AI labs is recursive self-improvement (RSI): the automation of AI research using AI agents. RSI also underpins forecasts of explosive AI progress. How can we assess if we are close to this milestone?
I had the honor of giving a keynote at the International Conference on Machine Learning in Seoul last week titled “What will be left for us to work on?” I addressed the widespread anxiety about how we should adapt as AI capabilities increase.
Our goal in this essay is to move beyond the debate over whether AI is a bubble. We do so in two ways: clearly separating current financials from the question of who captures value in the long run, and recognizing that the labs are not confined to be model providers.
There is great anxiety and uncertainty about AI replacing jobs. How can we move past vague warnings and bombastic predictions and bring data to bear on this question?
By Stephan Rabanser, Sayash Kapoor, Rishi Bommasani, Andrew Schwartz, Arvind Narayanan
Introducing CRUX, a new project for evaluating AI on long, messy tasks
Suppose you hear about a new AI agent for improving productivity — by making purchases, or writing code, or sending emails, or handling a customer on your behalf. Should you trust it? Can the agent do the job reliably enough? After all, there are many horror stories of agents going wrong.
When we published AI as Normal Technology, the impact caught us off guard. It quickly became the most influential thing either of us had ever done.1 We took this as a strong signal to spend more of our time thinking and writing about the medium-term future of AI and its impacts, offering grounded…
There is no capability threshold that will lead to sudden impacts
This post is over 15,000 words long—it is a new paper on our vision for the future of AI. We are pleased to announce that an expanded version of these ideas will become our next book together.
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