# tradeoffs (blogs) — RSS Amplifier Recent posts from the 1 feeds in the RSS Amplifier directory that cover tradeoffs. Page: Feed: --- ## [Correlation Implies Causation](https://tecunningham.github.io/posts/2026-08-08-correlation-implies-causation.html) _2026-08-08 · Tom Cunningham · Tom Cunningham_ Note I wrote this around 2020, when working at Facebook, trying to reconcile practical decision-making with how economists talk about identification. In a sense thes points are all well known. The idea is treated formally in Imbens (2003), Manski’s identification bounds; Oster (2019). I think Mostly Harmless Econometrics discusses the point informally. Still I found this a useful way of explaining… ## [Definitions of Recursive Self-Improvement](https://tecunningham.github.io/posts/2026-06-05-rsi-definitions.html) _2026-06-05 · Tom Cunningham · Tom Cunningham_ This page surveys definitions related to recursive self-improvement. Most of the content was put together by LLM agents, there are validation checks but it’s possible this contains errors. Any corrections or additions would be very welcome, send me an email! Note Inclusion criteria (source of truth) Coined (red diamond) — the publication is the earliest in this reference to use the term in its… ## [An Apple-Picking Model of AI R&D](https://tecunningham.github.io/posts/2026-03-13-apple-picking-ai.html) _2026-04-07 · Tom Cunningham · Tom Cunningham_ Thanks to Nate Rush, Thomas Kwa, Beth Barnes, Eli Lifland, Chris Ong, Basil Halperin, Tom Houlden, Parker Whitfill, Phil Trammell, & Andy Haupt for comments. An apple-picking model of AI R&D. Many people are talking about how AI is autonomously able to contribute to frontier R&D, yet it’s only picking low-hanging fruit: Andrej Karpathy , Terence Tao , Nathan Lambert , Ryan Greenblatt . In this… ## [When You Overtake More Runners than You’re Overtaken by](https://tecunningham.github.io/posts/2023-08-13-runner-percentile.html) _2026-03-07 · Tom Cunningham · Tom Cunningham_ Suppose you overtake 10 times as many runners as overtake you. What can you say about your speed relative to the other runners? For concreteness, suppose you’re looping around the Viveros Coyoacan, everyone is running in the same direction forever, and each person started at a random point. Under reasonable assumptions your relative frequency of passing runners will exaggerate your position in the… ## [Knowledge-Creating LLMs](https://tecunningham.github.io/posts/2026-01-29-knowledge-creating-llms.html) _2026-02-06 · Tom Cunningham · Tom Cunningham_ Thanks to Zoë Hitzig & Parker Whitfill, among others, for helpful comments. It’s useful to make a distinction between two types of LLMs: Knowledge-sharing LLMs. Traditionally LLMs have been trained with human judgment as the ground truth, as a consequence they rarely exhibit superhuman performance. Their economic value mainly comes from sharing existing knowledge, and the natural business model is… ## [LLM verification](https://tecunningham.github.io/posts/2025-12-30-llm-verification.html) _2025-12-30 · Tom Cunningham · Tom Cunningham_ A prediction: people will move towards producing documents that are machine-verified. A document will come with a checklist so you can see that it satisfies certain properties, as verified by LLMs: Claude Gemini GPT Factual claims are accurate ✅ ✅ ✅ Logically consistent ✅ ✅ ✅ Central idea is novel ✅ ✅ ✅ The writing is readable ✅ ✅ ✅ If your blog post starts with this checklist I’ll be more likely… ## [Forecasts of AI & Economic Growth](https://tecunningham.github.io/posts/2025-10-19-forecasts-of-AI-growth.html) _2025-11-06 · Tom Cunningham · Tom Cunningham_ Validation Checks Overall: ⚠️ Warning ✅ \[43/43\] Cited sources exist in posts/ai.bib (programmatic) ✅ \[33/33\] Table rows have required fields (programmatic) ✅ \[33/33\] QMD quotes match posts/ai.bib (programmatic) ✅ \[33/33\] QMD growth values match posts/ai.bib (programmatic) ⚠️ \[41/43\] Abstracts present for all cited sources (programmatic) ❌ \[25/26\] Bib quotes present in local fulltext version… ## [Economics and Transformative AI](https://tecunningham.github.io/posts/2025-09-19-transformative-AI-notes.html) _2025-10-02 · Tom Cunningham · Tom Cunningham_ Thanks to comments from Daniel Björkegren, Andreas Haupt, Philip Trammel, Brent Cohn, Nick Otis, Andrey Fradkin, Jon de Quidt, Joel Becker. This is a long collection of notes about economics & AI, prompted by two excellent workshops I attended in mid-September: the Windfall Trust’s “Economic Scenarios for Transformative AI” and the NBER’s “Workshop on the Economics of Transformative AI” . I had… ## [On Deriving Things](https://tecunningham.github.io/posts/2020-10-02-on-deriving-things.html) _2025-01-30 · Tom Cunningham · Tom Cunningham_ I’ve spent a lot of time trying to prove things. With diagrams and algebra, back and forth between clipboard whiteboard blackboard & keyboard. I can’t talk about what it’s like for a good mathematician but I can talk about it for a hack. I can prove true & interesting things occasionally but only after wallowing in it for a long time, and after a half-dozen proofs of things that I later realize… ## [Snapshot Showdown on Google Play (Sponsored)](https://crawlproof.com/a/lzpCaH8mu1rQ) _2025-01-30 · **Sponsored**_ Available on Google Play for Android devices. ## [Too Much Good News is Bad News](https://tecunningham.github.io/posts/2024-12-26-heavy-tailed-noise.html) _2024-12-26 · Tom Cunningham · Tom Cunningham_ Here are two nice pieces of Bayesian logic observations that help explain everyday intuitions: When an outcome is the sum of two components then your belief about the contribution of the thinner-tailed component will be first increasing then decreasing in the realization of the outcome. When you observe an outlier in some process, which is the sum of multiple components, then: If the components…