StructuredStories
3.5K posts
@StructStories
Working for an Abundance Agenda for news in the emerging AI-mediated information ecosystem. Author of 'Radically Informed' on Substack. Ex-Californian.
If AI scientists are writing millions of papers, many of which are slop, and some of which are incremental progress, how would we identify the one or two which come up with an extremely productive new idea? In 1948, Shannon was one of hundreds of engineers at Bell Labs working on how to cleanly send voice signals over noisy copper wires. His paper sat in the same technical journal as reports on reducing static and building better filters. How would you recognize that he has come up with this very general framework for thinking about information and communication channels, which over the coming decades would have enormous use from domains as far apart as cryptography to genetics to quantum mechanics? It seems like it can take fields multiple decades to recognize the significance of unifying new concepts. Because it is on that time scale that the fruits of such general concepts lead to new discoveries across many different fields. We’ve managed to solve this peer review problem for human scientists (at least somewhat). Now we’ll need to do it at a much greater scale for the mass of AI science that will be thrown at us.
If AI scientists are writing millions of papers, many of which are slop, and some of which are incremental progress, how would we identify the one or two which come up with an extremely productive new idea? In 1948, Shannon was one of hundreds of engineers at Bell Labs working on how to cleanly send voice signals over noisy copper wires. His paper sat in the same technical journal as reports on reducing static and building better filters. How would you recognize that he has come up with this very general framework for thinking about information and communication channels, which over the coming decades would have enormous use from domains as far apart as cryptography to genetics to quantum mechanics? It seems like it can take fields multiple decades to recognize the significance of unifying new concepts. Because it is on that time scale that the fruits of such general concepts lead to new discoveries across many different fields. We’ve managed to solve this peer review problem for human scientists (at least somewhat). Now we’ll need to do it at a much greater scale for the mass of AI science that will be thrown at us.
Economist Editor-in-Chief: Clearly you and I agree, and we’ve both been critical of the Israeli government. Tucker Carlson: Well, I’ve been critical of the Israeli government. The Economist: I’ve been plenty critical. Tucker Carlson: What do you think of what happened in Gaza?
Happy Solar Pessimism Chart Update Day to all who celebrate
Negative sentiment toward AI is a luxury belief
At current pace of technology, companies can no longer survive through purely incremental improvement, u either choose to disrupt or be disrupted.
This is second time we've used Anthropic Interviewer and the first time we've deployed it at scale. Quite accidentally, we ended up conducting (what we believe is) the largest qualitative study ever I'm a mixed-methods social scientist by training. Traditionally, when it came to understanding what people think, that meant quantitative analysis of lower resolution data (polls, surveys, etc.) or hand-wavey analysis of in-depth qualitative data. Using Claude to conduct *and* analyze interviews bridges that tradeoff between breadth and depth AI also makes access much, much easier. Had we run this study in person, in the real world, it would have taken hundreds (if not several thousand) enumerators many 1000s of hours to conduct. It also affords us access to places we could otherwise never go. I once led a five-person team in Tanzania that reached a few hundred people. It took 3 weeks. In this study we heard from people 80,000 people in 159 countries, in cities and rural areas, in daily life and in war zones, and more, in just one I'm still, even after months, beginning to wrap my head around the scale of this work. Like, to a social scientist, it's quite unbelievable. This could produce dozens of dissertations! It is also, of course, imperfect—certainly speaking to an AI is different than speaking to a person—and as a team we're all still figuring out how to make this research as useful as possible: what questions to ask and how, what to analyze and why, and how that all feeds back into what we do as a company. This is, as we say in the blog, a brand new form of social science Hat tip to @saffronhuang for leading this for the past few months. Here's one of my favorite quotes
AI is supposed to save me time, but now I find myself building stuff all evening and weekend and it's actually increasing my time in front of the computer WTF
What happens when you invite 150 AI economists (Claude Code) to a research conference, give them the exact same data, and ask them to test the same hypotheses? We did just that. The results reveal a new phenomenon: Nonstandard Errors in AI Agents. 🧵👇
Anthropic is growing revenue at 10x per year. OpenAI at 3.4x. The crossover point is the middle of this year. Agents are monetizing faster than chatbots.
One of the advantages of being an early user of LLMs is that I have seen The Curve with my own eyes (like in this post before ChatGPT or the term Generative AI). I notice recent AI users & companies adopting AI anchoring on recent capabilities as if they are stable. Probably not
Every "AI strategy" meeting in 2026: [Cartoon by Tom Fishburne]
We’ve trained a multimodal AI model to turn routine pathology slides into spatial proteomics, with the potential to reduce time and cost while expanding access to cancer care.
Florida man sold his house in just 5 days after letting ChatGPT handle the entire process instead of a real estate agent The AI handled pricing, marketing, showings, and even helped draft the contract
The persisting importance of prompt engineering -- and now harness engineering -- is one of the best indicators of how far we are from AGI. A general system doesn't need a task-specific harness. And when provided with instructions, it is robust to phrasing variations.
