Announcing Diffuse AI
And a Call for Contributors
AI is eating the US economy. It’s a bubble. It’s normal technology. It’s going to be the biggest thing since the internet, or electricity, or fire. It’s slop. It’s God. It’s plateauing. It’s going to replace us all.
Everyone wants to know what the next few years of AI will look like. We have a different question: what is AI capable of right now?
Whether you’re worried about unemployment, deskilling, or human extinction – and even if you think AI is a flash in the pan – we all have a shared interest in understanding how it is already changing our world. But static benchmarks — the multiple choice questions or verifiable math and coding challenges typically used to evaluate models — are getting saturated as quickly as we can build them. The real test is how well AIs function in complex, context-heavy, dynamic environments — in other words, the real world.
What do we know so far? We’re starting to see serious interest in how AI is playing out across the economy, but the picture is messy. We’ve all seen the buzzy paper from Stanford arguing that AI is currently causing jobs to plummet among junior workers in “highly exposed industries” and the equally buzzy paper from Yale claiming there’s no discernable effect. Labs are investing in more sophisticated measures of how their models perform on the kinds of tasks that matter for real jobs, but there’s a big difference between a self-contained evaluation and the messy reality of day-to-day employment. And for every report on the wonders of vibe-coding, there’s a thread on hacker news insisting that AI productivity gains are a mirage. For every $30 million AI for science startup, there’s a grizzled computational biologist who’s ready to deflate the hype. What’s really going on? What are these systems actually capable of? What are the bottlenecks to realizing AI-assisted productivity gains? What does this all look like on the ground?
Diffuse AI is going to help answer all these questions, in as much nitty gritty qualitative detail as possible. We want:
In-depth case studies of how AI is playing out in your industry — the more specific, the better.
Interviews with experts about what current models are and aren’t useful for in their work.
Stories of similar instances of historical tech diffusion.
Thoughtful discussions of the strengths, weaknesses, and methodologies of economic impact benchmarks like GDPeval or the Anthropic Economic Index.
We don’t want:
Predicting the future.
Theorizing from first principles.
Coming with an axe to grind.
Here are some examples of pieces we’d love to have published:
An interview with the AI Initiatives team at The New York Times
How Deepseek is actually diffusing in China: Shallow, Narrow, and Slow
A case study on the first Compute Arms Race: weather forecasting supercomputer
Pitch us through this form, we pay $1k USD for essays and reportage. Help us figure out what’s really going on.