Related posts

Professor of economics at George Mason University and Bartley J. Madden Chair at the Mercatus Center. Co-writes the blog Marginal Revolution and co-authors the textbook Modern Principles of Economics with Tyler Cowen.

• AI, Redistribution, and the Size of the Pie - updated with @akorinek et al. estimates. Still looks like bigger pie can overcome smaller slice with comfort.

  marginalrevolution.com

  Related

Computer science professor who works on fast data processing; co-author of the simdjson parser and a weekly blogger about software performance since 2004.

• The Economist estimates that AI has created about 1 million US jobs so far, against roughly 200,000 AI-attributed layoffs since mid-2023. The new roles pay more. Data-center installation and maintenance ads run about 40% above comparable work. Workers with AI skills earn a wage premium of over 60% (PwC). Companies most exposed to AI grew their headcount faster than the least exposed. Aging societies are running out of workers. Median age is already over 40 in Canada and much of Europe. Output per worker has to rise or living standards fall. We need a technology that makes each remaining worke…

  EuropeeconomistsRelated

Demographer; writes on fertility, marriage and population, and directs the Pro-Family Initiative at the Institute for Family Studies.

• Should You Trust the “Total Maternity Rate”?

  It does not estimate what many people seem to think it estimates

  Related

Economist at George Mason University. Writes Overcoming Bias on prediction markets, signalling and the far future, and wrote The Age of Em and The Elephant in the Brain.

• Status And Social-Skill Bias

  The following table gives medians of 3 LLM estimates re % relative importance for short term success in 5 areas of life of these 4 factors:

  LLMsRelated

Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

• When evaling models, we anchor on the median task. But this is like how devs estimate the median task accurately but underestimate the mean which tends to be ~2x estimated (thus "double your estimates"). This applies to models too—we optimize for time/cost on median tasks. But it's the tail tasks that determine a project's completion, and reliable models (like Fable and Opus) are the diff between success and failure. Work with models as collaborators, not tools. Go beyond pair-programming, where given a narrow spec, the tool just executes in minutes. Instead, explain your intent and what succ…
  Quoting@Steve_YeggeFable is careful. None of the other models are careful. GPT-5.6 Sol, Opus, Kimi, Grok. You can compare them all day long on capabilities, and it doesn't matter, because in order to use a model for real production work, it must first and foremost be careful. That's the only dimension that matters to…

  Related

Italian software architect working on Microsoft 365, Azure and GitHub tooling. Microsoft MVP and GitHub Star; writes, speaks and streams about developer productivity at emanuelebartolesi.com.

• vscode-copilot-footprint — Copilot Footprint is a VS Code extension that estimates the environmental footprint (energy, CO₂e, water, tree equivalent, and real-world equivalents) of GitHub Copilot tok…

  Commitsfeat: Implement Usage Service, Status Bar, and Dashboard for Copilot Footprint · feat: update README and CHANGELOG, add new prompts for changelog and frontend design · feat: initialize vscode-copilot-footprint extension

  energyRelated

Mathematician at UCLA, working mainly in harmonic analysis and partial differential equations. Writes What's new, a long-running blog on their research, open problems and expository notes.

• On the number of exceptional intervals to the prime number theorem in short intervals

  For a fixed exponent $0 < θ\leq 1$, it is expected that we have the prime number theorem in short intervals $\sum_{x \leq n < x+x^θ} Λ(n) \sim x^θ$ as $x \to \infty$. From the recent zero density estimates of Guth and M…

  Related

Emeritus Professor of Statistics at the University of Cambridge, former president of the Royal Statistical Society, and author of The Art of Statistics.

• Communicating personalized risks from COVID-19: guidelines from an empirical study

  As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups…

  Related